PS-MVSNAJ | | | 88.14 9 | 87.61 16 | 89.71 4 | 92.06 68 | 76.72 1 | 95.75 10 | 93.26 58 | 83.86 11 | 89.55 5 | 96.06 11 | 53.55 158 | 97.89 29 | 91.10 5 | 93.31 34 | 94.54 65 |
|
xiu_mvs_v2_base | | | 87.92 13 | 87.38 21 | 89.55 7 | 91.41 87 | 76.43 2 | 95.74 11 | 93.12 66 | 83.53 13 | 89.55 5 | 95.95 12 | 53.45 163 | 97.68 31 | 91.07 6 | 92.62 41 | 94.54 65 |
|
MG-MVS | | | 87.11 22 | 86.27 28 | 89.62 5 | 97.79 1 | 76.27 3 | 94.96 30 | 94.49 23 | 78.74 51 | 83.87 43 | 92.94 83 | 64.34 53 | 96.94 74 | 75.19 100 | 94.09 22 | 95.66 26 |
|
CHOSEN 1792x2688 | | | 84.98 46 | 83.45 55 | 89.57 6 | 89.94 106 | 75.14 4 | 92.07 106 | 92.32 90 | 81.87 25 | 75.68 103 | 88.27 139 | 60.18 83 | 98.60 13 | 80.46 69 | 90.27 70 | 94.96 54 |
|
MVS | | | 84.66 50 | 82.86 65 | 90.06 1 | 90.93 92 | 74.56 5 | 87.91 207 | 95.54 3 | 68.55 200 | 72.35 138 | 94.71 49 | 59.78 84 | 98.90 6 | 81.29 66 | 94.69 17 | 96.74 8 |
|
DELS-MVS | | | 90.05 4 | 90.09 5 | 89.94 2 | 93.14 49 | 73.88 6 | 97.01 2 | 94.40 28 | 88.32 2 | 85.71 24 | 94.91 44 | 74.11 7 | 98.91 5 | 87.26 25 | 95.94 3 | 97.03 5 |
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 |
MCST-MVS | | | 91.08 1 | 91.46 2 | 89.94 2 | 97.66 2 | 73.37 7 | 97.13 1 | 95.58 2 | 89.33 1 | 85.77 23 | 96.26 7 | 72.84 9 | 99.38 1 | 92.64 3 | 95.93 4 | 97.08 4 |
|
LFMVS | | | 84.34 54 | 82.73 69 | 89.18 8 | 94.76 19 | 73.25 8 | 94.99 29 | 91.89 108 | 71.90 149 | 82.16 50 | 93.49 73 | 47.98 209 | 97.05 62 | 82.55 54 | 84.82 104 | 97.25 2 |
|
PAPM | | | 85.89 38 | 85.46 40 | 87.18 29 | 88.20 142 | 72.42 9 | 92.41 96 | 92.77 77 | 82.11 21 | 80.34 62 | 93.07 80 | 68.27 20 | 95.02 125 | 78.39 82 | 93.59 31 | 94.09 83 |
|
canonicalmvs | | | 86.85 27 | 86.25 30 | 88.66 10 | 91.80 75 | 71.92 10 | 93.54 62 | 91.71 115 | 80.26 31 | 87.55 14 | 95.25 31 | 63.59 62 | 96.93 77 | 88.18 18 | 84.34 110 | 97.11 3 |
|
OpenMVS | | 70.45 11 | 78.54 141 | 75.92 152 | 86.41 54 | 85.93 176 | 71.68 11 | 92.74 82 | 92.51 87 | 66.49 218 | 64.56 208 | 91.96 100 | 43.88 234 | 98.10 24 | 54.61 239 | 90.65 66 | 89.44 163 |
|
QAPM | | | 79.95 116 | 77.39 136 | 87.64 19 | 89.63 111 | 71.41 12 | 93.30 67 | 93.70 39 | 65.34 227 | 67.39 194 | 91.75 105 | 47.83 210 | 98.96 4 | 57.71 231 | 89.81 71 | 92.54 126 |
|
3Dnovator | | 73.91 6 | 82.69 78 | 80.82 88 | 88.31 12 | 89.57 112 | 71.26 13 | 92.60 89 | 94.39 29 | 78.84 48 | 67.89 187 | 92.48 93 | 48.42 204 | 98.52 14 | 68.80 152 | 94.40 19 | 95.15 45 |
|
MVSFormer | | | 83.75 64 | 82.88 64 | 86.37 55 | 89.24 120 | 71.18 14 | 89.07 190 | 90.69 147 | 65.80 222 | 87.13 16 | 94.34 59 | 64.99 48 | 92.67 205 | 72.83 113 | 91.80 51 | 95.27 39 |
|
lupinMVS | | | 87.74 15 | 87.77 14 | 87.63 21 | 89.24 120 | 71.18 14 | 96.57 4 | 92.90 74 | 82.70 16 | 87.13 16 | 95.27 29 | 64.99 48 | 95.80 106 | 89.34 11 | 91.80 51 | 95.93 23 |
|
alignmvs | | | 87.28 19 | 86.97 24 | 88.24 13 | 91.30 88 | 71.14 16 | 95.61 15 | 93.56 44 | 79.30 38 | 87.07 18 | 95.25 31 | 68.43 19 | 96.93 77 | 87.87 20 | 84.33 111 | 96.65 9 |
|
CSCG | | | 86.87 26 | 86.26 29 | 88.72 9 | 95.05 18 | 70.79 17 | 93.83 55 | 95.33 4 | 68.48 202 | 77.63 88 | 94.35 58 | 73.04 8 | 98.45 15 | 84.92 40 | 93.71 29 | 96.92 6 |
|
CNVR-MVS | | | 90.32 3 | 90.89 4 | 88.61 11 | 96.76 4 | 70.65 18 | 96.47 6 | 94.83 13 | 84.83 8 | 89.07 7 | 96.80 2 | 70.86 14 | 99.06 2 | 92.64 3 | 95.71 5 | 96.12 17 |
|
API-MVS | | | 82.28 82 | 80.53 93 | 87.54 22 | 96.13 8 | 70.59 19 | 93.63 58 | 91.04 140 | 65.72 224 | 75.45 108 | 92.83 87 | 56.11 121 | 98.89 7 | 64.10 191 | 89.75 72 | 93.15 110 |
|
jason | | | 86.40 31 | 86.17 31 | 87.11 32 | 86.16 170 | 70.54 20 | 95.71 14 | 92.19 99 | 82.00 24 | 84.58 34 | 94.34 59 | 61.86 76 | 95.53 121 | 87.76 21 | 90.89 63 | 95.27 39 |
jason: jason. |
PatchmatchNet | | | 77.46 151 | 74.63 167 | 85.96 66 | 89.55 114 | 70.35 21 | 79.97 276 | 89.55 190 | 72.23 141 | 70.94 147 | 76.91 257 | 57.03 106 | 92.79 201 | 54.27 241 | 81.17 129 | 94.74 59 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
IB-MVS | | 77.80 4 | 82.18 83 | 80.46 94 | 87.35 26 | 89.14 122 | 70.28 22 | 95.59 16 | 95.17 6 | 78.85 47 | 70.19 153 | 85.82 162 | 70.66 16 | 97.67 32 | 72.19 122 | 66.52 216 | 94.09 83 |
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 |
Patchmatch-test1 | | | 75.00 189 | 71.80 202 | 84.58 106 | 86.63 163 | 70.08 23 | 81.06 265 | 89.19 201 | 71.60 162 | 70.01 155 | 77.16 255 | 45.53 225 | 88.63 264 | 51.79 250 | 73.27 173 | 95.02 53 |
|
DWT-MVSNet_test | | | 83.95 59 | 82.80 67 | 87.41 24 | 92.90 53 | 70.07 24 | 89.12 189 | 94.42 25 | 82.15 20 | 77.64 87 | 91.77 103 | 70.81 15 | 96.22 92 | 65.03 183 | 81.36 128 | 95.94 22 |
|
VNet | | | 86.20 34 | 85.65 39 | 87.84 17 | 93.92 34 | 69.99 25 | 95.73 13 | 95.94 1 | 78.43 53 | 86.00 21 | 93.07 80 | 58.22 95 | 97.00 67 | 85.22 38 | 84.33 111 | 96.52 13 |
|
MS-PatchMatch | | | 77.90 148 | 76.50 145 | 82.12 161 | 85.99 172 | 69.95 26 | 91.75 127 | 92.70 78 | 73.97 112 | 62.58 226 | 84.44 176 | 41.11 244 | 95.78 107 | 63.76 192 | 92.17 48 | 80.62 278 |
|
PatchFormer-LS_test | | | 83.14 70 | 81.81 78 | 87.12 31 | 92.34 62 | 69.92 27 | 88.64 195 | 93.32 55 | 82.07 23 | 74.87 112 | 91.62 107 | 68.91 17 | 96.08 99 | 66.07 174 | 78.45 147 | 95.37 32 |
|
MVS_Test | | | 84.16 57 | 83.20 61 | 87.05 34 | 91.56 80 | 69.82 28 | 89.99 175 | 92.05 102 | 77.77 59 | 82.84 47 | 86.57 154 | 63.93 56 | 96.09 97 | 74.91 106 | 89.18 73 | 95.25 42 |
|
VDDNet | | | 80.50 104 | 78.26 122 | 87.21 28 | 86.19 169 | 69.79 29 | 94.48 33 | 91.31 130 | 60.42 257 | 79.34 71 | 90.91 111 | 38.48 253 | 96.56 89 | 82.16 55 | 81.05 130 | 95.27 39 |
|
MVS_111021_HR | | | 86.19 35 | 85.80 36 | 87.37 25 | 93.17 48 | 69.79 29 | 93.99 46 | 93.76 37 | 79.08 45 | 78.88 76 | 93.99 65 | 62.25 73 | 98.15 23 | 85.93 34 | 91.15 61 | 94.15 79 |
|
EPMVS | | | 78.49 142 | 75.98 151 | 86.02 62 | 91.21 89 | 69.68 31 | 80.23 272 | 91.20 133 | 75.25 93 | 72.48 134 | 78.11 245 | 54.65 144 | 93.69 181 | 57.66 232 | 83.04 119 | 94.69 60 |
|
GG-mvs-BLEND | | | | | 86.53 48 | 91.91 72 | 69.67 32 | 75.02 292 | 94.75 16 | | 78.67 81 | 90.85 112 | 77.91 2 | 94.56 139 | 72.25 119 | 93.74 28 | 95.36 33 |
|
DI_MVS_plusplus_test | | | 79.78 120 | 77.50 135 | 86.62 42 | 80.90 222 | 69.46 33 | 90.69 158 | 91.97 106 | 77.00 68 | 59.07 239 | 82.34 194 | 46.82 215 | 95.88 104 | 82.14 56 | 86.59 92 | 94.53 67 |
|
Effi-MVS+ | | | 83.82 62 | 82.76 68 | 86.99 36 | 89.56 113 | 69.40 34 | 91.35 141 | 86.12 256 | 72.59 135 | 83.22 45 | 92.81 88 | 59.60 85 | 96.01 102 | 81.76 59 | 87.80 82 | 95.56 29 |
|
WTY-MVS | | | 86.32 32 | 85.81 35 | 87.85 16 | 92.82 56 | 69.37 35 | 95.20 22 | 95.25 5 | 82.71 15 | 81.91 51 | 94.73 48 | 67.93 26 | 97.63 37 | 79.55 72 | 82.25 124 | 96.54 12 |
|
test_normal | | | 79.66 121 | 77.36 138 | 86.54 46 | 80.72 226 | 69.21 36 | 90.68 159 | 92.16 101 | 76.99 69 | 58.63 243 | 82.03 203 | 46.70 217 | 95.86 105 | 81.74 60 | 86.63 91 | 94.56 62 |
|
cascas | | | 78.18 144 | 75.77 154 | 85.41 84 | 87.14 157 | 69.11 37 | 92.96 75 | 91.15 135 | 66.71 216 | 70.47 149 | 86.07 159 | 37.49 263 | 96.48 90 | 70.15 140 | 79.80 135 | 90.65 151 |
|
NCCC | | | 89.07 8 | 89.46 8 | 87.91 15 | 96.60 5 | 69.05 38 | 96.38 7 | 94.64 20 | 84.42 10 | 86.74 19 | 96.20 8 | 66.56 35 | 98.76 10 | 89.03 15 | 94.56 18 | 95.92 24 |
|
MVSTER | | | 82.47 79 | 82.05 74 | 83.74 120 | 92.68 59 | 69.01 39 | 91.90 118 | 93.21 59 | 79.83 32 | 72.14 139 | 85.71 164 | 74.72 6 | 94.72 135 | 75.72 94 | 72.49 180 | 87.50 180 |
|
FMVSNet3 | | | 77.73 149 | 76.04 150 | 82.80 134 | 91.20 90 | 68.99 40 | 91.87 119 | 91.99 104 | 73.35 125 | 67.04 196 | 83.19 186 | 56.62 115 | 92.14 218 | 59.80 222 | 69.34 196 | 87.28 186 |
|
MSLP-MVS++ | | | 86.27 33 | 85.91 34 | 87.35 26 | 92.01 69 | 68.97 41 | 95.04 28 | 92.70 78 | 79.04 46 | 81.50 54 | 96.50 4 | 58.98 92 | 96.78 81 | 83.49 49 | 93.93 24 | 96.29 15 |
|
MVS_0305 | | | 89.30 7 | 89.66 7 | 88.24 13 | 94.37 24 | 68.90 42 | 96.56 5 | 93.37 54 | 86.96 4 | 90.37 3 | 95.68 17 | 62.66 70 | 99.01 3 | 93.05 1 | 93.92 25 | 96.80 7 |
|
test12 | | | | | 87.09 33 | 94.60 23 | 68.86 43 | | 92.91 73 | | 82.67 48 | | 65.44 44 | 97.55 40 | | 93.69 30 | 94.84 57 |
|
nrg030 | | | 80.93 99 | 79.86 99 | 84.13 114 | 83.69 200 | 68.83 44 | 93.23 69 | 91.20 133 | 75.55 85 | 75.06 111 | 88.22 141 | 63.04 69 | 94.74 134 | 81.88 58 | 66.88 213 | 88.82 165 |
|
SD-MVS | | | 87.49 17 | 87.49 18 | 87.50 23 | 93.60 39 | 68.82 45 | 93.90 52 | 92.63 83 | 76.86 71 | 87.90 12 | 95.76 14 | 66.17 36 | 97.63 37 | 89.06 14 | 91.48 57 | 96.05 19 |
|
xiu_mvs_v1_base_debu | | | 82.16 84 | 81.12 85 | 85.26 89 | 86.42 164 | 68.72 46 | 92.59 91 | 90.44 152 | 73.12 128 | 84.20 38 | 94.36 54 | 38.04 257 | 95.73 110 | 84.12 44 | 86.81 87 | 91.33 142 |
|
xiu_mvs_v1_base | | | 82.16 84 | 81.12 85 | 85.26 89 | 86.42 164 | 68.72 46 | 92.59 91 | 90.44 152 | 73.12 128 | 84.20 38 | 94.36 54 | 38.04 257 | 95.73 110 | 84.12 44 | 86.81 87 | 91.33 142 |
|
xiu_mvs_v1_base_debi | | | 82.16 84 | 81.12 85 | 85.26 89 | 86.42 164 | 68.72 46 | 92.59 91 | 90.44 152 | 73.12 128 | 84.20 38 | 94.36 54 | 38.04 257 | 95.73 110 | 84.12 44 | 86.81 87 | 91.33 142 |
|
MDTV_nov1_ep13 | | | | 72.61 193 | | 89.06 123 | 68.48 49 | 80.33 270 | 90.11 171 | 71.84 154 | 71.81 143 | 75.92 262 | 53.01 165 | 93.92 174 | 48.04 263 | 73.38 172 | |
|
CostFormer | | | 82.33 81 | 81.15 84 | 85.86 68 | 89.01 125 | 68.46 50 | 82.39 255 | 93.01 69 | 75.59 84 | 80.25 63 | 81.57 209 | 72.03 12 | 94.96 127 | 79.06 77 | 77.48 154 | 94.16 78 |
|
mvs_anonymous | | | 81.36 94 | 79.99 97 | 85.46 80 | 90.39 101 | 68.40 51 | 86.88 225 | 90.61 151 | 74.41 99 | 70.31 152 | 84.67 173 | 63.79 58 | 92.32 216 | 73.13 110 | 85.70 99 | 95.67 25 |
|
tpmp4_e23 | | | 78.85 133 | 76.55 144 | 85.77 72 | 89.25 118 | 68.39 52 | 81.63 262 | 91.38 128 | 70.40 179 | 75.21 110 | 79.22 241 | 67.37 30 | 94.79 130 | 58.98 227 | 75.51 164 | 94.13 80 |
|
gg-mvs-nofinetune | | | 77.18 161 | 74.31 173 | 85.80 70 | 91.42 85 | 68.36 53 | 71.78 295 | 94.72 17 | 49.61 293 | 77.12 95 | 45.92 316 | 77.41 3 | 93.98 171 | 67.62 159 | 93.16 36 | 95.05 49 |
|
DeepC-MVS_fast | | 79.48 2 | 87.95 12 | 88.00 12 | 87.79 18 | 95.86 12 | 68.32 54 | 95.74 11 | 94.11 31 | 83.82 12 | 83.49 44 | 96.19 9 | 64.53 52 | 98.44 16 | 83.42 50 | 94.88 12 | 96.61 10 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PAPR | | | 85.15 44 | 84.47 46 | 87.18 29 | 96.02 10 | 68.29 55 | 91.85 120 | 93.00 71 | 76.59 76 | 79.03 75 | 95.00 38 | 61.59 77 | 97.61 39 | 78.16 83 | 89.00 74 | 95.63 27 |
|
tpmrst | | | 80.57 102 | 79.14 115 | 84.84 99 | 90.10 103 | 68.28 56 | 81.70 259 | 89.72 187 | 77.63 62 | 75.96 102 | 79.54 239 | 64.94 50 | 92.71 203 | 75.43 98 | 77.28 157 | 93.55 99 |
|
tpm2 | | | 79.80 119 | 77.95 127 | 85.34 87 | 88.28 140 | 68.26 57 | 81.56 263 | 91.42 126 | 70.11 182 | 77.59 90 | 80.50 225 | 67.40 29 | 94.26 159 | 67.34 161 | 77.35 155 | 93.51 100 |
|
HPM-MVS++ | | | 89.37 6 | 89.95 6 | 87.64 19 | 95.10 17 | 68.23 58 | 95.24 21 | 94.49 23 | 82.43 17 | 88.90 8 | 96.35 5 | 71.89 13 | 98.63 12 | 88.76 17 | 96.40 2 | 96.06 18 |
|
HyFIR lowres test | | | 81.03 98 | 79.56 104 | 85.43 83 | 87.81 149 | 68.11 59 | 90.18 171 | 90.01 175 | 70.65 176 | 72.95 125 | 86.06 160 | 63.61 61 | 94.50 143 | 75.01 104 | 79.75 136 | 93.67 96 |
|
TSAR-MVS + MP. | | | 88.11 10 | 88.64 9 | 86.54 46 | 91.73 76 | 68.04 60 | 90.36 167 | 93.55 45 | 82.89 14 | 91.29 2 | 92.89 86 | 72.27 10 | 96.03 100 | 87.99 19 | 94.77 13 | 95.54 30 |
|
CR-MVSNet | | | 73.79 199 | 70.82 208 | 82.70 137 | 83.15 206 | 67.96 61 | 70.25 298 | 84.00 272 | 73.67 121 | 69.97 157 | 72.41 277 | 57.82 99 | 89.48 260 | 52.99 248 | 73.13 174 | 90.64 152 |
|
RPMNet | | | 69.58 234 | 65.21 244 | 82.70 137 | 83.15 206 | 67.96 61 | 70.25 298 | 86.15 255 | 46.83 301 | 69.97 157 | 65.10 302 | 56.48 118 | 89.48 260 | 35.79 299 | 73.13 174 | 90.64 152 |
|
V42 | | | 76.46 169 | 74.55 170 | 82.19 159 | 79.14 262 | 67.82 63 | 90.26 170 | 89.42 194 | 73.75 118 | 68.63 179 | 81.89 205 | 51.31 180 | 94.09 163 | 71.69 124 | 64.84 231 | 84.66 230 |
|
tpm cat1 | | | 75.30 184 | 72.21 198 | 84.58 106 | 88.52 133 | 67.77 64 | 78.16 287 | 88.02 228 | 61.88 250 | 68.45 182 | 76.37 258 | 60.65 81 | 94.03 169 | 53.77 244 | 74.11 168 | 91.93 136 |
|
HY-MVS | | 76.49 5 | 84.28 55 | 83.36 60 | 87.02 35 | 92.22 67 | 67.74 65 | 84.65 237 | 94.50 22 | 79.15 42 | 82.23 49 | 87.93 143 | 66.88 32 | 96.94 74 | 80.53 68 | 82.20 125 | 96.39 14 |
|
VDD-MVS | | | 83.06 72 | 81.81 78 | 86.81 37 | 90.86 95 | 67.70 66 | 95.40 18 | 91.50 123 | 75.46 86 | 81.78 52 | 92.34 97 | 40.09 247 | 97.13 60 | 86.85 29 | 82.04 126 | 95.60 28 |
|
FMVSNet2 | | | 76.07 173 | 74.01 178 | 82.26 157 | 88.85 126 | 67.66 67 | 91.33 142 | 91.61 118 | 70.84 173 | 65.98 201 | 82.25 196 | 48.03 206 | 92.00 223 | 58.46 228 | 68.73 202 | 87.10 188 |
|
CLD-MVS | | | 82.73 75 | 82.35 73 | 83.86 118 | 87.90 148 | 67.65 68 | 95.45 17 | 92.18 100 | 85.06 6 | 72.58 131 | 92.27 98 | 52.46 171 | 95.78 107 | 84.18 43 | 79.06 141 | 88.16 175 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
Test4 | | | 76.45 170 | 73.45 185 | 85.45 82 | 76.07 284 | 67.61 69 | 88.38 199 | 90.83 143 | 76.71 74 | 53.06 272 | 79.65 238 | 31.61 286 | 94.35 153 | 78.47 80 | 86.22 96 | 94.40 71 |
|
1314 | | | 80.70 101 | 78.95 116 | 85.94 67 | 87.77 150 | 67.56 70 | 87.91 207 | 92.55 86 | 72.17 145 | 67.44 191 | 93.09 77 | 50.27 186 | 97.04 64 | 71.68 125 | 87.64 83 | 93.23 108 |
|
ACMMP_Plus | | | 86.05 36 | 85.80 36 | 86.80 38 | 91.58 79 | 67.53 71 | 91.79 122 | 93.49 48 | 74.93 96 | 84.61 33 | 95.30 27 | 59.42 86 | 97.92 27 | 86.13 32 | 94.92 9 | 94.94 55 |
|
PVSNet_BlendedMVS | | | 83.38 67 | 83.43 56 | 83.22 130 | 93.76 35 | 67.53 71 | 94.06 42 | 93.61 42 | 79.13 43 | 81.00 57 | 85.14 168 | 63.19 66 | 97.29 51 | 87.08 26 | 73.91 171 | 84.83 229 |
|
PVSNet_Blended | | | 86.73 30 | 86.86 26 | 86.31 58 | 93.76 35 | 67.53 71 | 96.33 8 | 93.61 42 | 82.34 18 | 81.00 57 | 93.08 78 | 63.19 66 | 97.29 51 | 87.08 26 | 91.38 58 | 94.13 80 |
|
MVS_dtu | | | 83.63 66 | 82.83 66 | 86.02 62 | 90.57 97 | 67.50 74 | 90.51 163 | 89.84 179 | 84.43 9 | 87.88 13 | 93.73 69 | 48.69 203 | 96.94 74 | 75.60 97 | 84.23 114 | 93.61 98 |
|
test_prior3 | | | 87.38 18 | 87.70 15 | 86.42 52 | 94.71 21 | 67.35 75 | 95.10 26 | 93.10 67 | 75.40 89 | 85.25 30 | 95.61 20 | 67.94 24 | 96.84 79 | 87.47 22 | 94.77 13 | 95.05 49 |
|
test_prior | | | | | 86.42 52 | 94.71 21 | 67.35 75 | | 93.10 67 | | | | | 96.84 79 | | | 95.05 49 |
|
TEST9 | | | | | | 94.18 27 | 67.28 77 | 94.16 35 | 93.51 46 | 71.75 159 | 85.52 26 | 95.33 25 | 68.01 23 | 97.27 53 | | | |
|
train_agg | | | 87.21 21 | 87.42 20 | 86.60 43 | 94.18 27 | 67.28 77 | 94.16 35 | 93.51 46 | 71.87 151 | 85.52 26 | 95.33 25 | 68.19 21 | 97.27 53 | 89.09 12 | 94.90 10 | 95.25 42 |
|
test_8 | | | | | | 94.19 26 | 67.19 79 | 94.15 37 | 93.42 51 | 71.87 151 | 85.38 28 | 95.35 24 | 68.19 21 | 96.95 73 | | | |
|
CDPH-MVS | | | 85.71 40 | 85.46 40 | 86.46 50 | 94.75 20 | 67.19 79 | 93.89 53 | 92.83 76 | 70.90 172 | 83.09 46 | 95.28 28 | 63.62 60 | 97.36 47 | 80.63 67 | 94.18 21 | 94.84 57 |
|
test_prior4 | | | | | | | 67.18 81 | 93.92 51 | | | | | | | | | |
|
v2v482 | | | 77.42 152 | 75.65 159 | 82.73 136 | 80.38 240 | 67.13 82 | 91.85 120 | 90.23 164 | 75.09 94 | 69.37 167 | 83.39 184 | 53.79 156 | 94.44 144 | 71.77 123 | 65.00 230 | 86.63 198 |
|
MVS_test0326 | | | 84.44 51 | 83.92 51 | 86.00 64 | 91.53 83 | 67.10 83 | 92.43 95 | 94.41 26 | 84.87 7 | 88.04 11 | 92.75 89 | 49.47 193 | 97.10 61 | 75.66 96 | 86.16 97 | 93.79 95 |
|
DP-MVS Recon | | | 82.73 75 | 81.65 80 | 85.98 65 | 97.31 3 | 67.06 84 | 95.15 24 | 91.99 104 | 69.08 192 | 76.50 101 | 93.89 67 | 54.48 147 | 98.20 21 | 70.76 136 | 85.66 100 | 92.69 121 |
|
tpmvs | | | 72.88 205 | 69.76 213 | 82.22 158 | 90.98 91 | 67.05 85 | 78.22 286 | 88.30 223 | 63.10 241 | 64.35 212 | 74.98 265 | 55.09 134 | 94.27 157 | 43.25 274 | 69.57 195 | 85.34 225 |
|
gm-plane-assit | | | | | | 88.42 138 | 67.04 86 | | | 78.62 52 | | 91.83 102 | | 97.37 46 | 76.57 91 | | |
|
agg_prior1 | | | 87.02 23 | 87.26 22 | 86.28 59 | 94.16 31 | 66.97 87 | 94.08 41 | 93.31 56 | 71.85 153 | 84.49 35 | 95.39 23 | 68.91 17 | 96.75 83 | 88.84 16 | 94.32 20 | 95.13 46 |
|
agg_prior | | | | | | 94.16 31 | 66.97 87 | | 93.31 56 | | 84.49 35 | | | 96.75 83 | | | |
|
agg_prior3 | | | 86.93 25 | 87.08 23 | 86.48 49 | 94.21 25 | 66.95 89 | 94.14 38 | 93.40 52 | 71.80 156 | 84.86 32 | 95.13 35 | 66.16 37 | 97.25 55 | 89.09 12 | 94.90 10 | 95.25 42 |
|
diffmvs | | | 80.18 109 | 78.55 120 | 85.07 93 | 88.56 132 | 66.93 90 | 86.70 228 | 88.62 217 | 70.42 178 | 78.69 80 | 85.26 166 | 56.93 110 | 94.77 131 | 68.68 153 | 83.09 118 | 93.51 100 |
|
ADS-MVSNet | | | 68.54 241 | 64.38 251 | 81.03 183 | 88.06 144 | 66.90 91 | 68.01 305 | 84.02 271 | 57.57 269 | 64.48 209 | 69.87 292 | 38.68 249 | 89.21 263 | 40.87 284 | 67.89 208 | 86.97 189 |
|
v1neww | | | 77.39 153 | 75.71 156 | 82.44 143 | 80.69 228 | 66.83 92 | 91.94 115 | 90.18 167 | 74.19 106 | 69.60 161 | 82.51 190 | 54.99 138 | 94.44 144 | 71.68 125 | 65.60 219 | 86.05 207 |
|
v7new | | | 77.39 153 | 75.71 156 | 82.44 143 | 80.69 228 | 66.83 92 | 91.94 115 | 90.18 167 | 74.19 106 | 69.60 161 | 82.51 190 | 54.99 138 | 94.44 144 | 71.68 125 | 65.60 219 | 86.05 207 |
|
v18 | | | 71.94 211 | 69.43 214 | 79.50 210 | 80.74 225 | 66.82 94 | 88.16 201 | 86.66 242 | 68.95 193 | 55.55 255 | 72.66 272 | 55.03 136 | 90.15 247 | 64.78 185 | 52.30 283 | 81.54 260 |
|
v8 | | | 75.35 183 | 73.26 187 | 81.61 172 | 80.67 230 | 66.82 94 | 89.54 181 | 89.27 198 | 71.65 160 | 63.30 220 | 80.30 229 | 54.99 138 | 94.06 165 | 67.33 162 | 62.33 245 | 83.94 235 |
|
v6 | | | 77.39 153 | 75.71 156 | 82.44 143 | 80.67 230 | 66.82 94 | 91.94 115 | 90.18 167 | 74.19 106 | 69.60 161 | 82.50 193 | 55.00 137 | 94.44 144 | 71.68 125 | 65.60 219 | 86.05 207 |
|
v17 | | | 71.77 214 | 69.20 217 | 79.46 212 | 80.62 235 | 66.81 97 | 87.93 205 | 86.63 244 | 68.71 197 | 55.25 257 | 72.49 274 | 54.72 143 | 90.11 250 | 64.50 188 | 51.97 285 | 81.47 261 |
|
v16 | | | 71.81 212 | 69.26 216 | 79.47 211 | 80.66 232 | 66.81 97 | 87.93 205 | 86.63 244 | 68.70 198 | 55.35 256 | 72.51 273 | 54.75 142 | 90.12 249 | 64.51 187 | 52.28 284 | 81.47 261 |
|
3Dnovator+ | | 73.60 7 | 82.10 87 | 80.60 92 | 86.60 43 | 90.89 94 | 66.80 99 | 95.20 22 | 93.44 50 | 74.05 109 | 67.42 192 | 92.49 92 | 49.46 194 | 97.65 36 | 70.80 135 | 91.68 53 | 95.33 34 |
|
PAPM_NR | | | 82.97 73 | 81.84 77 | 86.37 55 | 94.10 33 | 66.76 100 | 87.66 212 | 92.84 75 | 69.96 184 | 74.07 119 | 93.57 71 | 63.10 68 | 97.50 42 | 70.66 137 | 90.58 67 | 94.85 56 |
|
v1141 | | | 77.28 158 | 75.57 160 | 82.42 148 | 80.63 234 | 66.73 101 | 91.96 111 | 90.42 155 | 74.41 99 | 69.46 164 | 82.12 200 | 55.09 134 | 94.40 149 | 70.99 132 | 65.05 226 | 86.12 204 |
|
v1 | | | 77.29 157 | 75.57 160 | 82.42 148 | 80.61 238 | 66.73 101 | 91.96 111 | 90.42 155 | 74.41 99 | 69.46 164 | 82.12 200 | 55.14 132 | 94.40 149 | 71.00 130 | 65.04 227 | 86.13 203 |
|
divwei89l23v2f112 | | | 77.28 158 | 75.57 160 | 82.42 148 | 80.62 235 | 66.72 103 | 91.96 111 | 90.42 155 | 74.41 99 | 69.46 164 | 82.12 200 | 55.11 133 | 94.40 149 | 71.00 130 | 65.04 227 | 86.12 204 |
|
v10 | | | 74.77 191 | 72.54 195 | 81.46 173 | 80.33 245 | 66.71 104 | 89.15 188 | 89.08 207 | 70.94 171 | 63.08 221 | 79.86 234 | 52.52 169 | 94.04 168 | 65.70 179 | 62.17 246 | 83.64 237 |
|
v15 | | | 71.40 216 | 68.75 219 | 79.35 213 | 80.39 239 | 66.70 105 | 87.57 214 | 86.64 243 | 68.66 199 | 54.68 259 | 72.00 281 | 54.50 145 | 89.98 252 | 63.69 193 | 50.66 290 | 81.38 265 |
|
v7 | | | 76.83 165 | 75.01 165 | 82.29 154 | 80.35 241 | 66.70 105 | 91.68 129 | 89.97 176 | 73.47 124 | 69.22 169 | 82.22 197 | 52.52 169 | 94.43 148 | 69.73 142 | 65.96 218 | 85.74 218 |
|
V14 | | | 71.29 218 | 68.61 221 | 79.31 214 | 80.34 243 | 66.65 107 | 87.39 216 | 86.61 246 | 68.41 203 | 54.49 261 | 71.91 282 | 54.25 150 | 89.96 253 | 63.50 194 | 50.62 291 | 81.33 267 |
|
DeepC-MVS | | 77.85 3 | 85.52 41 | 85.24 42 | 86.37 55 | 88.80 129 | 66.64 108 | 92.15 100 | 93.68 40 | 81.07 28 | 76.91 98 | 93.64 70 | 62.59 72 | 98.44 16 | 85.50 36 | 92.84 39 | 94.03 87 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
v1144 | | | 76.73 167 | 74.88 166 | 82.27 155 | 80.23 248 | 66.60 109 | 91.68 129 | 90.21 166 | 73.69 119 | 69.06 172 | 81.89 205 | 52.73 168 | 94.40 149 | 69.21 148 | 65.23 223 | 85.80 214 |
|
V9 | | | 71.16 219 | 68.46 223 | 79.27 216 | 80.26 246 | 66.60 109 | 87.21 219 | 86.56 247 | 68.17 204 | 54.26 264 | 71.81 284 | 54.00 152 | 89.93 254 | 63.28 197 | 50.57 292 | 81.27 268 |
|
PVSNet_Blended_VisFu | | | 83.97 58 | 83.50 53 | 85.39 85 | 90.02 104 | 66.59 111 | 93.77 56 | 91.73 113 | 77.43 66 | 77.08 97 | 89.81 127 | 63.77 59 | 96.97 71 | 79.67 71 | 88.21 79 | 92.60 124 |
|
v11 | | | 71.05 222 | 68.32 226 | 79.23 217 | 80.34 243 | 66.57 112 | 87.01 222 | 86.55 248 | 68.11 205 | 54.40 262 | 71.66 286 | 52.94 166 | 89.91 255 | 62.71 205 | 51.12 288 | 81.21 269 |
|
v144192 | | | 76.05 174 | 74.03 177 | 82.12 161 | 79.50 257 | 66.55 113 | 91.39 137 | 89.71 188 | 72.30 140 | 68.17 183 | 81.33 213 | 51.75 176 | 94.03 169 | 67.94 155 | 64.19 236 | 85.77 215 |
|
v12 | | | 71.02 223 | 68.29 228 | 79.22 218 | 80.18 249 | 66.53 114 | 87.01 222 | 86.54 249 | 67.90 206 | 54.00 267 | 71.70 285 | 53.66 157 | 89.91 255 | 63.09 199 | 50.51 293 | 81.21 269 |
|
testing_2 | | | 71.09 221 | 67.32 233 | 82.40 151 | 69.82 301 | 66.52 115 | 83.64 242 | 90.77 145 | 72.21 142 | 45.12 298 | 71.07 291 | 27.60 298 | 93.74 179 | 75.71 95 | 69.96 191 | 86.95 191 |
|
v13 | | | 70.90 224 | 68.15 229 | 79.15 222 | 80.08 250 | 66.45 116 | 86.83 226 | 86.50 250 | 67.62 212 | 53.78 269 | 71.61 287 | 53.51 161 | 89.87 257 | 62.89 203 | 50.50 294 | 81.14 271 |
|
VPNet | | | 78.82 134 | 77.53 134 | 82.70 137 | 84.52 188 | 66.44 117 | 93.93 50 | 92.23 93 | 80.46 30 | 72.60 130 | 88.38 137 | 49.18 197 | 93.13 189 | 72.47 118 | 63.97 239 | 88.55 169 |
|
SteuartSystems-ACMMP | | | 86.82 29 | 86.90 25 | 86.58 45 | 90.42 99 | 66.38 118 | 96.09 9 | 93.87 33 | 77.73 60 | 84.01 42 | 95.66 18 | 63.39 63 | 97.94 26 | 87.40 24 | 93.55 32 | 95.42 31 |
Skip Steuart: Steuart Systems R&D Blog. |
v1921920 | | | 75.63 181 | 73.49 184 | 82.06 165 | 79.38 258 | 66.35 119 | 91.07 151 | 89.48 191 | 71.98 148 | 67.99 184 | 81.22 216 | 49.16 199 | 93.90 175 | 66.56 168 | 64.56 235 | 85.92 213 |
|
MVP-Stereo | | | 77.12 162 | 76.23 148 | 79.79 203 | 81.72 216 | 66.34 120 | 89.29 184 | 90.88 142 | 70.56 177 | 62.01 229 | 82.88 187 | 49.34 195 | 94.13 161 | 65.55 180 | 93.80 26 | 78.88 290 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
GA-MVS | | | 78.33 143 | 76.23 148 | 84.65 104 | 83.65 201 | 66.30 121 | 91.44 135 | 90.14 170 | 76.01 81 | 70.32 151 | 84.02 178 | 42.50 237 | 94.72 135 | 70.98 133 | 77.00 158 | 92.94 117 |
|
APDe-MVS | | | 87.54 16 | 87.84 13 | 86.65 41 | 96.07 9 | 66.30 121 | 94.84 31 | 93.78 35 | 69.35 189 | 88.39 9 | 96.34 6 | 67.74 28 | 97.66 35 | 90.62 8 | 93.44 33 | 96.01 21 |
|
v1192 | | | 75.98 176 | 73.92 179 | 82.15 160 | 79.73 253 | 66.24 123 | 91.22 146 | 89.75 182 | 72.67 134 | 68.49 181 | 81.42 211 | 49.86 190 | 94.27 157 | 67.08 163 | 65.02 229 | 85.95 211 |
|
dp | | | 75.01 188 | 72.09 199 | 83.76 119 | 89.28 117 | 66.22 124 | 79.96 277 | 89.75 182 | 71.16 169 | 67.80 189 | 77.19 253 | 51.81 175 | 92.54 208 | 50.39 254 | 71.44 188 | 92.51 127 |
|
EPNet | | | 87.84 14 | 88.38 10 | 86.23 60 | 93.30 44 | 66.05 125 | 95.26 20 | 94.84 12 | 87.09 3 | 88.06 10 | 94.53 51 | 66.79 33 | 97.34 49 | 83.89 47 | 91.68 53 | 95.29 37 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v1240 | | | 75.21 186 | 72.98 189 | 81.88 166 | 79.20 260 | 66.00 126 | 90.75 157 | 89.11 206 | 71.63 161 | 67.41 193 | 81.22 216 | 47.36 213 | 93.87 176 | 65.46 181 | 64.72 233 | 85.77 215 |
|
PCF-MVS | | 73.15 9 | 79.29 126 | 77.63 132 | 84.29 112 | 86.06 171 | 65.96 127 | 87.03 220 | 91.10 137 | 69.86 185 | 69.79 160 | 90.64 114 | 57.54 102 | 96.59 86 | 64.37 190 | 82.29 123 | 90.32 154 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MAR-MVS | | | 84.18 56 | 83.43 56 | 86.44 51 | 96.25 7 | 65.93 128 | 94.28 34 | 94.27 30 | 74.41 99 | 79.16 74 | 95.61 20 | 53.99 153 | 98.88 8 | 69.62 144 | 93.26 35 | 94.50 68 |
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 |
Fast-Effi-MVS+ | | | 81.14 96 | 80.01 96 | 84.51 109 | 90.24 102 | 65.86 129 | 94.12 39 | 89.15 204 | 73.81 117 | 75.37 109 | 88.26 140 | 57.26 103 | 94.53 142 | 66.97 165 | 84.92 103 | 93.15 110 |
|
AdaColmap | | | 78.94 132 | 77.00 141 | 84.76 100 | 96.34 6 | 65.86 129 | 92.66 88 | 87.97 230 | 62.18 246 | 70.56 148 | 92.37 96 | 43.53 235 | 97.35 48 | 64.50 188 | 82.86 120 | 91.05 148 |
|
Regformer-1 | | | 87.24 20 | 87.60 17 | 86.15 61 | 95.14 15 | 65.83 131 | 93.95 48 | 95.12 7 | 82.11 21 | 84.25 37 | 95.73 15 | 67.88 27 | 98.35 18 | 85.60 35 | 88.64 76 | 94.26 72 |
|
BH-RMVSNet | | | 79.46 125 | 77.65 131 | 84.89 97 | 91.68 78 | 65.66 132 | 93.55 61 | 88.09 227 | 72.93 131 | 73.37 123 | 91.12 110 | 46.20 223 | 96.12 96 | 56.28 235 | 85.61 101 | 92.91 118 |
|
MP-MVS-pluss | | | 85.24 43 | 85.13 43 | 85.56 77 | 91.42 85 | 65.59 133 | 91.54 134 | 92.51 87 | 74.56 98 | 80.62 59 | 95.64 19 | 59.15 89 | 97.00 67 | 86.94 28 | 93.80 26 | 94.07 85 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
PHI-MVS | | | 86.83 28 | 86.85 27 | 86.78 39 | 93.47 43 | 65.55 134 | 95.39 19 | 95.10 9 | 71.77 158 | 85.69 25 | 96.52 3 | 62.07 74 | 98.77 9 | 86.06 33 | 95.60 6 | 96.03 20 |
|
114514_t | | | 79.17 128 | 77.67 130 | 83.68 124 | 95.32 14 | 65.53 135 | 92.85 80 | 91.60 119 | 63.49 236 | 67.92 186 | 90.63 116 | 46.65 218 | 95.72 114 | 67.01 164 | 83.54 117 | 89.79 159 |
|
ab-mvs | | | 80.18 109 | 78.31 121 | 85.80 70 | 88.44 137 | 65.49 136 | 83.00 252 | 92.67 80 | 71.82 155 | 77.36 92 | 85.01 169 | 54.50 145 | 96.59 86 | 76.35 93 | 75.63 163 | 95.32 36 |
|
TSAR-MVS + GP. | | | 87.96 11 | 88.37 11 | 86.70 40 | 93.51 42 | 65.32 137 | 95.15 24 | 93.84 34 | 78.17 55 | 85.93 22 | 94.80 47 | 75.80 4 | 98.21 20 | 89.38 10 | 88.78 75 | 96.59 11 |
|
pmmvs4 | | | 73.92 198 | 71.81 201 | 80.25 192 | 79.17 261 | 65.24 138 | 87.43 215 | 87.26 239 | 67.64 211 | 63.46 218 | 83.91 179 | 48.96 201 | 91.53 234 | 62.94 202 | 65.49 222 | 83.96 234 |
|
APD-MVS | | | 85.93 37 | 85.99 32 | 85.76 73 | 95.98 11 | 65.21 139 | 93.59 60 | 92.58 85 | 66.54 217 | 86.17 20 | 95.88 13 | 63.83 57 | 97.00 67 | 86.39 31 | 92.94 37 | 95.06 48 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MPTG | | | 84.73 48 | 84.47 46 | 85.50 78 | 91.89 73 | 65.16 140 | 91.55 133 | 92.23 93 | 75.32 91 | 80.53 60 | 95.21 33 | 56.06 122 | 97.16 58 | 84.86 41 | 92.55 43 | 94.18 75 |
|
MTAPA | | | 83.91 60 | 83.38 59 | 85.50 78 | 91.89 73 | 65.16 140 | 81.75 258 | 92.23 93 | 75.32 91 | 80.53 60 | 95.21 33 | 56.06 122 | 97.16 58 | 84.86 41 | 92.55 43 | 94.18 75 |
|
GBi-Net | | | 75.65 179 | 73.83 180 | 81.10 180 | 88.85 126 | 65.11 142 | 90.01 172 | 90.32 158 | 70.84 173 | 67.04 196 | 80.25 230 | 48.03 206 | 91.54 231 | 59.80 222 | 69.34 196 | 86.64 195 |
|
test1 | | | 75.65 179 | 73.83 180 | 81.10 180 | 88.85 126 | 65.11 142 | 90.01 172 | 90.32 158 | 70.84 173 | 67.04 196 | 80.25 230 | 48.03 206 | 91.54 231 | 59.80 222 | 69.34 196 | 86.64 195 |
|
FMVSNet1 | | | 72.71 207 | 69.91 211 | 81.10 180 | 83.60 202 | 65.11 142 | 90.01 172 | 90.32 158 | 63.92 234 | 63.56 217 | 80.25 230 | 36.35 271 | 91.54 231 | 54.46 240 | 66.75 214 | 86.64 195 |
|
Regformer-3 | | | 85.80 39 | 85.92 33 | 85.46 80 | 94.17 29 | 65.09 145 | 92.95 76 | 95.11 8 | 81.13 27 | 81.68 53 | 95.04 36 | 65.82 41 | 98.32 19 | 83.02 51 | 84.36 108 | 92.97 116 |
|
HFP-MVS | | | 84.73 48 | 84.40 48 | 85.72 74 | 93.75 37 | 65.01 146 | 93.50 63 | 93.19 62 | 72.19 143 | 79.22 72 | 94.93 41 | 59.04 90 | 97.67 32 | 81.55 61 | 92.21 45 | 94.49 69 |
|
#test# | | | 84.98 46 | 84.74 45 | 85.72 74 | 93.75 37 | 65.01 146 | 94.09 40 | 93.19 62 | 73.55 123 | 79.22 72 | 94.93 41 | 59.04 90 | 97.67 32 | 82.66 53 | 92.21 45 | 94.49 69 |
|
PVSNet | | 73.49 8 | 80.05 113 | 78.63 118 | 84.31 111 | 90.92 93 | 64.97 148 | 92.47 94 | 91.05 139 | 79.18 41 | 72.43 136 | 90.51 118 | 37.05 269 | 94.06 165 | 68.06 154 | 86.00 98 | 93.90 93 |
|
Regformer-2 | | | 87.00 24 | 87.43 19 | 85.71 76 | 95.14 15 | 64.73 149 | 93.95 48 | 94.95 10 | 81.69 26 | 84.03 41 | 95.73 15 | 67.35 31 | 98.19 22 | 85.40 37 | 88.64 76 | 94.20 74 |
|
tpm | | | 78.58 140 | 77.03 140 | 83.22 130 | 85.94 175 | 64.56 150 | 83.21 250 | 91.14 136 | 78.31 54 | 73.67 122 | 79.68 236 | 64.01 54 | 92.09 221 | 66.07 174 | 71.26 189 | 93.03 114 |
|
VPA-MVSNet | | | 79.03 129 | 78.00 126 | 82.11 164 | 85.95 173 | 64.48 151 | 93.22 70 | 94.66 19 | 75.05 95 | 74.04 120 | 84.95 170 | 52.17 173 | 93.52 184 | 74.90 107 | 67.04 212 | 88.32 174 |
|
CDS-MVSNet | | | 81.43 93 | 80.74 89 | 83.52 126 | 86.26 168 | 64.45 152 | 92.09 104 | 90.65 150 | 75.83 83 | 73.95 121 | 89.81 127 | 63.97 55 | 92.91 197 | 71.27 129 | 82.82 121 | 93.20 109 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
v148 | | | 76.19 171 | 74.47 172 | 81.36 174 | 80.05 252 | 64.44 153 | 91.75 127 | 90.23 164 | 73.68 120 | 67.13 195 | 80.84 221 | 55.92 125 | 93.86 178 | 68.95 150 | 61.73 251 | 85.76 217 |
|
XXY-MVS | | | 77.94 147 | 76.44 146 | 82.43 146 | 82.60 210 | 64.44 153 | 92.01 107 | 91.83 111 | 73.59 122 | 70.00 156 | 85.82 162 | 54.43 148 | 94.76 132 | 69.63 143 | 68.02 207 | 88.10 176 |
|
MIMVSNet | | | 71.64 215 | 68.44 224 | 81.23 177 | 81.97 215 | 64.44 153 | 73.05 294 | 88.80 213 | 69.67 186 | 64.59 207 | 74.79 266 | 32.79 280 | 87.82 273 | 53.99 242 | 76.35 160 | 91.42 141 |
|
Patchmtry | | | 67.53 248 | 63.93 252 | 78.34 226 | 82.12 213 | 64.38 156 | 68.72 302 | 84.00 272 | 48.23 298 | 59.24 236 | 72.41 277 | 57.82 99 | 89.27 262 | 46.10 269 | 56.68 273 | 81.36 266 |
|
ACMMPR | | | 84.37 52 | 84.06 49 | 85.28 88 | 93.56 40 | 64.37 157 | 93.50 63 | 93.15 65 | 72.19 143 | 78.85 78 | 94.86 45 | 56.69 114 | 97.45 43 | 81.55 61 | 92.20 47 | 94.02 88 |
|
BH-w/o | | | 80.49 105 | 79.30 111 | 84.05 116 | 90.83 96 | 64.36 158 | 93.60 59 | 89.42 194 | 74.35 104 | 69.09 171 | 90.15 122 | 55.23 129 | 95.61 116 | 64.61 186 | 86.43 95 | 92.17 134 |
|
region2R | | | 84.36 53 | 84.03 50 | 85.36 86 | 93.54 41 | 64.31 159 | 93.43 66 | 92.95 72 | 72.16 146 | 78.86 77 | 94.84 46 | 56.97 108 | 97.53 41 | 81.38 64 | 92.11 49 | 94.24 73 |
|
1121 | | | 81.25 95 | 80.05 95 | 84.87 98 | 92.30 64 | 64.31 159 | 87.91 207 | 91.39 127 | 59.44 263 | 79.94 65 | 92.91 84 | 57.09 104 | 97.01 65 | 66.63 166 | 92.81 40 | 93.29 106 |
|
新几何1 | | | | | 84.73 101 | 92.32 63 | 64.28 161 | | 91.46 125 | 59.56 262 | 79.77 67 | 92.90 85 | 56.95 109 | 96.57 88 | 63.40 195 | 92.91 38 | 93.34 103 |
|
原ACMM1 | | | | | 84.42 110 | 93.21 47 | 64.27 162 | | 93.40 52 | 65.39 225 | 79.51 70 | 92.50 91 | 58.11 97 | 96.69 85 | 65.27 182 | 93.96 23 | 92.32 130 |
|
MP-MVS | | | 85.02 45 | 84.97 44 | 85.17 92 | 92.60 60 | 64.27 162 | 93.24 68 | 92.27 92 | 73.13 127 | 79.63 69 | 94.43 52 | 61.90 75 | 97.17 57 | 85.00 39 | 92.56 42 | 94.06 86 |
|
PGM-MVS | | | 83.25 68 | 82.70 70 | 84.92 96 | 92.81 57 | 64.07 164 | 90.44 164 | 92.20 98 | 71.28 168 | 77.23 94 | 94.43 52 | 55.17 131 | 97.31 50 | 79.33 74 | 91.38 58 | 93.37 102 |
|
HSP-MVS | | | 90.38 2 | 91.89 1 | 85.84 69 | 92.83 54 | 64.03 165 | 93.06 73 | 94.52 21 | 82.19 19 | 93.65 1 | 96.15 10 | 85.89 1 | 97.19 56 | 91.02 7 | 97.75 1 | 96.29 15 |
|
CP-MVS | | | 83.71 65 | 83.40 58 | 84.65 104 | 93.14 49 | 63.84 166 | 94.59 32 | 92.28 91 | 71.03 170 | 77.41 91 | 94.92 43 | 55.21 130 | 96.19 93 | 81.32 65 | 90.70 65 | 93.91 92 |
|
OPM-MVS | | | 79.00 130 | 78.09 124 | 81.73 168 | 83.52 203 | 63.83 167 | 91.64 132 | 90.30 162 | 76.36 79 | 71.97 141 | 89.93 126 | 46.30 222 | 95.17 124 | 75.10 101 | 77.70 151 | 86.19 202 |
|
XVS | | | 83.87 61 | 83.47 54 | 85.05 94 | 93.22 45 | 63.78 168 | 92.92 78 | 92.66 81 | 73.99 110 | 78.18 82 | 94.31 61 | 55.25 127 | 97.41 44 | 79.16 75 | 91.58 55 | 93.95 90 |
|
X-MVStestdata | | | 76.86 163 | 74.13 176 | 85.05 94 | 93.22 45 | 63.78 168 | 92.92 78 | 92.66 81 | 73.99 110 | 78.18 82 | 10.19 331 | 55.25 127 | 97.41 44 | 79.16 75 | 91.58 55 | 93.95 90 |
|
TESTMET0.1,1 | | | 82.41 80 | 81.98 76 | 83.72 123 | 88.08 143 | 63.74 170 | 92.70 84 | 93.77 36 | 79.30 38 | 77.61 89 | 87.57 146 | 58.19 96 | 94.08 164 | 73.91 109 | 86.68 90 | 93.33 105 |
|
BH-untuned | | | 78.68 138 | 77.08 139 | 83.48 128 | 89.84 108 | 63.74 170 | 92.70 84 | 88.59 218 | 71.57 163 | 66.83 199 | 88.65 133 | 51.75 176 | 95.39 122 | 59.03 225 | 84.77 105 | 91.32 145 |
|
MSDG | | | 69.54 235 | 65.73 239 | 80.96 184 | 85.11 182 | 63.71 172 | 84.19 239 | 83.28 279 | 56.95 274 | 54.50 260 | 84.03 177 | 31.50 287 | 96.03 100 | 42.87 278 | 69.13 199 | 83.14 248 |
|
PatchT | | | 69.11 238 | 65.37 243 | 80.32 190 | 82.07 214 | 63.68 173 | 67.96 307 | 87.62 233 | 50.86 291 | 69.37 167 | 65.18 301 | 57.09 104 | 88.53 268 | 41.59 282 | 66.60 215 | 88.74 166 |
|
HQP5-MVS | | | | | | | 63.66 174 | | | | | | | | | | |
|
HQP-MVS | | | 81.14 96 | 80.64 91 | 82.64 140 | 87.54 151 | 63.66 174 | 94.06 42 | 91.70 116 | 79.80 33 | 74.18 115 | 90.30 120 | 51.63 178 | 95.61 116 | 77.63 86 | 78.90 142 | 88.63 167 |
|
EI-MVSNet-Vis-set | | | 83.77 63 | 83.67 52 | 84.06 115 | 92.79 58 | 63.56 176 | 91.76 125 | 94.81 15 | 79.65 36 | 77.87 84 | 94.09 63 | 63.35 64 | 97.90 28 | 79.35 73 | 79.36 138 | 90.74 150 |
|
TAMVS | | | 80.37 106 | 79.45 107 | 83.13 132 | 85.14 181 | 63.37 177 | 91.23 145 | 90.76 146 | 74.81 97 | 72.65 129 | 88.49 134 | 60.63 82 | 92.95 193 | 69.41 146 | 81.95 127 | 93.08 113 |
|
ACMH | | 63.93 17 | 68.62 239 | 64.81 245 | 80.03 196 | 85.22 180 | 63.25 178 | 87.72 211 | 84.66 266 | 60.83 255 | 51.57 278 | 79.43 240 | 27.29 299 | 94.96 127 | 41.76 280 | 64.84 231 | 81.88 258 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Regformer-4 | | | 85.45 42 | 85.69 38 | 84.73 101 | 94.17 29 | 63.23 179 | 92.95 76 | 94.83 13 | 80.66 29 | 81.29 55 | 95.04 36 | 65.12 46 | 98.08 25 | 82.74 52 | 84.36 108 | 92.88 120 |
|
EI-MVSNet-UG-set | | | 83.14 70 | 82.96 62 | 83.67 125 | 92.28 65 | 63.19 180 | 91.38 139 | 94.68 18 | 79.22 40 | 76.60 99 | 93.75 68 | 62.64 71 | 97.76 30 | 78.07 84 | 78.01 148 | 90.05 157 |
|
NP-MVS | | | | | | 87.41 154 | 63.04 181 | | | | | 90.30 120 | | | | | |
|
IterMVS | | | 72.65 209 | 70.83 207 | 78.09 233 | 82.17 212 | 62.96 182 | 87.64 213 | 86.28 252 | 71.56 164 | 60.44 231 | 78.85 243 | 45.42 227 | 86.66 278 | 63.30 196 | 61.83 248 | 84.65 231 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EG-PatchMatch MVS | | | 68.55 240 | 65.41 242 | 77.96 234 | 78.69 267 | 62.93 183 | 89.86 177 | 89.17 202 | 60.55 256 | 50.27 283 | 77.73 248 | 22.60 307 | 94.06 165 | 47.18 266 | 72.65 179 | 76.88 298 |
|
DP-MVS | | | 69.90 230 | 66.48 236 | 80.14 193 | 95.36 13 | 62.93 183 | 89.56 179 | 76.11 297 | 50.27 292 | 57.69 249 | 85.23 167 | 39.68 248 | 95.73 110 | 33.35 303 | 71.05 190 | 81.78 259 |
|
mPP-MVS | | | 82.96 74 | 82.44 71 | 84.52 108 | 92.83 54 | 62.92 185 | 92.76 81 | 91.85 110 | 71.52 165 | 75.61 106 | 94.24 62 | 53.48 162 | 96.99 70 | 78.97 78 | 90.73 64 | 93.64 97 |
|
ACMMP | | | 81.49 92 | 80.67 90 | 83.93 117 | 91.71 77 | 62.90 186 | 92.13 101 | 92.22 97 | 71.79 157 | 71.68 144 | 93.49 73 | 50.32 184 | 96.96 72 | 78.47 80 | 84.22 116 | 91.93 136 |
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 |
HPM-MVS | | | 83.25 68 | 82.95 63 | 84.17 113 | 92.25 66 | 62.88 187 | 90.91 152 | 91.86 109 | 70.30 181 | 77.12 95 | 93.96 66 | 56.75 112 | 96.28 91 | 82.04 57 | 91.34 60 | 93.34 103 |
|
MVS_111021_LR | | | 82.02 88 | 81.52 81 | 83.51 127 | 88.42 138 | 62.88 187 | 89.77 178 | 88.93 211 | 76.78 73 | 75.55 107 | 93.10 76 | 50.31 185 | 95.38 123 | 83.82 48 | 87.02 86 | 92.26 133 |
|
IterMVS-LS | | | 76.49 168 | 75.18 164 | 80.43 189 | 84.49 189 | 62.74 189 | 90.64 160 | 88.80 213 | 72.40 138 | 65.16 204 | 81.72 208 | 60.98 80 | 92.27 217 | 67.74 157 | 64.65 234 | 86.29 200 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 78.97 131 | 78.22 123 | 81.25 176 | 85.33 178 | 62.73 190 | 89.53 182 | 93.21 59 | 72.39 139 | 72.14 139 | 90.13 123 | 60.99 79 | 94.72 135 | 67.73 158 | 72.49 180 | 86.29 200 |
|
CHOSEN 280x420 | | | 77.35 156 | 76.95 142 | 78.55 225 | 87.07 158 | 62.68 191 | 69.71 301 | 82.95 282 | 68.80 195 | 71.48 145 | 87.27 150 | 66.03 39 | 84.00 290 | 76.47 92 | 82.81 122 | 88.95 164 |
|
HQP_MVS | | | 80.34 107 | 79.75 101 | 82.12 161 | 86.94 159 | 62.42 192 | 93.13 71 | 91.31 130 | 78.81 49 | 72.53 132 | 89.14 131 | 50.66 182 | 95.55 119 | 76.74 89 | 78.53 145 | 88.39 172 |
|
plane_prior | | | | | | | 62.42 192 | 93.85 54 | | 79.38 37 | | | | | | 78.80 144 | |
|
plane_prior6 | | | | | | 87.23 155 | 62.32 194 | | | | | | 50.66 182 | | | | |
|
PVSNet_0 | | 68.08 15 | 71.81 212 | 68.32 226 | 82.27 155 | 84.68 185 | 62.31 195 | 88.68 194 | 90.31 161 | 75.84 82 | 57.93 246 | 80.65 224 | 37.85 260 | 94.19 160 | 69.94 141 | 29.05 319 | 90.31 155 |
|
WR-MVS | | | 76.76 166 | 75.74 155 | 79.82 202 | 84.60 186 | 62.27 196 | 92.60 89 | 92.51 87 | 76.06 80 | 67.87 188 | 85.34 165 | 56.76 111 | 90.24 244 | 62.20 209 | 63.69 241 | 86.94 192 |
|
NR-MVSNet | | | 76.05 174 | 74.59 168 | 80.44 188 | 82.96 208 | 62.18 197 | 90.83 154 | 91.73 113 | 77.12 67 | 60.96 230 | 86.35 155 | 59.28 88 | 91.80 225 | 60.74 216 | 61.34 254 | 87.35 184 |
|
plane_prior3 | | | | | | | 61.95 198 | | | 79.09 44 | 72.53 132 | | | | | | |
|
Vis-MVSNet | | | 80.92 100 | 79.98 98 | 83.74 120 | 88.48 135 | 61.80 199 | 93.44 65 | 88.26 226 | 73.96 113 | 77.73 85 | 91.76 104 | 49.94 189 | 94.76 132 | 65.84 177 | 90.37 69 | 94.65 61 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
CNLPA | | | 74.31 194 | 72.30 197 | 80.32 190 | 91.49 84 | 61.66 200 | 90.85 153 | 80.72 289 | 56.67 277 | 63.85 215 | 90.64 114 | 46.75 216 | 90.84 238 | 53.79 243 | 75.99 162 | 88.47 171 |
|
test222 | | | | | | 89.77 109 | 61.60 201 | 89.55 180 | 89.42 194 | 56.83 276 | 77.28 93 | 92.43 94 | 52.76 167 | | | 91.14 62 | 93.09 112 |
|
plane_prior7 | | | | | | 86.94 159 | 61.51 202 | | | | | | | | | | |
|
UGNet | | | 79.87 117 | 78.68 117 | 83.45 129 | 89.96 105 | 61.51 202 | 92.13 101 | 90.79 144 | 76.83 72 | 78.85 78 | 86.33 157 | 38.16 255 | 96.17 94 | 67.93 156 | 87.17 85 | 92.67 122 |
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 |
test-LLR | | | 80.10 112 | 79.56 104 | 81.72 169 | 86.93 161 | 61.17 204 | 92.70 84 | 91.54 120 | 71.51 166 | 75.62 104 | 86.94 151 | 53.83 154 | 92.38 213 | 72.21 120 | 84.76 106 | 91.60 139 |
|
test-mter | | | 79.96 115 | 79.38 110 | 81.72 169 | 86.93 161 | 61.17 204 | 92.70 84 | 91.54 120 | 73.85 115 | 75.62 104 | 86.94 151 | 49.84 191 | 92.38 213 | 72.21 120 | 84.76 106 | 91.60 139 |
|
TR-MVS | | | 78.77 136 | 77.37 137 | 82.95 133 | 90.49 98 | 60.88 206 | 93.67 57 | 90.07 172 | 70.08 183 | 74.51 114 | 91.37 108 | 45.69 224 | 95.70 115 | 60.12 220 | 80.32 133 | 92.29 131 |
|
UniMVSNet (Re) | | | 77.58 150 | 76.78 143 | 79.98 197 | 84.11 196 | 60.80 207 | 91.76 125 | 93.17 64 | 76.56 77 | 69.93 159 | 84.78 172 | 63.32 65 | 92.36 215 | 64.89 184 | 62.51 244 | 86.78 194 |
|
1112_ss | | | 80.56 103 | 79.83 100 | 82.77 135 | 88.65 131 | 60.78 208 | 92.29 97 | 88.36 222 | 72.58 136 | 72.46 135 | 94.95 39 | 65.09 47 | 93.42 186 | 66.38 170 | 77.71 150 | 94.10 82 |
|
v7n | | | 71.31 217 | 68.65 220 | 79.28 215 | 76.40 280 | 60.77 209 | 86.71 227 | 89.45 192 | 64.17 233 | 58.77 242 | 78.24 244 | 44.59 232 | 93.54 183 | 57.76 230 | 61.75 250 | 83.52 240 |
|
test_0402 | | | 64.54 261 | 61.09 266 | 74.92 256 | 84.10 197 | 60.75 210 | 87.95 204 | 79.71 292 | 52.03 287 | 52.41 274 | 77.20 252 | 32.21 284 | 91.64 228 | 23.14 318 | 61.03 255 | 72.36 305 |
|
旧先验1 | | | | | | 91.94 70 | 60.74 211 | | 91.50 123 | | | 94.36 54 | 65.23 45 | | | 91.84 50 | 94.55 63 |
|
ADS-MVSNet2 | | | 66.90 251 | 63.44 254 | 77.26 244 | 88.06 144 | 60.70 212 | 68.01 305 | 75.56 302 | 57.57 269 | 64.48 209 | 69.87 292 | 38.68 249 | 84.10 286 | 40.87 284 | 67.89 208 | 86.97 189 |
|
semantic-postprocess | | | | | 76.32 249 | 81.48 217 | 60.67 213 | | 85.99 258 | 66.17 220 | 59.50 235 | 78.88 242 | 45.51 226 | 83.65 292 | 62.58 207 | 61.93 247 | 84.63 232 |
|
TranMVSNet+NR-MVSNet | | | 75.86 177 | 74.52 171 | 79.89 200 | 82.44 211 | 60.64 214 | 91.37 140 | 91.37 129 | 76.63 75 | 67.65 190 | 86.21 158 | 52.37 172 | 91.55 230 | 61.84 211 | 60.81 257 | 87.48 181 |
|
pmmvs5 | | | 73.35 201 | 71.52 203 | 78.86 223 | 78.64 268 | 60.61 215 | 91.08 150 | 86.90 240 | 67.69 208 | 63.32 219 | 83.64 180 | 44.33 233 | 90.53 239 | 62.04 210 | 66.02 217 | 85.46 222 |
|
MDA-MVSNet_test_wron | | | 63.78 267 | 60.16 268 | 74.64 257 | 78.15 271 | 60.41 216 | 83.49 244 | 84.03 270 | 56.17 280 | 39.17 311 | 71.59 289 | 37.22 265 | 83.24 297 | 42.87 278 | 48.73 297 | 80.26 281 |
|
Test_1112_low_res | | | 79.56 123 | 78.60 119 | 82.43 146 | 88.24 141 | 60.39 217 | 92.09 104 | 87.99 229 | 72.10 147 | 71.84 142 | 87.42 148 | 64.62 51 | 93.04 190 | 65.80 178 | 77.30 156 | 93.85 94 |
|
LP | | | 56.71 282 | 51.64 286 | 71.91 280 | 80.08 250 | 60.33 218 | 61.72 313 | 75.61 301 | 43.87 310 | 43.76 303 | 60.30 309 | 30.46 292 | 84.05 287 | 22.94 319 | 46.06 302 | 71.34 307 |
|
UniMVSNet_NR-MVSNet | | | 78.15 145 | 77.55 133 | 79.98 197 | 84.46 190 | 60.26 219 | 92.25 98 | 93.20 61 | 77.50 64 | 68.88 175 | 86.61 153 | 66.10 38 | 92.13 219 | 66.38 170 | 62.55 242 | 87.54 179 |
|
DU-MVS | | | 76.86 163 | 75.84 153 | 79.91 199 | 82.96 208 | 60.26 219 | 91.26 144 | 91.54 120 | 76.46 78 | 68.88 175 | 86.35 155 | 56.16 119 | 92.13 219 | 66.38 170 | 62.55 242 | 87.35 184 |
|
EPP-MVSNet | | | 81.79 90 | 81.52 81 | 82.61 141 | 88.77 130 | 60.21 221 | 93.02 74 | 93.66 41 | 68.52 201 | 72.90 126 | 90.39 119 | 72.19 11 | 94.96 127 | 74.93 105 | 79.29 140 | 92.67 122 |
|
YYNet1 | | | 63.76 268 | 60.14 269 | 74.62 258 | 78.06 272 | 60.19 222 | 83.46 246 | 83.99 274 | 56.18 279 | 39.25 310 | 71.56 290 | 37.18 266 | 83.34 295 | 42.90 277 | 48.70 298 | 80.32 280 |
|
v52 | | | 69.80 232 | 67.01 235 | 78.15 231 | 71.84 294 | 60.10 223 | 82.02 256 | 87.39 234 | 64.48 230 | 57.80 247 | 75.97 261 | 41.47 242 | 92.90 198 | 63.00 200 | 59.13 263 | 81.45 263 |
|
V4 | | | 69.80 232 | 67.02 234 | 78.15 231 | 71.86 293 | 60.10 223 | 82.02 256 | 87.39 234 | 64.48 230 | 57.78 248 | 75.98 260 | 41.49 241 | 92.90 198 | 63.00 200 | 59.16 262 | 81.44 264 |
|
IS-MVSNet | | | 80.14 111 | 79.41 108 | 82.33 152 | 87.91 147 | 60.08 225 | 91.97 110 | 88.27 225 | 72.90 132 | 71.44 146 | 91.73 106 | 61.44 78 | 93.66 182 | 62.47 208 | 86.53 93 | 93.24 107 |
|
HPM-MVS_fast | | | 80.25 108 | 79.55 106 | 82.33 152 | 91.55 81 | 59.95 226 | 91.32 143 | 89.16 203 | 65.23 228 | 74.71 113 | 93.07 80 | 47.81 211 | 95.74 109 | 74.87 108 | 88.23 78 | 91.31 146 |
|
MDTV_nov1_ep13_2view | | | | | | | 59.90 227 | 80.13 274 | | 67.65 210 | 72.79 127 | | 54.33 149 | | 59.83 221 | | 92.58 125 |
|
CPTT-MVS | | | 79.59 122 | 79.16 114 | 80.89 186 | 91.54 82 | 59.80 228 | 92.10 103 | 88.54 220 | 60.42 257 | 72.96 124 | 93.28 75 | 48.27 205 | 92.80 200 | 78.89 79 | 86.50 94 | 90.06 156 |
|
ACMP | | 71.68 10 | 75.58 182 | 74.23 175 | 79.62 206 | 84.97 183 | 59.64 229 | 90.80 155 | 89.07 208 | 70.39 180 | 62.95 222 | 87.30 149 | 38.28 254 | 93.87 176 | 72.89 112 | 71.45 187 | 85.36 224 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
pmmvs-eth3d | | | 65.53 258 | 62.32 262 | 75.19 254 | 69.39 303 | 59.59 230 | 82.80 253 | 83.43 276 | 62.52 245 | 51.30 280 | 72.49 274 | 32.86 279 | 87.16 277 | 55.32 238 | 50.73 289 | 78.83 291 |
|
sss | | | 82.71 77 | 82.38 72 | 83.73 122 | 89.25 118 | 59.58 231 | 92.24 99 | 94.89 11 | 77.96 57 | 79.86 66 | 92.38 95 | 56.70 113 | 97.05 62 | 77.26 88 | 80.86 132 | 94.55 63 |
|
Fast-Effi-MVS+-dtu | | | 75.04 187 | 73.37 186 | 80.07 195 | 80.86 223 | 59.52 232 | 91.20 148 | 85.38 263 | 71.90 149 | 65.20 203 | 84.84 171 | 41.46 243 | 92.97 192 | 66.50 169 | 72.96 176 | 87.73 178 |
|
FIs | | | 79.47 124 | 79.41 108 | 79.67 204 | 85.95 173 | 59.40 233 | 91.68 129 | 93.94 32 | 78.06 56 | 68.96 174 | 88.28 138 | 66.61 34 | 91.77 226 | 66.20 173 | 74.99 165 | 87.82 177 |
|
LPG-MVS_test | | | 75.82 178 | 74.58 169 | 79.56 208 | 84.31 193 | 59.37 234 | 90.44 164 | 89.73 185 | 69.49 187 | 64.86 205 | 88.42 135 | 38.65 251 | 94.30 155 | 72.56 116 | 72.76 177 | 85.01 227 |
|
LGP-MVS_train | | | | | 79.56 208 | 84.31 193 | 59.37 234 | | 89.73 185 | 69.49 187 | 64.86 205 | 88.42 135 | 38.65 251 | 94.30 155 | 72.56 116 | 72.76 177 | 85.01 227 |
|
Baseline_NR-MVSNet | | | 73.99 197 | 72.83 190 | 77.48 239 | 80.78 224 | 59.29 236 | 91.79 122 | 84.55 267 | 68.85 194 | 68.99 173 | 80.70 222 | 56.16 119 | 92.04 222 | 62.67 206 | 60.98 256 | 81.11 272 |
|
PS-MVSNAJss | | | 77.26 160 | 76.31 147 | 80.13 194 | 80.64 233 | 59.16 237 | 90.63 162 | 91.06 138 | 72.80 133 | 68.58 180 | 84.57 175 | 53.55 158 | 93.96 172 | 72.97 111 | 71.96 183 | 87.27 187 |
|
TransMVSNet (Re) | | | 70.07 229 | 67.66 231 | 77.31 243 | 80.62 235 | 59.13 238 | 91.78 124 | 84.94 265 | 65.97 221 | 60.08 233 | 80.44 226 | 50.78 181 | 91.87 224 | 48.84 260 | 45.46 303 | 80.94 274 |
|
Patchmatch-test | | | 65.86 256 | 60.94 267 | 80.62 187 | 83.75 199 | 58.83 239 | 58.91 318 | 75.26 304 | 44.50 308 | 50.95 282 | 77.09 256 | 58.81 93 | 87.90 272 | 35.13 300 | 64.03 237 | 95.12 47 |
|
v748 | | | 70.55 227 | 67.97 230 | 78.27 228 | 75.75 285 | 58.78 240 | 86.29 231 | 89.25 199 | 65.12 229 | 56.66 253 | 77.17 254 | 45.05 230 | 92.95 193 | 58.13 229 | 58.33 268 | 83.10 249 |
|
APD-MVS_3200maxsize | | | 81.64 91 | 81.32 83 | 82.59 142 | 92.36 61 | 58.74 241 | 91.39 137 | 91.01 141 | 63.35 237 | 79.72 68 | 94.62 50 | 51.82 174 | 96.14 95 | 79.71 70 | 87.93 81 | 92.89 119 |
|
PLC | | 68.80 14 | 75.23 185 | 73.68 182 | 79.86 201 | 92.93 52 | 58.68 242 | 90.64 160 | 88.30 223 | 60.90 254 | 64.43 211 | 90.53 117 | 42.38 238 | 94.57 138 | 56.52 233 | 76.54 159 | 86.33 199 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
abl_6 | | | 79.82 118 | 79.20 113 | 81.70 171 | 89.85 107 | 58.34 243 | 88.47 197 | 90.07 172 | 62.56 244 | 77.71 86 | 93.08 78 | 47.65 212 | 96.78 81 | 77.94 85 | 85.45 102 | 89.99 158 |
|
DeepPCF-MVS | | 81.17 1 | 89.72 5 | 91.38 3 | 84.72 103 | 93.00 51 | 58.16 244 | 96.72 3 | 94.41 26 | 86.50 5 | 90.25 4 | 97.83 1 | 75.46 5 | 98.67 11 | 92.78 2 | 95.49 7 | 97.32 1 |
|
FMVSNet5 | | | 68.04 244 | 65.66 240 | 75.18 255 | 84.43 191 | 57.89 245 | 83.54 243 | 86.26 253 | 61.83 251 | 53.64 270 | 73.30 269 | 37.15 267 | 85.08 282 | 48.99 259 | 61.77 249 | 82.56 255 |
|
ACMM | | 69.62 13 | 74.34 193 | 72.73 191 | 79.17 220 | 84.25 195 | 57.87 246 | 90.36 167 | 89.93 177 | 63.17 240 | 65.64 202 | 86.04 161 | 37.79 261 | 94.10 162 | 65.89 176 | 71.52 186 | 85.55 221 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
OpenMVS_ROB | | 61.12 18 | 66.39 253 | 62.92 258 | 76.80 247 | 76.51 279 | 57.77 247 | 89.22 185 | 83.41 277 | 55.48 281 | 53.86 268 | 77.84 247 | 26.28 302 | 93.95 173 | 34.90 301 | 68.76 201 | 78.68 292 |
|
UA-Net | | | 80.02 114 | 79.65 102 | 81.11 179 | 89.33 116 | 57.72 248 | 86.33 230 | 89.00 210 | 77.44 65 | 81.01 56 | 89.15 130 | 59.33 87 | 95.90 103 | 61.01 215 | 84.28 113 | 89.73 161 |
|
testdata | | | | | 81.34 175 | 89.02 124 | 57.72 248 | | 89.84 179 | 58.65 267 | 85.32 29 | 94.09 63 | 57.03 106 | 93.28 187 | 69.34 147 | 90.56 68 | 93.03 114 |
|
pm-mvs1 | | | 72.89 204 | 71.09 206 | 78.26 229 | 79.10 264 | 57.62 250 | 90.80 155 | 89.30 197 | 67.66 209 | 62.91 223 | 81.78 207 | 49.11 200 | 92.95 193 | 60.29 219 | 58.89 266 | 84.22 233 |
|
XVG-OURS | | | 74.25 195 | 72.46 196 | 79.63 205 | 78.45 269 | 57.59 251 | 80.33 270 | 87.39 234 | 63.86 235 | 68.76 177 | 89.62 129 | 40.50 246 | 91.72 227 | 69.00 149 | 74.25 167 | 89.58 162 |
|
OMC-MVS | | | 78.67 139 | 77.91 129 | 80.95 185 | 85.76 177 | 57.40 252 | 88.49 196 | 88.67 215 | 73.85 115 | 72.43 136 | 92.10 99 | 49.29 196 | 94.55 140 | 72.73 115 | 77.89 149 | 90.91 149 |
|
XVG-OURS-SEG-HR | | | 74.70 192 | 73.08 188 | 79.57 207 | 78.25 270 | 57.33 253 | 80.49 268 | 87.32 237 | 63.22 239 | 68.76 177 | 90.12 125 | 44.89 231 | 91.59 229 | 70.55 138 | 74.09 169 | 89.79 159 |
|
mvs-test1 | | | 78.74 137 | 77.95 127 | 81.14 178 | 83.22 204 | 57.13 254 | 93.96 47 | 87.78 231 | 75.42 87 | 72.68 128 | 90.80 113 | 45.08 228 | 94.54 141 | 75.08 102 | 77.49 153 | 91.74 138 |
|
ACMH+ | | 65.35 16 | 67.65 246 | 64.55 247 | 76.96 245 | 84.59 187 | 57.10 255 | 88.08 202 | 80.79 288 | 58.59 268 | 53.00 273 | 81.09 220 | 26.63 301 | 92.95 193 | 46.51 267 | 61.69 252 | 80.82 275 |
|
MDA-MVSNet-bldmvs | | | 61.54 273 | 57.70 274 | 73.05 268 | 79.53 256 | 57.00 256 | 83.08 251 | 81.23 285 | 57.57 269 | 34.91 314 | 72.45 276 | 32.79 280 | 86.26 281 | 35.81 298 | 41.95 307 | 75.89 300 |
|
MVS-HIRNet | | | 60.25 275 | 55.55 281 | 74.35 260 | 84.37 192 | 56.57 257 | 71.64 296 | 74.11 306 | 34.44 317 | 45.54 297 | 42.24 319 | 31.11 290 | 89.81 258 | 40.36 287 | 76.10 161 | 76.67 299 |
|
PMMVS | | | 81.98 89 | 82.04 75 | 81.78 167 | 89.76 110 | 56.17 258 | 91.13 149 | 90.69 147 | 77.96 57 | 80.09 64 | 93.57 71 | 46.33 221 | 94.99 126 | 81.41 63 | 87.46 84 | 94.17 77 |
|
LS3D | | | 69.17 237 | 66.40 237 | 77.50 238 | 91.92 71 | 56.12 259 | 85.12 234 | 80.37 290 | 46.96 299 | 56.50 254 | 87.51 147 | 37.25 264 | 93.71 180 | 32.52 308 | 79.40 137 | 82.68 254 |
|
F-COLMAP | | | 70.66 225 | 68.44 224 | 77.32 242 | 86.37 167 | 55.91 260 | 88.00 203 | 86.32 251 | 56.94 275 | 57.28 251 | 88.07 142 | 33.58 278 | 92.49 210 | 51.02 252 | 68.37 204 | 83.55 238 |
|
PatchMatch-RL | | | 72.06 210 | 69.98 209 | 78.28 227 | 89.51 115 | 55.70 261 | 83.49 244 | 83.39 278 | 61.24 253 | 63.72 216 | 82.76 188 | 34.77 276 | 93.03 191 | 53.37 247 | 77.59 152 | 86.12 204 |
|
FC-MVSNet-test | | | 77.99 146 | 78.08 125 | 77.70 235 | 84.89 184 | 55.51 262 | 90.27 169 | 93.75 38 | 76.87 70 | 66.80 200 | 87.59 145 | 65.71 43 | 90.23 245 | 62.89 203 | 73.94 170 | 87.37 182 |
|
USDC | | | 67.43 250 | 64.51 248 | 76.19 250 | 77.94 273 | 55.29 263 | 78.38 284 | 85.00 264 | 73.17 126 | 48.36 288 | 80.37 227 | 21.23 309 | 92.48 211 | 52.15 249 | 64.02 238 | 80.81 276 |
|
Effi-MVS+-dtu | | | 76.14 172 | 75.28 163 | 78.72 224 | 83.22 204 | 55.17 264 | 89.87 176 | 87.78 231 | 75.42 87 | 67.98 185 | 81.43 210 | 45.08 228 | 92.52 209 | 75.08 102 | 71.63 184 | 88.48 170 |
|
jajsoiax | | | 73.05 202 | 71.51 204 | 77.67 236 | 77.46 275 | 54.83 265 | 88.81 192 | 90.04 174 | 69.13 191 | 62.85 224 | 83.51 182 | 31.16 289 | 92.75 202 | 70.83 134 | 69.80 192 | 85.43 223 |
|
anonymousdsp | | | 71.14 220 | 69.37 215 | 76.45 248 | 72.95 290 | 54.71 266 | 84.19 239 | 88.88 212 | 61.92 249 | 62.15 228 | 79.77 235 | 38.14 256 | 91.44 236 | 68.90 151 | 67.45 211 | 83.21 246 |
|
mvs_tets | | | 72.71 207 | 71.11 205 | 77.52 237 | 77.41 276 | 54.52 267 | 88.45 198 | 89.76 181 | 68.76 196 | 62.70 225 | 83.26 185 | 29.49 293 | 92.71 203 | 70.51 139 | 69.62 194 | 85.34 225 |
|
JIA-IIPM | | | 66.06 255 | 62.45 261 | 76.88 246 | 81.42 220 | 54.45 268 | 57.49 319 | 88.67 215 | 49.36 294 | 63.86 214 | 46.86 315 | 56.06 122 | 90.25 242 | 49.53 258 | 68.83 200 | 85.95 211 |
|
Patchmatch-RL test | | | 68.17 243 | 64.49 249 | 79.19 219 | 71.22 296 | 53.93 269 | 70.07 300 | 71.54 313 | 69.22 190 | 56.79 252 | 62.89 305 | 56.58 116 | 88.61 265 | 69.53 145 | 52.61 282 | 95.03 52 |
|
test_djsdf | | | 73.76 200 | 72.56 194 | 77.39 241 | 77.00 278 | 53.93 269 | 89.07 190 | 90.69 147 | 65.80 222 | 63.92 213 | 82.03 203 | 43.14 236 | 92.67 205 | 72.83 113 | 68.53 203 | 85.57 220 |
|
pmmvs6 | | | 67.57 247 | 64.76 246 | 76.00 252 | 72.82 292 | 53.37 271 | 88.71 193 | 86.78 241 | 53.19 284 | 57.58 250 | 78.03 246 | 35.33 274 | 92.41 212 | 55.56 237 | 54.88 278 | 82.21 256 |
|
TinyColmap | | | 60.32 274 | 56.42 280 | 72.00 279 | 78.78 265 | 53.18 272 | 78.36 285 | 75.64 300 | 52.30 286 | 41.59 309 | 75.82 263 | 14.76 318 | 88.35 269 | 35.84 297 | 54.71 279 | 74.46 302 |
|
COLMAP_ROB | | 57.96 20 | 62.98 270 | 59.65 270 | 72.98 269 | 81.44 219 | 53.00 273 | 83.75 241 | 75.53 303 | 48.34 297 | 48.81 287 | 81.40 212 | 24.14 303 | 90.30 241 | 32.95 305 | 60.52 259 | 75.65 301 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
XVG-ACMP-BASELINE | | | 68.04 244 | 65.53 241 | 75.56 253 | 74.06 289 | 52.37 274 | 78.43 283 | 85.88 260 | 62.03 247 | 58.91 241 | 81.21 218 | 20.38 310 | 91.15 237 | 60.69 217 | 68.18 205 | 83.16 247 |
|
Vis-MVSNet (Re-imp) | | | 79.24 127 | 79.57 103 | 78.24 230 | 88.46 136 | 52.29 275 | 90.41 166 | 89.12 205 | 74.24 105 | 69.13 170 | 91.91 101 | 65.77 42 | 90.09 251 | 59.00 226 | 88.09 80 | 92.33 129 |
|
TAPA-MVS | | 70.22 12 | 74.94 190 | 73.53 183 | 79.17 220 | 90.40 100 | 52.07 276 | 89.19 187 | 89.61 189 | 62.69 243 | 70.07 154 | 92.67 90 | 48.89 202 | 94.32 154 | 38.26 293 | 79.97 134 | 91.12 147 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
UnsupCasMVSNet_bld | | | 61.60 272 | 57.71 273 | 73.29 267 | 68.73 305 | 51.64 277 | 78.61 282 | 89.05 209 | 57.20 273 | 46.11 292 | 61.96 307 | 28.70 295 | 88.60 266 | 50.08 256 | 38.90 312 | 79.63 286 |
|
LTVRE_ROB | | 59.60 19 | 66.27 254 | 63.54 253 | 74.45 259 | 84.00 198 | 51.55 278 | 67.08 308 | 83.53 275 | 58.78 266 | 54.94 258 | 80.31 228 | 34.54 277 | 93.23 188 | 40.64 286 | 68.03 206 | 78.58 293 |
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 |
WR-MVS_H | | | 70.59 226 | 69.94 210 | 72.53 272 | 81.03 221 | 51.43 279 | 87.35 217 | 92.03 103 | 67.38 213 | 60.23 232 | 80.70 222 | 55.84 126 | 83.45 294 | 46.33 268 | 58.58 267 | 82.72 253 |
|
AllTest | | | 61.66 271 | 58.06 272 | 72.46 273 | 79.57 254 | 51.42 280 | 80.17 273 | 68.61 316 | 51.25 289 | 45.88 293 | 81.23 214 | 19.86 311 | 86.58 279 | 38.98 290 | 57.01 271 | 79.39 287 |
|
TestCases | | | | | 72.46 273 | 79.57 254 | 51.42 280 | | 68.61 316 | 51.25 289 | 45.88 293 | 81.23 214 | 19.86 311 | 86.58 279 | 38.98 290 | 57.01 271 | 79.39 287 |
|
CP-MVSNet | | | 70.50 228 | 69.91 211 | 72.26 275 | 80.71 227 | 51.00 282 | 87.23 218 | 90.30 162 | 67.84 207 | 59.64 234 | 82.69 189 | 50.23 187 | 82.30 301 | 51.28 251 | 59.28 261 | 83.46 242 |
|
pmmvs3 | | | 55.51 285 | 51.50 288 | 67.53 289 | 57.90 318 | 50.93 283 | 80.37 269 | 73.66 307 | 40.63 314 | 44.15 302 | 64.75 303 | 16.30 314 | 78.97 312 | 44.77 273 | 40.98 310 | 72.69 304 |
|
PS-CasMVS | | | 69.86 231 | 69.13 218 | 72.07 278 | 80.35 241 | 50.57 284 | 87.02 221 | 89.75 182 | 67.27 214 | 59.19 237 | 82.28 195 | 46.58 219 | 82.24 302 | 50.69 253 | 59.02 264 | 83.39 244 |
|
CMPMVS | | 48.56 21 | 66.77 252 | 64.41 250 | 73.84 263 | 70.65 299 | 50.31 285 | 77.79 288 | 85.73 262 | 45.54 304 | 44.76 299 | 82.14 199 | 35.40 273 | 90.14 248 | 63.18 198 | 74.54 166 | 81.07 273 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
UnsupCasMVSNet_eth | | | 65.79 257 | 63.10 256 | 73.88 262 | 70.71 298 | 50.29 286 | 81.09 264 | 89.88 178 | 72.58 136 | 49.25 286 | 74.77 267 | 32.57 282 | 87.43 275 | 55.96 236 | 41.04 309 | 83.90 236 |
|
SixPastTwentyTwo | | | 64.92 259 | 61.78 265 | 74.34 261 | 78.74 266 | 49.76 287 | 83.42 247 | 79.51 293 | 62.86 242 | 50.27 283 | 77.35 249 | 30.92 291 | 90.49 240 | 45.89 270 | 47.06 300 | 82.78 250 |
|
PEN-MVS | | | 69.46 236 | 68.56 222 | 72.17 277 | 79.27 259 | 49.71 288 | 86.90 224 | 89.24 200 | 67.24 215 | 59.08 238 | 82.51 190 | 47.23 214 | 83.54 293 | 48.42 262 | 57.12 269 | 83.25 245 |
|
EPNet_dtu | | | 78.80 135 | 79.26 112 | 77.43 240 | 88.06 144 | 49.71 288 | 91.96 111 | 91.95 107 | 77.67 61 | 76.56 100 | 91.28 109 | 58.51 94 | 90.20 246 | 56.37 234 | 80.95 131 | 92.39 128 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
K. test v3 | | | 63.09 269 | 59.61 271 | 73.53 265 | 76.26 281 | 49.38 290 | 83.27 248 | 77.15 296 | 64.35 232 | 47.77 289 | 72.32 279 | 28.73 294 | 87.79 274 | 49.93 257 | 36.69 314 | 83.41 243 |
|
DTE-MVSNet | | | 68.46 242 | 67.33 232 | 71.87 281 | 77.94 273 | 49.00 291 | 86.16 232 | 88.58 219 | 66.36 219 | 58.19 244 | 82.21 198 | 46.36 220 | 83.87 291 | 44.97 272 | 55.17 276 | 82.73 252 |
|
LCM-MVSNet-Re | | | 72.93 203 | 71.84 200 | 76.18 251 | 88.49 134 | 48.02 292 | 80.07 275 | 70.17 314 | 73.96 113 | 52.25 275 | 80.09 233 | 49.98 188 | 88.24 270 | 67.35 160 | 84.23 114 | 92.28 132 |
|
test0.0.03 1 | | | 72.76 206 | 72.71 192 | 72.88 270 | 80.25 247 | 47.99 293 | 91.22 146 | 89.45 192 | 71.51 166 | 62.51 227 | 87.66 144 | 53.83 154 | 85.06 283 | 50.16 255 | 67.84 210 | 85.58 219 |
|
lessismore_v0 | | | | | 73.72 264 | 72.93 291 | 47.83 294 | | 61.72 324 | | 45.86 295 | 73.76 268 | 28.63 296 | 89.81 258 | 47.75 265 | 31.37 318 | 83.53 239 |
|
Anonymous20231206 | | | 67.53 248 | 65.78 238 | 72.79 271 | 74.95 286 | 47.59 295 | 88.23 200 | 87.32 237 | 61.75 252 | 58.07 245 | 77.29 251 | 37.79 261 | 87.29 276 | 42.91 276 | 63.71 240 | 83.48 241 |
|
OurMVSNet-221017-0 | | | 64.68 260 | 62.17 263 | 72.21 276 | 76.08 283 | 47.35 296 | 80.67 267 | 81.02 287 | 56.19 278 | 51.60 277 | 79.66 237 | 27.05 300 | 88.56 267 | 53.60 245 | 53.63 281 | 80.71 277 |
|
ITE_SJBPF | | | | | 70.43 283 | 74.44 287 | 47.06 297 | | 77.32 295 | 60.16 259 | 54.04 266 | 83.53 181 | 23.30 306 | 84.01 289 | 43.07 275 | 61.58 253 | 80.21 282 |
|
TDRefinement | | | 55.28 286 | 51.58 287 | 66.39 293 | 59.53 317 | 46.15 298 | 76.23 290 | 72.80 308 | 44.60 307 | 42.49 305 | 76.28 259 | 15.29 316 | 82.39 300 | 33.20 304 | 43.75 305 | 70.62 309 |
|
RPSCF | | | 64.24 263 | 61.98 264 | 71.01 282 | 76.10 282 | 45.00 299 | 75.83 291 | 75.94 299 | 46.94 300 | 58.96 240 | 84.59 174 | 31.40 288 | 82.00 303 | 47.76 264 | 60.33 260 | 86.04 210 |
|
new-patchmatchnet | | | 59.30 279 | 56.48 279 | 67.79 288 | 65.86 307 | 44.19 300 | 82.47 254 | 81.77 283 | 59.94 260 | 43.65 304 | 66.20 298 | 27.67 297 | 81.68 304 | 39.34 289 | 41.40 308 | 77.50 297 |
|
MIMVSNet1 | | | 60.16 276 | 57.33 276 | 68.67 286 | 69.71 302 | 44.13 301 | 78.92 281 | 84.21 268 | 55.05 282 | 44.63 300 | 71.85 283 | 23.91 304 | 81.54 305 | 32.63 307 | 55.03 277 | 80.35 279 |
|
CVMVSNet | | | 74.04 196 | 74.27 174 | 73.33 266 | 85.33 178 | 43.94 302 | 89.53 182 | 88.39 221 | 54.33 283 | 70.37 150 | 90.13 123 | 49.17 198 | 84.05 287 | 61.83 212 | 79.36 138 | 91.99 135 |
|
testpf | | | 57.17 280 | 56.93 277 | 57.88 302 | 79.13 263 | 42.40 303 | 34.23 325 | 85.97 259 | 52.64 285 | 47.66 291 | 66.50 296 | 36.33 272 | 79.65 309 | 53.60 245 | 56.31 274 | 51.60 318 |
|
Anonymous20231211 | | | 53.57 288 | 49.43 290 | 66.00 294 | 65.01 308 | 42.08 304 | 80.95 266 | 72.60 309 | 38.46 315 | 41.65 308 | 64.48 304 | 15.72 315 | 84.23 285 | 25.78 315 | 40.24 311 | 71.68 306 |
|
no-one | | | 44.13 295 | 38.39 296 | 61.34 299 | 45.91 326 | 41.94 305 | 61.67 314 | 75.07 305 | 45.05 306 | 20.07 320 | 40.68 322 | 11.58 321 | 79.82 308 | 30.18 311 | 15.30 322 | 62.26 315 |
|
PM-MVS | | | 59.40 277 | 56.59 278 | 67.84 287 | 63.63 309 | 41.86 306 | 76.76 289 | 63.22 322 | 59.01 265 | 51.07 281 | 72.27 280 | 11.72 320 | 83.25 296 | 61.34 213 | 50.28 295 | 78.39 294 |
|
ambc | | | | | 69.61 284 | 61.38 315 | 41.35 307 | 49.07 322 | 85.86 261 | | 50.18 285 | 66.40 297 | 10.16 323 | 88.14 271 | 45.73 271 | 44.20 304 | 79.32 289 |
|
new_pmnet | | | 49.31 290 | 46.44 292 | 57.93 301 | 62.84 311 | 40.74 308 | 68.47 304 | 62.96 323 | 36.48 316 | 35.09 313 | 57.81 311 | 14.97 317 | 72.18 317 | 32.86 306 | 46.44 301 | 60.88 316 |
|
testgi | | | 64.48 262 | 62.87 259 | 69.31 285 | 71.24 295 | 40.62 309 | 85.49 233 | 79.92 291 | 65.36 226 | 54.18 265 | 83.49 183 | 23.74 305 | 84.55 284 | 41.60 281 | 60.79 258 | 82.77 251 |
|
test20.03 | | | 63.83 266 | 62.65 260 | 67.38 290 | 70.58 300 | 39.94 310 | 86.57 229 | 84.17 269 | 63.29 238 | 51.86 276 | 77.30 250 | 37.09 268 | 82.47 299 | 38.87 292 | 54.13 280 | 79.73 285 |
|
LF4IMVS | | | 54.01 287 | 52.12 285 | 59.69 300 | 62.41 312 | 39.91 311 | 68.59 303 | 68.28 318 | 42.96 311 | 44.55 301 | 75.18 264 | 14.09 319 | 68.39 320 | 41.36 283 | 51.68 286 | 70.78 308 |
|
test2356 | | | 64.16 264 | 63.28 255 | 66.81 292 | 69.37 304 | 39.86 312 | 87.76 210 | 86.02 257 | 59.83 261 | 53.54 271 | 73.23 270 | 34.94 275 | 80.67 306 | 39.66 288 | 65.20 224 | 79.89 283 |
|
Gipuma | | | 34.91 299 | 31.44 301 | 45.30 311 | 70.99 297 | 39.64 313 | 19.85 328 | 72.56 310 | 20.10 324 | 16.16 324 | 21.47 327 | 5.08 331 | 71.16 319 | 13.07 325 | 43.70 306 | 25.08 324 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
EU-MVSNet | | | 64.01 265 | 63.01 257 | 67.02 291 | 74.40 288 | 38.86 314 | 83.27 248 | 86.19 254 | 45.11 305 | 54.27 263 | 81.15 219 | 36.91 270 | 80.01 307 | 48.79 261 | 57.02 270 | 82.19 257 |
|
testus | | | 59.36 278 | 57.51 275 | 64.90 295 | 66.72 306 | 37.56 315 | 84.98 235 | 81.09 286 | 57.46 272 | 47.72 290 | 72.76 271 | 11.43 322 | 78.78 313 | 36.56 294 | 58.91 265 | 78.36 295 |
|
FPMVS | | | 45.64 294 | 43.10 295 | 53.23 308 | 51.42 321 | 36.46 316 | 64.97 310 | 71.91 311 | 29.13 319 | 27.53 317 | 61.55 308 | 9.83 324 | 65.01 324 | 16.00 324 | 55.58 275 | 58.22 317 |
|
test1235678 | | | 55.73 284 | 52.74 284 | 64.68 296 | 60.16 316 | 35.56 317 | 81.65 260 | 81.46 284 | 51.27 288 | 38.93 312 | 62.82 306 | 17.44 313 | 78.58 314 | 30.87 310 | 50.09 296 | 79.89 283 |
|
ANet_high | | | 40.27 297 | 35.20 298 | 55.47 305 | 34.74 330 | 34.47 318 | 63.84 312 | 71.56 312 | 48.42 296 | 18.80 322 | 41.08 320 | 9.52 325 | 64.45 325 | 20.18 321 | 8.66 329 | 67.49 313 |
|
1111 | | | 56.66 283 | 54.98 282 | 61.69 298 | 61.99 313 | 31.38 319 | 79.81 278 | 83.17 280 | 45.66 302 | 41.94 306 | 65.44 299 | 41.50 239 | 79.56 310 | 27.64 312 | 47.68 299 | 74.14 303 |
|
.test1245 | | | 46.52 293 | 49.68 289 | 37.02 315 | 61.99 313 | 31.38 319 | 79.81 278 | 83.17 280 | 45.66 302 | 41.94 306 | 65.44 299 | 41.50 239 | 79.56 310 | 27.64 312 | 0.01 331 | 0.13 330 |
|
LCM-MVSNet | | | 40.54 296 | 35.79 297 | 54.76 307 | 36.92 329 | 30.81 321 | 51.41 320 | 69.02 315 | 22.07 321 | 24.63 318 | 45.37 317 | 4.56 332 | 65.81 322 | 33.67 302 | 34.50 316 | 67.67 312 |
|
testmv | | | 46.98 292 | 43.53 294 | 57.35 303 | 47.75 324 | 30.41 322 | 74.99 293 | 77.69 294 | 42.84 312 | 28.03 316 | 53.36 312 | 8.18 327 | 71.18 318 | 24.36 317 | 34.55 315 | 70.46 310 |
|
DSMNet-mixed | | | 56.78 281 | 54.44 283 | 63.79 297 | 63.21 310 | 29.44 323 | 64.43 311 | 64.10 321 | 42.12 313 | 51.32 279 | 71.60 288 | 31.76 285 | 75.04 316 | 36.23 296 | 65.20 224 | 86.87 193 |
|
PNet_i23d | | | 32.77 300 | 29.98 302 | 41.11 313 | 48.05 322 | 29.17 324 | 65.82 309 | 50.02 327 | 21.42 322 | 14.74 325 | 37.19 323 | 1.11 336 | 55.11 327 | 19.75 322 | 11.77 324 | 39.06 320 |
|
wuykxyi23d | | | 29.03 303 | 23.09 308 | 46.84 310 | 31.67 332 | 28.82 325 | 43.46 323 | 57.72 325 | 14.39 327 | 7.52 330 | 20.84 328 | 0.64 337 | 60.29 326 | 21.57 320 | 10.04 326 | 51.40 319 |
|
PMVS | | 26.43 22 | 31.84 301 | 28.16 303 | 42.89 312 | 25.87 333 | 27.58 326 | 50.92 321 | 49.78 328 | 21.37 323 | 14.17 326 | 40.81 321 | 2.01 334 | 66.62 321 | 9.61 327 | 38.88 313 | 34.49 323 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE | | 24.84 23 | 24.35 305 | 19.77 309 | 38.09 314 | 34.56 331 | 26.92 327 | 26.57 326 | 38.87 331 | 11.73 328 | 11.37 327 | 27.44 324 | 1.37 335 | 50.42 328 | 11.41 326 | 14.60 323 | 36.93 321 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PMMVS2 | | | 37.93 298 | 33.61 299 | 50.92 309 | 46.31 325 | 24.76 328 | 60.55 317 | 50.05 326 | 28.94 320 | 20.93 319 | 47.59 314 | 4.41 333 | 65.13 323 | 25.14 316 | 18.55 321 | 62.87 314 |
|
DeepMVS_CX | | | | | 34.71 316 | 51.45 320 | 24.73 329 | | 28.48 334 | 31.46 318 | 17.49 323 | 52.75 313 | 5.80 330 | 42.60 331 | 18.18 323 | 19.42 320 | 36.81 322 |
|
test12356 | | | 47.51 291 | 44.82 293 | 55.56 304 | 52.53 319 | 21.09 330 | 71.45 297 | 76.03 298 | 44.14 309 | 30.69 315 | 58.18 310 | 9.01 326 | 76.14 315 | 26.95 314 | 34.43 317 | 69.46 311 |
|
wuyk23d | | | 11.30 309 | 10.95 310 | 12.33 321 | 48.05 322 | 19.89 331 | 25.89 327 | 1.92 336 | 3.58 329 | 3.12 331 | 1.37 332 | 0.64 337 | 15.77 333 | 6.23 330 | 7.77 330 | 1.35 328 |
|
E-PMN | | | 24.61 304 | 24.00 305 | 26.45 318 | 43.74 327 | 18.44 332 | 60.86 315 | 39.66 329 | 15.11 325 | 9.53 328 | 22.10 326 | 6.52 329 | 46.94 329 | 8.31 328 | 10.14 325 | 13.98 326 |
|
EMVS | | | 23.76 306 | 23.20 307 | 25.46 319 | 41.52 328 | 16.90 333 | 60.56 316 | 38.79 332 | 14.62 326 | 8.99 329 | 20.24 330 | 7.35 328 | 45.82 330 | 7.25 329 | 9.46 327 | 13.64 327 |
|
tmp_tt | | | 22.26 307 | 23.75 306 | 17.80 320 | 5.23 334 | 12.06 334 | 35.26 324 | 39.48 330 | 2.82 330 | 18.94 321 | 44.20 318 | 22.23 308 | 24.64 332 | 36.30 295 | 9.31 328 | 16.69 325 |
|
N_pmnet | | | 50.55 289 | 49.11 291 | 54.88 306 | 77.17 277 | 4.02 335 | 84.36 238 | 2.00 335 | 48.59 295 | 45.86 295 | 68.82 294 | 32.22 283 | 82.80 298 | 31.58 309 | 51.38 287 | 77.81 296 |
|
test123 | | | 6.92 312 | 9.21 313 | 0.08 322 | 0.03 336 | 0.05 336 | 81.65 260 | 0.01 338 | 0.02 332 | 0.14 333 | 0.85 334 | 0.03 339 | 0.02 334 | 0.12 332 | 0.00 333 | 0.16 329 |
|
testmvs | | | 7.23 311 | 9.62 312 | 0.06 323 | 0.04 335 | 0.02 337 | 84.98 235 | 0.02 337 | 0.03 331 | 0.18 332 | 1.21 333 | 0.01 340 | 0.02 334 | 0.14 331 | 0.01 331 | 0.13 330 |
|
cdsmvs_eth3d_5k | | | 19.86 308 | 26.47 304 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 93.45 49 | 0.00 333 | 0.00 334 | 95.27 29 | 49.56 192 | 0.00 336 | 0.00 333 | 0.00 333 | 0.00 332 |
|
pcd_1.5k_mvsjas | | | 4.46 313 | 5.95 314 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 53.55 158 | 0.00 336 | 0.00 333 | 0.00 333 | 0.00 332 |
|
pcd1.5k->3k | | | 31.17 302 | 31.85 300 | 29.12 317 | 81.48 217 | 0.00 338 | 0.00 329 | 91.79 112 | 0.00 333 | 0.00 334 | 0.00 335 | 41.05 245 | 0.00 336 | 0.00 333 | 72.34 182 | 87.36 183 |
|
sosnet-low-res | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 333 | 0.00 332 |
|
sosnet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 333 | 0.00 332 |
|
uncertanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 333 | 0.00 332 |
|
Regformer | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 333 | 0.00 332 |
|
ab-mvs-re | | | 7.91 310 | 10.55 311 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 94.95 39 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 333 | 0.00 332 |
|
uanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 333 | 0.00 332 |
|
sam_mvs1 | | | | | | | | | | | | | 57.85 98 | | | | |
|
sam_mvs | | | | | | | | | | | | | 54.91 141 | | | | |
|
MTGPA | | | | | | | | | 92.23 93 | | | | | | | | |
|
test_post1 | | | | | | | | 78.95 280 | | | | 20.70 329 | 53.05 164 | 91.50 235 | 60.43 218 | | |
|
test_post | | | | | | | | | | | | 23.01 325 | 56.49 117 | 92.67 205 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 67.62 295 | 57.62 101 | 90.25 242 | | | |
|
MTMP | | | | | | | | | 32.52 333 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 89.41 9 | 94.96 8 | 95.29 37 |
|
agg_prior2 | | | | | | | | | | | | | | | 86.41 30 | 94.75 16 | 95.33 34 |
|
test_prior2 | | | | | | | | 95.10 26 | | 75.40 89 | 85.25 30 | 95.61 20 | 67.94 24 | | 87.47 22 | 94.77 13 | |
|
旧先验2 | | | | | | | | 92.00 109 | | 59.37 264 | 87.54 15 | | | 93.47 185 | 75.39 99 | | |
|
新几何2 | | | | | | | | 91.41 136 | | | | | | | | | |
|
无先验 | | | | | | | | 92.71 83 | 92.61 84 | 62.03 247 | | | | 97.01 65 | 66.63 166 | | 93.97 89 |
|
原ACMM2 | | | | | | | | 92.01 107 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 96.09 97 | 61.26 214 | | |
|
segment_acmp | | | | | | | | | | | | | 65.94 40 | | | | |
|
testdata1 | | | | | | | | 89.21 186 | | 77.55 63 | | | | | | | |
|
plane_prior5 | | | | | | | | | 91.31 130 | | | | | 95.55 119 | 76.74 89 | 78.53 145 | 88.39 172 |
|
plane_prior4 | | | | | | | | | | | | 89.14 131 | | | | | |
|
plane_prior2 | | | | | | | | 93.13 71 | | 78.81 49 | | | | | | | |
|
plane_prior1 | | | | | | 87.15 156 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 339 | | | | | | | | |
|
nn | | | | | | | | | 0.00 339 | | | | | | | | |
|
door-mid | | | | | | | | | 66.01 320 | | | | | | | | |
|
test11 | | | | | | | | | 93.01 69 | | | | | | | | |
|
door | | | | | | | | | 66.57 319 | | | | | | | | |
|
HQP-NCC | | | | | | 87.54 151 | | 94.06 42 | | 79.80 33 | 74.18 115 | | | | | | |
|
ACMP_Plane | | | | | | 87.54 151 | | 94.06 42 | | 79.80 33 | 74.18 115 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.63 86 | | |
|
HQP4-MVS | | | | | | | | | | | 74.18 115 | | | 95.61 116 | | | 88.63 167 |
|
HQP3-MVS | | | | | | | | | 91.70 116 | | | | | | | 78.90 142 | |
|
HQP2-MVS | | | | | | | | | | | | | 51.63 178 | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 71.63 184 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 69.72 193 | |
|
Test By Simon | | | | | | | | | | | | | 54.21 151 | | | | |
|