DeepPCF-MVS | | 80.84 1 | 88.10 9 | 88.56 9 | 86.73 45 | 92.24 60 | 69.03 89 | 89.57 74 | 93.39 23 | 77.53 42 | 89.79 10 | 94.12 31 | 78.98 5 | 96.58 26 | 85.66 19 | 95.72 16 | 94.58 12 |
|
DeepC-MVS | | 79.81 2 | 87.08 29 | 86.88 30 | 87.69 28 | 91.16 72 | 72.32 40 | 90.31 56 | 93.94 9 | 77.12 49 | 82.82 73 | 94.23 27 | 72.13 40 | 97.09 8 | 84.83 26 | 95.37 22 | 93.65 52 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DeepC-MVS_fast | | 79.65 3 | 86.91 30 | 86.62 33 | 87.76 22 | 93.52 38 | 72.37 38 | 91.26 37 | 93.04 31 | 76.62 63 | 84.22 54 | 93.36 45 | 71.44 44 | 96.76 16 | 80.82 62 | 95.33 25 | 94.16 24 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
3Dnovator+ | | 77.84 4 | 85.48 48 | 84.47 61 | 88.51 3 | 91.08 73 | 73.49 14 | 93.18 7 | 93.78 11 | 80.79 10 | 76.66 159 | 93.37 44 | 60.40 159 | 96.75 17 | 77.20 91 | 93.73 53 | 95.29 2 |
|
3Dnovator | | 76.31 5 | 83.38 73 | 82.31 81 | 86.59 49 | 87.94 160 | 72.94 25 | 90.64 47 | 92.14 70 | 77.21 47 | 75.47 180 | 92.83 57 | 58.56 166 | 94.72 88 | 73.24 124 | 92.71 60 | 92.13 105 |
|
ACMP | | 74.13 6 | 81.51 101 | 80.57 102 | 84.36 96 | 89.42 106 | 68.69 104 | 89.97 64 | 91.50 99 | 74.46 102 | 75.04 196 | 90.41 102 | 53.82 202 | 94.54 91 | 77.56 87 | 82.91 171 | 89.86 178 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PCF-MVS | | 73.52 7 | 80.38 126 | 78.84 137 | 85.01 76 | 87.71 168 | 68.99 92 | 83.65 229 | 91.46 100 | 63.00 251 | 77.77 137 | 90.28 103 | 66.10 88 | 95.09 73 | 61.40 219 | 88.22 110 | 90.94 135 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
ACMM | | 73.20 8 | 80.78 118 | 79.84 115 | 83.58 121 | 89.31 115 | 68.37 109 | 89.99 63 | 91.60 93 | 70.28 166 | 77.25 146 | 89.66 116 | 53.37 205 | 93.53 138 | 74.24 115 | 82.85 172 | 88.85 206 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
TAPA-MVS | | 73.13 9 | 79.15 151 | 77.94 156 | 82.79 158 | 89.59 100 | 62.99 211 | 88.16 121 | 91.51 96 | 65.77 224 | 77.14 151 | 91.09 88 | 60.91 149 | 93.21 147 | 50.26 283 | 87.05 123 | 92.17 104 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
OpenMVS | | 72.83 10 | 79.77 137 | 78.33 149 | 84.09 106 | 85.17 209 | 69.91 74 | 90.57 48 | 90.97 111 | 66.70 211 | 72.17 221 | 91.91 67 | 54.70 194 | 93.96 112 | 61.81 216 | 90.95 76 | 88.41 219 |
|
PLC | | 70.83 11 | 78.05 175 | 76.37 187 | 83.08 141 | 91.88 67 | 67.80 120 | 88.19 119 | 89.46 153 | 64.33 241 | 69.87 248 | 88.38 151 | 53.66 203 | 93.58 133 | 58.86 240 | 82.73 174 | 87.86 227 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
HY-MVS | | 69.67 12 | 77.95 178 | 77.15 173 | 80.36 199 | 87.57 175 | 60.21 236 | 83.37 234 | 87.78 193 | 66.11 219 | 75.37 185 | 87.06 186 | 63.27 111 | 90.48 222 | 61.38 220 | 82.43 178 | 90.40 155 |
|
LTVRE_ROB | | 69.57 13 | 76.25 203 | 74.54 207 | 81.41 182 | 88.60 140 | 64.38 180 | 79.24 268 | 89.12 164 | 70.76 158 | 69.79 250 | 87.86 163 | 49.09 253 | 93.20 149 | 56.21 261 | 80.16 203 | 86.65 255 |
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 |
ACMH+ | | 68.96 14 | 76.01 206 | 74.01 212 | 82.03 171 | 88.60 140 | 65.31 163 | 88.86 91 | 87.55 196 | 70.25 167 | 67.75 262 | 87.47 173 | 41.27 294 | 93.19 150 | 58.37 245 | 75.94 246 | 87.60 232 |
|
IB-MVS | | 68.01 15 | 75.85 208 | 73.36 219 | 83.31 129 | 84.76 216 | 66.03 145 | 83.38 233 | 85.06 225 | 70.21 168 | 69.40 252 | 81.05 273 | 45.76 273 | 94.66 89 | 65.10 189 | 75.49 253 | 89.25 190 |
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 |
ACMH | | 67.68 16 | 75.89 207 | 73.93 213 | 81.77 176 | 88.71 138 | 66.61 138 | 88.62 101 | 89.01 167 | 69.81 172 | 66.78 274 | 86.70 196 | 41.95 293 | 91.51 199 | 55.64 262 | 78.14 222 | 87.17 243 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
COLMAP_ROB | | 66.92 17 | 73.01 237 | 70.41 246 | 80.81 194 | 87.13 185 | 65.63 154 | 88.30 114 | 84.19 235 | 62.96 252 | 63.80 294 | 87.69 166 | 38.04 307 | 92.56 172 | 46.66 300 | 74.91 261 | 84.24 284 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PVSNet | | 64.34 18 | 72.08 245 | 70.87 244 | 75.69 264 | 86.21 197 | 56.44 278 | 74.37 300 | 80.73 274 | 62.06 263 | 70.17 241 | 82.23 265 | 42.86 286 | 83.31 289 | 54.77 265 | 84.45 152 | 87.32 239 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 255 | 68.19 259 | 77.65 246 | 80.26 286 | 59.41 243 | 85.01 199 | 82.96 255 | 58.76 287 | 65.43 283 | 82.33 263 | 37.63 309 | 91.23 206 | 45.34 308 | 76.03 245 | 82.32 300 |
|
PVSNet_0 | | 57.27 20 | 61.67 291 | 59.27 293 | 68.85 300 | 79.61 296 | 57.44 265 | 68.01 316 | 73.44 312 | 55.93 303 | 58.54 309 | 70.41 315 | 44.58 278 | 77.55 310 | 47.01 299 | 35.91 325 | 71.55 320 |
|
CMPMVS | | 51.72 21 | 70.19 259 | 68.16 260 | 76.28 260 | 73.15 320 | 57.55 263 | 79.47 266 | 83.92 237 | 48.02 316 | 56.48 315 | 84.81 237 | 43.13 284 | 86.42 272 | 62.67 207 | 81.81 185 | 84.89 277 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PMVS | | 37.38 22 | 44.16 301 | 40.28 303 | 55.82 310 | 40.82 335 | 42.54 324 | 65.12 321 | 63.99 328 | 34.43 325 | 24.48 329 | 57.12 325 | 3.92 338 | 76.17 315 | 17.10 329 | 55.52 318 | 48.75 325 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE | | 26.22 23 | 30.37 305 | 25.89 308 | 43.81 315 | 44.55 334 | 35.46 329 | 28.87 333 | 39.07 334 | 18.20 330 | 18.58 332 | 40.18 327 | 2.68 339 | 47.37 333 | 17.07 330 | 23.78 328 | 48.60 326 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
9.14 | | | | 88.26 11 | | 92.84 52 | | 91.52 35 | 94.75 1 | 73.93 113 | 88.57 13 | 94.67 12 | 75.57 13 | 95.79 45 | 86.77 15 | 95.76 15 | |
|
testtj | | | 87.78 14 | 87.78 15 | 87.77 21 | 94.55 12 | 72.47 35 | 92.23 24 | 93.49 18 | 74.75 97 | 88.33 15 | 94.43 22 | 73.27 29 | 97.02 10 | 84.18 36 | 94.84 35 | 93.82 43 |
|
save filter2 | | | | | | | | | | | 88.41 14 | 94.67 12 | 72.46 35 | 94.59 90 | 86.67 18 | 95.86 11 | 91.94 109 |
|
save fliter | | | | | | 93.80 32 | 72.35 39 | 90.47 52 | 91.17 107 | 74.31 104 | | | | | | | |
|
ET-MVSNet_ETH3D | | | 78.63 162 | 76.63 183 | 84.64 88 | 86.73 192 | 69.47 84 | 85.01 199 | 84.61 229 | 69.54 179 | 66.51 277 | 86.59 200 | 50.16 240 | 91.75 193 | 76.26 100 | 84.24 154 | 92.69 87 |
|
UniMVSNet_ETH3D | | | 79.10 153 | 78.24 151 | 81.70 177 | 86.85 188 | 60.24 235 | 87.28 142 | 88.79 173 | 74.25 106 | 76.84 153 | 90.53 101 | 49.48 248 | 91.56 198 | 67.98 162 | 82.15 180 | 93.29 66 |
|
ETV-MVS | | | 83.31 74 | 82.80 75 | 84.82 83 | 89.59 100 | 65.59 155 | 88.21 118 | 92.68 50 | 74.66 99 | 78.96 112 | 86.42 207 | 69.06 67 | 95.26 64 | 75.54 108 | 90.09 87 | 93.62 54 |
|
miper_lstm_enhance | | | 74.11 225 | 73.11 223 | 77.13 255 | 80.11 288 | 59.62 239 | 72.23 304 | 86.92 206 | 66.76 210 | 70.40 237 | 82.92 255 | 56.93 182 | 82.92 291 | 69.06 156 | 72.63 277 | 88.87 205 |
|
EIA-MVS | | | 84.90 60 | 84.67 59 | 85.59 63 | 89.39 108 | 68.66 105 | 88.74 98 | 92.64 54 | 79.97 17 | 84.10 56 | 85.71 221 | 69.32 64 | 95.38 59 | 80.82 62 | 91.37 71 | 92.72 84 |
|
CS-MVS | | | 84.76 61 | 84.61 60 | 85.22 71 | 89.66 98 | 66.43 140 | 90.23 58 | 93.56 15 | 76.52 65 | 82.59 77 | 85.93 215 | 70.41 52 | 95.80 44 | 79.93 71 | 92.68 61 | 93.42 61 |
|
D2MVS | | | 74.82 218 | 73.21 221 | 79.64 215 | 79.81 292 | 62.56 214 | 80.34 258 | 87.35 201 | 64.37 240 | 68.86 255 | 82.66 260 | 46.37 266 | 90.10 227 | 67.91 163 | 81.24 189 | 86.25 259 |
|
DVP-MVS | | | 89.60 1 | 90.35 1 | 87.33 34 | 95.27 2 | 71.25 51 | 93.49 5 | 92.73 49 | 77.33 45 | 92.12 4 | 95.78 4 | 80.98 3 | 97.40 2 | 89.08 4 | 96.41 4 | 93.33 65 |
|
test_0728_THIRD | | | | | | | | | | 78.38 32 | 92.12 4 | 95.78 4 | 81.46 2 | 97.40 2 | 89.42 2 | 96.57 2 | 94.67 9 |
|
test_0728_SECOND | | | | | 87.71 27 | 95.34 1 | 71.43 50 | 93.49 5 | 94.23 2 | | | | | 97.49 1 | 89.08 4 | 96.41 4 | 94.21 23 |
|
test0726 | | | | | | 95.27 2 | 71.25 51 | 93.60 4 | 94.11 3 | 77.33 45 | 92.81 1 | 95.79 3 | 80.98 3 | | | | |
|
SR-MVS | | | 86.73 31 | 86.67 32 | 86.91 41 | 94.11 29 | 72.11 43 | 92.37 20 | 92.56 56 | 74.50 101 | 86.84 24 | 94.65 14 | 67.31 80 | 95.77 46 | 84.80 27 | 92.85 58 | 92.84 83 |
|
DPM-MVS | | | 84.93 58 | 84.29 62 | 86.84 42 | 90.20 88 | 73.04 20 | 87.12 145 | 93.04 31 | 69.80 173 | 82.85 72 | 91.22 84 | 73.06 31 | 96.02 38 | 76.72 98 | 94.63 39 | 91.46 123 |
|
GST-MVS | | | 87.42 21 | 87.26 21 | 87.89 20 | 94.12 28 | 72.97 21 | 92.39 18 | 93.43 21 | 76.89 55 | 84.68 45 | 93.99 35 | 70.67 51 | 96.82 13 | 84.18 36 | 95.01 29 | 93.90 38 |
|
test_yl | | | 81.17 104 | 80.47 105 | 83.24 133 | 89.13 122 | 63.62 191 | 86.21 172 | 89.95 140 | 72.43 135 | 81.78 86 | 89.61 118 | 57.50 174 | 93.58 133 | 70.75 137 | 86.90 125 | 92.52 90 |
|
thisisatest0530 | | | 79.40 146 | 77.76 163 | 84.31 99 | 87.69 170 | 65.10 168 | 87.36 139 | 84.26 234 | 70.04 169 | 77.42 142 | 88.26 156 | 49.94 243 | 94.79 86 | 70.20 143 | 84.70 149 | 93.03 77 |
|
Anonymous20240529 | | | 80.19 131 | 78.89 136 | 84.10 105 | 90.60 81 | 64.75 172 | 88.95 88 | 90.90 113 | 65.97 223 | 80.59 100 | 91.17 86 | 49.97 242 | 93.73 131 | 69.16 155 | 82.70 176 | 93.81 44 |
|
Anonymous202405211 | | | 78.25 168 | 77.01 175 | 81.99 172 | 91.03 74 | 60.67 231 | 84.77 204 | 83.90 238 | 70.65 161 | 80.00 103 | 91.20 85 | 41.08 296 | 91.43 200 | 65.21 187 | 85.26 143 | 93.85 40 |
|
DCV-MVSNet | | | 81.17 104 | 80.47 105 | 83.24 133 | 89.13 122 | 63.62 191 | 86.21 172 | 89.95 140 | 72.43 135 | 81.78 86 | 89.61 118 | 57.50 174 | 93.58 133 | 70.75 137 | 86.90 125 | 92.52 90 |
|
tttt0517 | | | 79.40 146 | 77.91 157 | 83.90 117 | 88.10 155 | 63.84 188 | 88.37 112 | 84.05 236 | 71.45 149 | 76.78 156 | 89.12 132 | 49.93 245 | 94.89 81 | 70.18 144 | 83.18 168 | 92.96 81 |
|
our_test_3 | | | 69.14 265 | 67.00 272 | 75.57 266 | 79.80 293 | 58.80 245 | 77.96 281 | 77.81 295 | 59.55 280 | 62.90 298 | 78.25 295 | 47.43 259 | 83.97 284 | 51.71 275 | 67.58 299 | 83.93 288 |
|
thisisatest0515 | | | 77.33 187 | 75.38 197 | 83.18 136 | 85.27 208 | 63.80 189 | 82.11 242 | 83.27 249 | 65.06 231 | 75.91 175 | 83.84 246 | 49.54 247 | 94.27 99 | 67.24 171 | 86.19 136 | 91.48 122 |
|
ppachtmachnet_test | | | 70.04 260 | 67.34 271 | 78.14 238 | 79.80 293 | 61.13 225 | 79.19 270 | 80.59 276 | 59.16 284 | 65.27 284 | 79.29 288 | 46.75 265 | 87.29 265 | 49.33 287 | 66.72 300 | 86.00 268 |
|
SMA-MVS | | | 89.08 5 | 89.23 5 | 88.61 2 | 94.25 22 | 73.73 7 | 92.40 17 | 93.63 13 | 74.77 96 | 92.29 2 | 95.97 2 | 74.28 22 | 97.24 5 | 88.58 7 | 96.91 1 | 94.87 7 |
|
GSMVS | | | | | | | | | | | | | | | | | 88.96 202 |
|
DPE-MVS | | | 89.48 3 | 89.98 2 | 88.01 10 | 94.80 6 | 72.69 28 | 91.59 32 | 94.10 5 | 75.90 77 | 92.29 2 | 95.66 6 | 81.67 1 | 97.38 4 | 87.44 14 | 96.34 7 | 93.95 35 |
|
test_part2 | | | | | | 95.06 4 | 72.65 29 | | | | 91.80 6 | | | | | | |
|
test_part1 | | | | | 0.00 323 | | 0.00 341 | 0.00 334 | 94.09 6 | | | | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
thres100view900 | | | 76.50 197 | 75.55 193 | 79.33 219 | 89.52 103 | 56.99 269 | 85.83 183 | 83.23 250 | 73.94 112 | 76.32 167 | 87.12 183 | 51.89 222 | 91.95 188 | 48.33 291 | 83.75 159 | 89.07 191 |
|
tfpnnormal | | | 74.39 221 | 73.16 222 | 78.08 239 | 86.10 198 | 58.05 252 | 84.65 209 | 87.53 197 | 70.32 165 | 71.22 231 | 85.63 224 | 54.97 189 | 89.86 229 | 43.03 312 | 75.02 260 | 86.32 258 |
|
tfpn200view9 | | | 76.42 200 | 75.37 198 | 79.55 218 | 89.13 122 | 57.65 261 | 85.17 194 | 83.60 241 | 73.41 123 | 76.45 162 | 86.39 208 | 52.12 215 | 91.95 188 | 48.33 291 | 83.75 159 | 89.07 191 |
|
CHOSEN 280x420 | | | 66.51 280 | 64.71 279 | 71.90 285 | 81.45 272 | 63.52 196 | 57.98 325 | 68.95 322 | 53.57 308 | 62.59 299 | 76.70 303 | 46.22 268 | 75.29 319 | 55.25 263 | 79.68 206 | 76.88 317 |
|
CANet | | | 86.45 36 | 86.10 41 | 87.51 31 | 90.09 90 | 70.94 58 | 89.70 72 | 92.59 55 | 81.78 4 | 81.32 90 | 91.43 81 | 70.34 53 | 97.23 6 | 84.26 33 | 93.36 54 | 94.37 18 |
|
Fast-Effi-MVS+-dtu | | | 78.02 176 | 76.49 184 | 82.62 163 | 83.16 246 | 66.96 135 | 86.94 150 | 87.45 200 | 72.45 132 | 71.49 229 | 84.17 242 | 54.79 193 | 91.58 197 | 67.61 165 | 80.31 202 | 89.30 189 |
|
Effi-MVS+-dtu | | | 80.03 133 | 78.57 142 | 84.42 94 | 85.13 212 | 68.74 99 | 88.77 95 | 88.10 186 | 74.99 92 | 74.97 197 | 83.49 251 | 57.27 177 | 93.36 143 | 73.53 120 | 80.88 192 | 91.18 128 |
|
CANet_DTU | | | 80.61 120 | 79.87 114 | 82.83 153 | 85.60 204 | 63.17 207 | 87.36 139 | 88.65 178 | 76.37 70 | 75.88 176 | 88.44 150 | 53.51 204 | 93.07 157 | 73.30 123 | 89.74 92 | 92.25 100 |
|
MVS_0304 | | | 72.48 241 | 70.89 243 | 77.24 253 | 82.20 263 | 59.68 238 | 84.11 223 | 83.49 245 | 67.10 208 | 66.87 272 | 80.59 278 | 35.00 316 | 87.40 264 | 59.07 238 | 79.58 207 | 84.63 281 |
|
MP-MVS-pluss | | | 87.67 16 | 87.72 16 | 87.54 30 | 93.64 36 | 72.04 44 | 89.80 68 | 93.50 17 | 75.17 91 | 86.34 26 | 95.29 7 | 70.86 47 | 96.00 40 | 88.78 6 | 96.04 8 | 94.58 12 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
MSP-MVS | | | 89.51 2 | 89.91 3 | 88.30 6 | 94.28 21 | 73.46 15 | 92.90 11 | 94.11 3 | 80.27 12 | 91.35 7 | 94.16 29 | 78.35 6 | 96.77 15 | 89.59 1 | 94.22 50 | 94.67 9 |
|
sam_mvs1 | | | | | | | | | | | | | 51.32 228 | | | | 88.96 202 |
|
sam_mvs | | | | | | | | | | | | | 50.01 241 | | | | |
|
IterMVS-SCA-FT | | | 75.43 214 | 73.87 215 | 80.11 204 | 82.69 258 | 64.85 170 | 81.57 249 | 83.47 246 | 69.16 186 | 70.49 236 | 84.15 243 | 51.95 220 | 88.15 257 | 69.23 153 | 72.14 281 | 87.34 238 |
|
TSAR-MVS + MP. | | | 88.02 13 | 88.11 12 | 87.72 25 | 93.68 35 | 72.13 42 | 91.41 36 | 92.35 63 | 74.62 100 | 88.90 11 | 93.85 37 | 75.75 12 | 96.00 40 | 87.80 9 | 94.63 39 | 95.04 3 |
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition |
xiu_mvs_v1_base_debu | | | 80.80 115 | 79.72 118 | 84.03 110 | 87.35 176 | 70.19 70 | 85.56 186 | 88.77 174 | 69.06 188 | 81.83 82 | 88.16 157 | 50.91 231 | 92.85 164 | 78.29 82 | 87.56 115 | 89.06 193 |
|
OPM-MVS | | | 83.50 69 | 82.95 72 | 85.14 72 | 88.79 135 | 70.95 57 | 89.13 85 | 91.52 95 | 77.55 41 | 80.96 97 | 91.75 70 | 60.71 151 | 94.50 94 | 79.67 72 | 86.51 132 | 89.97 174 |
|
ACMMP_NAP | | | 88.05 12 | 88.08 13 | 87.94 13 | 93.70 33 | 73.05 19 | 90.86 43 | 93.59 14 | 76.27 73 | 88.14 16 | 95.09 8 | 71.06 46 | 96.67 19 | 87.67 10 | 96.37 6 | 94.09 27 |
|
ambc | | | | | 75.24 270 | 73.16 319 | 50.51 312 | 63.05 324 | 87.47 199 | | 64.28 290 | 77.81 299 | 17.80 329 | 89.73 232 | 57.88 250 | 60.64 312 | 85.49 270 |
|
zzz-MVS | | | 87.53 18 | 87.41 20 | 87.90 17 | 94.18 26 | 74.25 2 | 90.23 58 | 92.02 73 | 79.45 19 | 85.88 28 | 94.80 9 | 68.07 71 | 96.21 33 | 86.69 16 | 95.34 23 | 93.23 68 |
|
MTGPA | | | | | | | | | 92.02 73 | | | | | | | | |
|
mvs-test1 | | | 80.88 108 | 79.40 125 | 85.29 67 | 85.13 212 | 69.75 78 | 89.28 77 | 88.10 186 | 74.99 92 | 76.44 165 | 86.72 191 | 57.27 177 | 94.26 103 | 73.53 120 | 83.18 168 | 91.87 111 |
|
Effi-MVS+ | | | 83.62 68 | 83.08 69 | 85.24 69 | 88.38 148 | 67.45 124 | 88.89 90 | 89.15 163 | 75.50 83 | 82.27 78 | 88.28 154 | 69.61 61 | 94.45 95 | 77.81 85 | 87.84 111 | 93.84 42 |
|
xiu_mvs_v2_base | | | 81.69 95 | 81.05 97 | 83.60 120 | 89.15 121 | 68.03 117 | 84.46 214 | 90.02 138 | 70.67 159 | 81.30 93 | 86.53 205 | 63.17 114 | 94.19 105 | 75.60 107 | 88.54 105 | 88.57 215 |
|
xiu_mvs_v1_base | | | 80.80 115 | 79.72 118 | 84.03 110 | 87.35 176 | 70.19 70 | 85.56 186 | 88.77 174 | 69.06 188 | 81.83 82 | 88.16 157 | 50.91 231 | 92.85 164 | 78.29 82 | 87.56 115 | 89.06 193 |
|
new-patchmatchnet | | | 61.73 290 | 61.73 291 | 61.70 307 | 72.74 322 | 24.50 335 | 69.16 313 | 78.03 294 | 61.40 266 | 56.72 314 | 75.53 308 | 38.42 305 | 76.48 314 | 45.95 305 | 57.67 315 | 84.13 286 |
|
pmmvs6 | | | 74.69 219 | 73.39 218 | 78.61 231 | 81.38 274 | 57.48 264 | 86.64 161 | 87.95 190 | 64.99 234 | 70.18 240 | 86.61 199 | 50.43 238 | 89.52 235 | 62.12 212 | 70.18 291 | 88.83 207 |
|
pmmvs5 | | | 71.55 247 | 70.20 248 | 75.61 265 | 77.83 303 | 56.39 279 | 81.74 245 | 80.89 271 | 57.76 292 | 67.46 265 | 84.49 240 | 49.26 252 | 85.32 280 | 57.08 257 | 75.29 257 | 85.11 276 |
|
test_post1 | | | | | | | | 78.90 274 | | | | 5.43 334 | 48.81 256 | 85.44 279 | 59.25 235 | | |
|
test_post | | | | | | | | | | | | 5.46 333 | 50.36 239 | 84.24 283 | | | |
|
Fast-Effi-MVS+ | | | 80.81 113 | 79.92 113 | 83.47 123 | 88.85 129 | 64.51 175 | 85.53 191 | 89.39 154 | 70.79 156 | 78.49 121 | 85.06 235 | 67.54 77 | 93.58 133 | 67.03 175 | 86.58 130 | 92.32 97 |
|
patchmatchnet-post | | | | | | | | | | | | 74.00 310 | 51.12 230 | 88.60 252 | | | |
|
Anonymous20231211 | | | 78.97 157 | 77.69 165 | 82.81 155 | 90.54 82 | 64.29 181 | 90.11 62 | 91.51 96 | 65.01 233 | 76.16 174 | 88.13 161 | 50.56 236 | 93.03 161 | 69.68 150 | 77.56 226 | 91.11 130 |
|
pmmvs-eth3d | | | 70.50 256 | 67.83 266 | 78.52 234 | 77.37 306 | 66.18 144 | 81.82 243 | 81.51 268 | 58.90 286 | 63.90 293 | 80.42 280 | 42.69 287 | 86.28 273 | 58.56 243 | 65.30 305 | 83.11 295 |
|
GG-mvs-BLEND | | | | | 75.38 269 | 81.59 270 | 55.80 286 | 79.32 267 | 69.63 318 | | 67.19 268 | 73.67 311 | 43.24 283 | 88.90 250 | 50.41 280 | 84.50 150 | 81.45 305 |
|
xiu_mvs_v1_base_debi | | | 80.80 115 | 79.72 118 | 84.03 110 | 87.35 176 | 70.19 70 | 85.56 186 | 88.77 174 | 69.06 188 | 81.83 82 | 88.16 157 | 50.91 231 | 92.85 164 | 78.29 82 | 87.56 115 | 89.06 193 |
|
Anonymous20231206 | | | 68.60 267 | 67.80 267 | 71.02 292 | 80.23 287 | 50.75 311 | 78.30 279 | 80.47 278 | 56.79 299 | 66.11 280 | 82.63 261 | 46.35 267 | 78.95 303 | 43.62 311 | 75.70 249 | 83.36 292 |
|
MTAPA | | | 87.23 25 | 87.00 26 | 87.90 17 | 94.18 26 | 74.25 2 | 86.58 163 | 92.02 73 | 79.45 19 | 85.88 28 | 94.80 9 | 68.07 71 | 96.21 33 | 86.69 16 | 95.34 23 | 93.23 68 |
|
MTMP | | | | | | | | 92.18 26 | 32.83 335 | | | | | | | | |
|
gm-plane-assit | | | | | | 81.40 273 | 53.83 298 | | | 62.72 257 | | 80.94 276 | | 92.39 176 | 63.40 200 | | |
|
test9_res | | | | | | | | | | | | | | | 84.90 23 | 95.70 18 | 92.87 82 |
|
MVP-Stereo | | | 76.12 204 | 74.46 209 | 81.13 190 | 85.37 207 | 69.79 76 | 84.42 217 | 87.95 190 | 65.03 232 | 67.46 265 | 85.33 230 | 53.28 206 | 91.73 195 | 58.01 249 | 83.27 166 | 81.85 303 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
TEST9 | | | | | | 93.26 42 | 72.96 22 | 88.75 96 | 91.89 82 | 68.44 199 | 85.00 37 | 93.10 49 | 74.36 21 | 95.41 57 | | | |
|
train_agg | | | 86.43 37 | 86.20 38 | 87.13 38 | 93.26 42 | 72.96 22 | 88.75 96 | 91.89 82 | 68.69 195 | 85.00 37 | 93.10 49 | 74.43 18 | 95.41 57 | 84.97 22 | 95.71 17 | 93.02 78 |
|
gg-mvs-nofinetune | | | 69.95 261 | 67.96 263 | 75.94 262 | 83.07 247 | 54.51 294 | 77.23 285 | 70.29 316 | 63.11 249 | 70.32 238 | 62.33 319 | 43.62 282 | 88.69 251 | 53.88 268 | 87.76 112 | 84.62 282 |
|
SCA | | | 74.22 224 | 72.33 230 | 79.91 207 | 84.05 228 | 62.17 219 | 79.96 262 | 79.29 289 | 66.30 218 | 72.38 219 | 80.13 282 | 51.95 220 | 88.60 252 | 59.25 235 | 77.67 225 | 88.96 202 |
|
Patchmatch-test | | | 64.82 286 | 63.24 285 | 69.57 296 | 79.42 298 | 49.82 314 | 63.49 323 | 69.05 321 | 51.98 313 | 59.95 306 | 80.13 282 | 50.91 231 | 70.98 325 | 40.66 317 | 73.57 273 | 87.90 226 |
|
test_8 | | | | | | 93.13 44 | 72.57 32 | 88.68 100 | 91.84 85 | 68.69 195 | 84.87 43 | 93.10 49 | 74.43 18 | 95.16 67 | | | |
|
MS-PatchMatch | | | 73.83 228 | 72.67 226 | 77.30 252 | 83.87 231 | 66.02 146 | 81.82 243 | 84.66 228 | 61.37 268 | 68.61 258 | 82.82 258 | 47.29 260 | 88.21 256 | 59.27 234 | 84.32 153 | 77.68 315 |
|
Patchmatch-RL test | | | 70.24 258 | 67.78 268 | 77.61 247 | 77.43 305 | 59.57 241 | 71.16 306 | 70.33 315 | 62.94 253 | 68.65 257 | 72.77 312 | 50.62 235 | 85.49 278 | 69.58 151 | 66.58 302 | 87.77 229 |
|
cdsmvs_eth3d_5k | | | 19.96 306 | 26.61 307 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 89.26 160 | 0.00 336 | 0.00 338 | 88.61 144 | 61.62 136 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
pcd_1.5k_mvsjas | | | 5.26 312 | 7.02 314 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 63.15 115 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
agg_prior1 | | | 86.22 41 | 86.09 42 | 86.62 48 | 92.85 50 | 71.94 45 | 88.59 102 | 91.78 88 | 68.96 192 | 84.41 50 | 93.18 48 | 74.94 14 | 94.93 76 | 84.75 28 | 95.33 25 | 93.01 79 |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 47 | 95.45 20 | 92.70 85 |
|
agg_prior | | | | | | 92.85 50 | 71.94 45 | | 91.78 88 | | 84.41 50 | | | 94.93 76 | | | |
|
tmp_tt | | | 18.61 307 | 21.40 309 | 10.23 320 | 4.82 337 | 10.11 337 | 34.70 331 | 30.74 336 | 1.48 333 | 23.91 331 | 26.07 331 | 28.42 322 | 13.41 336 | 27.12 325 | 15.35 332 | 7.17 331 |
|
canonicalmvs | | | 85.91 43 | 85.87 44 | 86.04 59 | 89.84 96 | 69.44 87 | 90.45 54 | 93.00 35 | 76.70 62 | 88.01 19 | 91.23 83 | 73.28 28 | 93.91 119 | 81.50 56 | 88.80 100 | 94.77 8 |
|
anonymousdsp | | | 78.60 163 | 77.15 173 | 82.98 147 | 80.51 285 | 67.08 131 | 87.24 143 | 89.53 151 | 65.66 226 | 75.16 192 | 87.19 181 | 52.52 207 | 92.25 181 | 77.17 92 | 79.34 213 | 89.61 183 |
|
alignmvs | | | 85.48 48 | 85.32 49 | 85.96 60 | 89.51 104 | 69.47 84 | 89.74 70 | 92.47 57 | 76.17 74 | 87.73 21 | 91.46 80 | 70.32 54 | 93.78 125 | 81.51 55 | 88.95 97 | 94.63 11 |
|
nrg030 | | | 83.88 63 | 83.53 64 | 84.96 78 | 86.77 191 | 69.28 88 | 90.46 53 | 92.67 51 | 74.79 95 | 82.95 69 | 91.33 82 | 72.70 33 | 93.09 156 | 80.79 64 | 79.28 214 | 92.50 92 |
|
v144192 | | | 79.47 143 | 78.37 147 | 82.78 159 | 83.35 238 | 63.96 186 | 86.96 149 | 90.36 129 | 69.99 170 | 77.50 140 | 85.67 223 | 60.66 153 | 93.77 127 | 74.27 114 | 76.58 239 | 90.62 144 |
|
FIs | | | 82.07 89 | 82.42 77 | 81.04 191 | 88.80 134 | 58.34 249 | 88.26 117 | 93.49 18 | 76.93 54 | 78.47 122 | 91.04 90 | 69.92 58 | 92.34 179 | 69.87 148 | 84.97 145 | 92.44 94 |
|
v1921920 | | | 79.22 150 | 78.03 154 | 82.80 156 | 83.30 240 | 63.94 187 | 86.80 155 | 90.33 130 | 69.91 171 | 77.48 141 | 85.53 226 | 58.44 167 | 93.75 129 | 73.60 119 | 76.85 235 | 90.71 142 |
|
UA-Net | | | 85.08 57 | 84.96 55 | 85.45 64 | 92.07 63 | 68.07 116 | 89.78 69 | 90.86 115 | 82.48 2 | 84.60 48 | 93.20 47 | 69.35 63 | 95.22 65 | 71.39 136 | 90.88 77 | 93.07 75 |
|
v1192 | | | 79.59 140 | 78.43 146 | 83.07 142 | 83.55 236 | 64.52 174 | 86.93 151 | 90.58 120 | 70.83 155 | 77.78 136 | 85.90 216 | 59.15 163 | 93.94 115 | 73.96 117 | 77.19 230 | 90.76 139 |
|
FC-MVSNet-test | | | 81.52 99 | 82.02 85 | 80.03 205 | 88.42 147 | 55.97 285 | 87.95 125 | 93.42 22 | 77.10 50 | 77.38 143 | 90.98 95 | 69.96 57 | 91.79 192 | 68.46 160 | 84.50 150 | 92.33 96 |
|
v1144 | | | 80.03 133 | 79.03 133 | 83.01 145 | 83.78 232 | 64.51 175 | 87.11 146 | 90.57 121 | 71.96 141 | 78.08 132 | 86.20 212 | 61.41 139 | 93.94 115 | 74.93 110 | 77.23 228 | 90.60 146 |
|
sosnet-low-res | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
HFP-MVS | | | 87.58 17 | 87.47 19 | 87.94 13 | 94.58 10 | 73.54 12 | 93.04 8 | 93.24 25 | 76.78 58 | 84.91 39 | 94.44 20 | 70.78 48 | 96.61 22 | 84.53 29 | 94.89 33 | 93.66 47 |
|
v148 | | | 78.72 160 | 77.80 160 | 81.47 181 | 82.73 257 | 61.96 221 | 86.30 170 | 88.08 188 | 73.26 125 | 76.18 171 | 85.47 228 | 62.46 126 | 92.36 178 | 71.92 133 | 73.82 272 | 90.09 165 |
|
sosnet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
uncertanet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
AllTest | | | 70.96 251 | 68.09 262 | 79.58 216 | 85.15 210 | 63.62 191 | 84.58 211 | 79.83 285 | 62.31 260 | 60.32 304 | 86.73 189 | 32.02 319 | 88.96 247 | 50.28 281 | 71.57 285 | 86.15 262 |
|
TestCases | | | | | 79.58 216 | 85.15 210 | 63.62 191 | | 79.83 285 | 62.31 260 | 60.32 304 | 86.73 189 | 32.02 319 | 88.96 247 | 50.28 281 | 71.57 285 | 86.15 262 |
|
v7n | | | 78.97 157 | 77.58 167 | 83.14 138 | 83.45 237 | 65.51 156 | 88.32 113 | 91.21 105 | 73.69 117 | 72.41 218 | 86.32 210 | 57.93 169 | 93.81 123 | 69.18 154 | 75.65 250 | 90.11 163 |
|
region2R | | | 87.42 21 | 87.20 24 | 88.09 8 | 94.63 9 | 73.55 10 | 93.03 10 | 93.12 30 | 76.73 61 | 84.45 49 | 94.52 15 | 69.09 66 | 96.70 18 | 84.37 32 | 94.83 36 | 94.03 30 |
|
testing_2 | | | 75.73 209 | 73.34 220 | 82.89 152 | 77.37 306 | 65.22 164 | 84.10 224 | 90.54 122 | 69.09 187 | 60.46 303 | 81.15 272 | 40.48 298 | 92.84 167 | 76.36 99 | 80.54 200 | 90.60 146 |
|
test_normal | | | 67.47 273 | 63.56 283 | 79.18 224 | 72.78 321 | 55.71 288 | 40.72 329 | 90.78 116 | 72.12 140 | 48.43 322 | 65.82 317 | 32.32 318 | 92.25 181 | 72.25 131 | 76.85 235 | 89.59 185 |
|
PS-MVSNAJss | | | 82.07 89 | 81.31 92 | 84.34 98 | 86.51 194 | 67.27 129 | 89.27 78 | 91.51 96 | 71.75 142 | 79.37 108 | 90.22 106 | 63.15 115 | 94.27 99 | 77.69 86 | 82.36 179 | 91.49 121 |
|
PS-MVSNAJ | | | 81.69 95 | 81.02 98 | 83.70 119 | 89.51 104 | 68.21 114 | 84.28 220 | 90.09 137 | 70.79 156 | 81.26 94 | 85.62 225 | 63.15 115 | 94.29 97 | 75.62 106 | 88.87 99 | 88.59 214 |
|
jajsoiax | | | 79.29 149 | 77.96 155 | 83.27 131 | 84.68 218 | 66.57 139 | 89.25 79 | 90.16 135 | 69.20 185 | 75.46 181 | 89.49 122 | 45.75 274 | 93.13 154 | 76.84 96 | 80.80 194 | 90.11 163 |
|
mvs_tets | | | 79.13 152 | 77.77 162 | 83.22 135 | 84.70 217 | 66.37 142 | 89.17 80 | 90.19 134 | 69.38 181 | 75.40 184 | 89.46 125 | 44.17 280 | 93.15 152 | 76.78 97 | 80.70 196 | 90.14 161 |
|
#test# | | | 87.33 24 | 87.13 25 | 87.94 13 | 94.58 10 | 73.54 12 | 92.34 21 | 93.24 25 | 75.23 88 | 84.91 39 | 94.44 20 | 70.78 48 | 96.61 22 | 83.75 39 | 94.89 33 | 93.66 47 |
|
EI-MVSNet-UG-set | | | 83.81 64 | 83.38 66 | 85.09 74 | 87.87 161 | 67.53 123 | 87.44 138 | 89.66 148 | 79.74 18 | 82.23 79 | 89.41 129 | 70.24 55 | 94.74 87 | 79.95 69 | 83.92 156 | 92.99 80 |
|
EI-MVSNet-Vis-set | | | 84.19 62 | 83.81 63 | 85.31 66 | 88.18 152 | 67.85 119 | 87.66 132 | 89.73 147 | 80.05 16 | 82.95 69 | 89.59 120 | 70.74 50 | 94.82 84 | 80.66 65 | 84.72 148 | 93.28 67 |
|
Regformer-3 | | | 85.23 53 | 85.07 53 | 85.70 62 | 88.95 127 | 69.01 91 | 88.29 115 | 89.91 142 | 80.95 8 | 85.01 36 | 90.01 110 | 72.45 36 | 94.19 105 | 82.50 51 | 87.57 113 | 93.90 38 |
|
Regformer-4 | | | 85.68 47 | 85.45 46 | 86.35 51 | 88.95 127 | 69.67 79 | 88.29 115 | 91.29 103 | 81.73 5 | 85.36 33 | 90.01 110 | 72.62 34 | 95.35 63 | 83.28 42 | 87.57 113 | 94.03 30 |
|
Regformer-1 | | | 86.41 39 | 86.33 35 | 86.64 47 | 89.33 110 | 70.93 59 | 88.43 105 | 91.39 101 | 82.14 3 | 86.65 25 | 90.09 108 | 74.39 20 | 95.01 75 | 83.97 38 | 90.63 79 | 93.97 34 |
|
Regformer-2 | | | 86.63 35 | 86.53 34 | 86.95 40 | 89.33 110 | 71.24 53 | 88.43 105 | 92.05 72 | 82.50 1 | 86.88 23 | 90.09 108 | 74.45 17 | 95.61 49 | 84.38 31 | 90.63 79 | 94.01 32 |
|
HPM-MVS++ | | | 89.02 6 | 89.15 6 | 88.63 1 | 95.01 5 | 76.03 1 | 92.38 19 | 92.85 44 | 80.26 13 | 87.78 20 | 94.27 25 | 75.89 11 | 96.81 14 | 87.45 13 | 96.44 3 | 93.05 76 |
|
test_prior4 | | | | | | | 72.60 31 | 89.01 87 | | | | | | | | | |
|
XVS | | | 87.18 26 | 86.91 29 | 88.00 11 | 94.42 16 | 73.33 17 | 92.78 12 | 92.99 37 | 79.14 21 | 83.67 63 | 94.17 28 | 67.45 78 | 96.60 24 | 83.06 44 | 94.50 42 | 94.07 28 |
|
v1240 | | | 78.99 156 | 77.78 161 | 82.64 162 | 83.21 242 | 63.54 195 | 86.62 162 | 90.30 132 | 69.74 177 | 77.33 144 | 85.68 222 | 57.04 181 | 93.76 128 | 73.13 125 | 76.92 232 | 90.62 144 |
|
test_prior3 | | | 86.73 31 | 86.86 31 | 86.33 52 | 92.61 56 | 69.59 80 | 88.85 92 | 92.97 40 | 75.41 84 | 84.91 39 | 93.54 39 | 74.28 22 | 95.48 53 | 83.31 40 | 95.86 11 | 93.91 36 |
|
pm-mvs1 | | | 77.25 188 | 76.68 182 | 78.93 226 | 84.22 224 | 58.62 247 | 86.41 166 | 88.36 183 | 71.37 150 | 73.31 207 | 88.01 162 | 61.22 144 | 89.15 242 | 64.24 195 | 73.01 276 | 89.03 197 |
|
test_prior2 | | | | | | | | 88.85 92 | | 75.41 84 | 84.91 39 | 93.54 39 | 74.28 22 | | 83.31 40 | 95.86 11 | |
|
X-MVStestdata | | | 80.37 127 | 77.83 159 | 88.00 11 | 94.42 16 | 73.33 17 | 92.78 12 | 92.99 37 | 79.14 21 | 83.67 63 | 12.47 332 | 67.45 78 | 96.60 24 | 83.06 44 | 94.50 42 | 94.07 28 |
|
test_prior | | | | | 86.33 52 | 92.61 56 | 69.59 80 | | 92.97 40 | | | | | 95.48 53 | | | 93.91 36 |
|
旧先验2 | | | | | | | | 86.56 164 | | 58.10 290 | 87.04 22 | | | 88.98 245 | 74.07 116 | | |
|
新几何2 | | | | | | | | 86.29 171 | | | | | | | | | |
|
新几何1 | | | | | 83.42 125 | 93.13 44 | 70.71 63 | | 85.48 221 | 57.43 295 | 81.80 85 | 91.98 66 | 63.28 110 | 92.27 180 | 64.60 194 | 92.99 55 | 87.27 240 |
|
旧先验1 | | | | | | 91.96 64 | 65.79 152 | | 86.37 213 | | | 93.08 53 | 69.31 65 | | | 92.74 59 | 88.74 211 |
|
无先验 | | | | | | | | 87.48 136 | 88.98 168 | 60.00 276 | | | | 94.12 108 | 67.28 169 | | 88.97 201 |
|
原ACMM2 | | | | | | | | 86.86 153 | | | | | | | | | |
|
原ACMM1 | | | | | 84.35 97 | 93.01 48 | 68.79 95 | | 92.44 58 | 63.96 246 | 81.09 95 | 91.57 76 | 66.06 90 | 95.45 55 | 67.19 172 | 94.82 37 | 88.81 208 |
|
test222 | | | | | | 91.50 69 | 68.26 112 | 84.16 221 | 83.20 252 | 54.63 307 | 79.74 104 | 91.63 74 | 58.97 164 | | | 91.42 70 | 86.77 252 |
|
testdata2 | | | | | | | | | | | | | | 91.01 214 | 62.37 209 | | |
|
segment_acmp | | | | | | | | | | | | | 73.08 30 | | | | |
|
testdata | | | | | 79.97 206 | 90.90 77 | 64.21 182 | | 84.71 227 | 59.27 283 | 85.40 32 | 92.91 54 | 62.02 133 | 89.08 243 | 68.95 157 | 91.37 71 | 86.63 256 |
|
testdata1 | | | | | | | | 84.14 222 | | 75.71 79 | | | | | | | |
|
v8 | | | 79.97 135 | 79.02 134 | 82.80 156 | 84.09 226 | 64.50 177 | 87.96 124 | 90.29 133 | 74.13 110 | 75.24 191 | 86.81 188 | 62.88 120 | 93.89 121 | 74.39 113 | 75.40 255 | 90.00 171 |
|
1314 | | | 76.53 196 | 75.30 200 | 80.21 203 | 83.93 230 | 62.32 217 | 84.66 206 | 88.81 172 | 60.23 274 | 70.16 242 | 84.07 244 | 55.30 188 | 90.73 219 | 67.37 168 | 83.21 167 | 87.59 233 |
|
1121 | | | 80.84 110 | 79.77 116 | 84.05 108 | 93.11 46 | 70.78 62 | 84.66 206 | 85.42 222 | 57.37 296 | 81.76 88 | 92.02 65 | 63.41 108 | 94.12 108 | 67.28 169 | 92.93 56 | 87.26 241 |
|
LFMVS | | | 81.82 94 | 81.23 94 | 83.57 122 | 91.89 66 | 63.43 200 | 89.84 65 | 81.85 266 | 77.04 52 | 83.21 66 | 93.10 49 | 52.26 213 | 93.43 142 | 71.98 132 | 89.95 90 | 93.85 40 |
|
VDD-MVS | | | 83.01 79 | 82.36 80 | 84.96 78 | 91.02 75 | 66.40 141 | 88.91 89 | 88.11 185 | 77.57 38 | 84.39 52 | 93.29 46 | 52.19 214 | 93.91 119 | 77.05 93 | 88.70 102 | 94.57 14 |
|
VDDNet | | | 81.52 99 | 80.67 101 | 84.05 108 | 90.44 84 | 64.13 184 | 89.73 71 | 85.91 219 | 71.11 152 | 83.18 67 | 93.48 41 | 50.54 237 | 93.49 139 | 73.40 122 | 88.25 109 | 94.54 15 |
|
v10 | | | 79.74 138 | 78.67 138 | 82.97 148 | 84.06 227 | 64.95 169 | 87.88 129 | 90.62 119 | 73.11 126 | 75.11 194 | 86.56 203 | 61.46 138 | 94.05 111 | 73.68 118 | 75.55 252 | 89.90 176 |
|
VPNet | | | 78.69 161 | 78.66 139 | 78.76 229 | 88.31 150 | 55.72 287 | 84.45 215 | 86.63 209 | 76.79 57 | 78.26 126 | 90.55 100 | 59.30 162 | 89.70 233 | 66.63 176 | 77.05 231 | 90.88 136 |
|
MVS | | | 78.19 172 | 76.99 176 | 81.78 175 | 85.66 202 | 66.99 132 | 84.66 206 | 90.47 124 | 55.08 306 | 72.02 223 | 85.27 231 | 63.83 106 | 94.11 110 | 66.10 180 | 89.80 91 | 84.24 284 |
|
v2v482 | | | 80.23 129 | 79.29 129 | 83.05 143 | 83.62 234 | 64.14 183 | 87.04 147 | 89.97 139 | 73.61 118 | 78.18 129 | 87.22 179 | 61.10 146 | 93.82 122 | 76.11 101 | 76.78 238 | 91.18 128 |
|
V42 | | | 79.38 148 | 78.24 151 | 82.83 153 | 81.10 279 | 65.50 157 | 85.55 189 | 89.82 143 | 71.57 147 | 78.21 127 | 86.12 213 | 60.66 153 | 93.18 151 | 75.64 105 | 75.46 254 | 89.81 181 |
|
SD-MVS | | | 88.06 10 | 88.50 10 | 86.71 46 | 92.60 58 | 72.71 26 | 91.81 31 | 93.19 28 | 77.87 33 | 90.32 9 | 94.00 34 | 74.83 15 | 93.78 125 | 87.63 11 | 94.27 49 | 93.65 52 |
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024 |
GA-MVS | | | 76.87 193 | 75.17 201 | 81.97 173 | 82.75 256 | 62.58 213 | 81.44 251 | 86.35 214 | 72.16 139 | 74.74 199 | 82.89 256 | 46.20 269 | 92.02 187 | 68.85 158 | 81.09 190 | 91.30 126 |
|
MSLP-MVS++ | | | 85.43 50 | 85.76 45 | 84.45 93 | 91.93 65 | 70.24 67 | 90.71 46 | 92.86 43 | 77.46 44 | 84.22 54 | 92.81 59 | 67.16 82 | 92.94 162 | 80.36 66 | 94.35 47 | 90.16 160 |
|
APDe-MVS | | | 89.15 4 | 89.63 4 | 87.73 23 | 94.49 14 | 71.69 47 | 93.83 2 | 93.96 8 | 75.70 80 | 91.06 8 | 96.03 1 | 76.84 7 | 97.03 9 | 89.09 3 | 95.65 19 | 94.47 16 |
|
APD-MVS_3200maxsize | | | 85.97 42 | 85.88 43 | 86.22 55 | 92.69 54 | 69.53 82 | 91.93 29 | 92.99 37 | 73.54 121 | 85.94 27 | 94.51 18 | 65.80 94 | 95.61 49 | 83.04 46 | 92.51 63 | 93.53 59 |
|
ADS-MVSNet2 | | | 66.20 283 | 63.33 284 | 74.82 273 | 79.92 290 | 58.75 246 | 67.55 317 | 75.19 305 | 53.37 309 | 65.25 285 | 75.86 306 | 42.32 289 | 80.53 299 | 41.57 315 | 68.91 295 | 85.18 273 |
|
EI-MVSNet | | | 80.52 124 | 79.98 112 | 82.12 168 | 84.28 222 | 63.19 206 | 86.41 166 | 88.95 170 | 74.18 108 | 78.69 116 | 87.54 171 | 66.62 84 | 92.43 174 | 72.57 129 | 80.57 198 | 90.74 141 |
|
Regformer | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
CVMVSNet | | | 72.99 238 | 72.58 227 | 74.25 278 | 84.28 222 | 50.85 310 | 86.41 166 | 83.45 247 | 44.56 317 | 73.23 209 | 87.54 171 | 49.38 249 | 85.70 276 | 65.90 182 | 78.44 219 | 86.19 261 |
|
pmmvs4 | | | 74.03 227 | 71.91 232 | 80.39 198 | 81.96 266 | 68.32 110 | 81.45 250 | 82.14 262 | 59.32 282 | 69.87 248 | 85.13 233 | 52.40 210 | 88.13 258 | 60.21 228 | 74.74 263 | 84.73 280 |
|
EU-MVSNet | | | 68.53 269 | 67.61 270 | 71.31 291 | 78.51 302 | 47.01 319 | 84.47 212 | 84.27 233 | 42.27 318 | 66.44 278 | 84.79 238 | 40.44 299 | 83.76 285 | 58.76 242 | 68.54 298 | 83.17 293 |
|
VNet | | | 82.21 86 | 82.41 78 | 81.62 178 | 90.82 79 | 60.93 227 | 84.47 212 | 89.78 144 | 76.36 71 | 84.07 57 | 91.88 69 | 64.71 101 | 90.26 223 | 70.68 139 | 88.89 98 | 93.66 47 |
|
test-LLR | | | 72.94 239 | 72.43 228 | 74.48 275 | 81.35 275 | 58.04 253 | 78.38 276 | 77.46 297 | 66.66 212 | 69.95 246 | 79.00 291 | 48.06 257 | 79.24 301 | 66.13 178 | 84.83 146 | 86.15 262 |
|
TESTMET0.1,1 | | | 69.89 262 | 69.00 253 | 72.55 284 | 79.27 300 | 56.85 270 | 78.38 276 | 74.71 309 | 57.64 293 | 68.09 260 | 77.19 302 | 37.75 308 | 76.70 312 | 63.92 196 | 84.09 155 | 84.10 287 |
|
test-mter | | | 71.41 248 | 70.39 247 | 74.48 275 | 81.35 275 | 58.04 253 | 78.38 276 | 77.46 297 | 60.32 273 | 69.95 246 | 79.00 291 | 36.08 313 | 79.24 301 | 66.13 178 | 84.83 146 | 86.15 262 |
|
VPA-MVSNet | | | 80.60 121 | 80.55 103 | 80.76 195 | 88.07 156 | 60.80 230 | 86.86 153 | 91.58 94 | 75.67 81 | 80.24 102 | 89.45 127 | 63.34 109 | 90.25 224 | 70.51 141 | 79.22 215 | 91.23 127 |
|
ACMMPR | | | 87.44 19 | 87.23 23 | 88.08 9 | 94.64 8 | 73.59 9 | 93.04 8 | 93.20 27 | 76.78 58 | 84.66 46 | 94.52 15 | 68.81 69 | 96.65 20 | 84.53 29 | 94.90 32 | 94.00 33 |
|
testgi | | | 66.67 279 | 66.53 275 | 67.08 304 | 75.62 313 | 41.69 325 | 75.93 289 | 76.50 302 | 66.11 219 | 65.20 287 | 86.59 200 | 35.72 314 | 74.71 320 | 43.71 310 | 73.38 275 | 84.84 278 |
|
test20.03 | | | 67.45 274 | 66.95 273 | 68.94 298 | 75.48 314 | 44.84 321 | 77.50 283 | 77.67 296 | 66.66 212 | 63.01 296 | 83.80 247 | 47.02 262 | 78.40 305 | 42.53 314 | 68.86 297 | 83.58 290 |
|
thres600view7 | | | 76.50 197 | 75.44 194 | 79.68 212 | 89.40 107 | 57.16 266 | 85.53 191 | 83.23 250 | 73.79 116 | 76.26 168 | 87.09 184 | 51.89 222 | 91.89 191 | 48.05 296 | 83.72 162 | 90.00 171 |
|
ADS-MVSNet | | | 64.36 287 | 62.88 288 | 68.78 301 | 79.92 290 | 47.17 318 | 67.55 317 | 71.18 314 | 53.37 309 | 65.25 285 | 75.86 306 | 42.32 289 | 73.99 323 | 41.57 315 | 68.91 295 | 85.18 273 |
|
MP-MVS | | | 87.71 15 | 87.64 17 | 87.93 16 | 94.36 20 | 73.88 4 | 92.71 16 | 92.65 53 | 77.57 38 | 83.84 60 | 94.40 24 | 72.24 38 | 96.28 31 | 85.65 20 | 95.30 27 | 93.62 54 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
testmvs | | | 6.04 311 | 8.02 313 | 0.10 322 | 0.08 338 | 0.03 340 | 69.74 309 | 0.04 340 | 0.05 334 | 0.31 336 | 1.68 336 | 0.02 342 | 0.04 337 | 0.24 334 | 0.02 334 | 0.25 334 |
|
thres400 | | | 76.50 197 | 75.37 198 | 79.86 208 | 89.13 122 | 57.65 261 | 85.17 194 | 83.60 241 | 73.41 123 | 76.45 162 | 86.39 208 | 52.12 215 | 91.95 188 | 48.33 291 | 83.75 159 | 90.00 171 |
|
test123 | | | 6.12 310 | 8.11 312 | 0.14 321 | 0.06 339 | 0.09 339 | 71.05 307 | 0.03 341 | 0.04 335 | 0.25 337 | 1.30 337 | 0.05 341 | 0.03 338 | 0.21 335 | 0.01 335 | 0.29 333 |
|
thres200 | | | 75.55 212 | 74.47 208 | 78.82 228 | 87.78 167 | 57.85 258 | 83.07 235 | 83.51 244 | 72.44 134 | 75.84 177 | 84.42 241 | 52.08 217 | 91.75 193 | 47.41 298 | 83.64 163 | 86.86 250 |
|
test0.0.03 1 | | | 68.00 271 | 67.69 269 | 68.90 299 | 77.55 304 | 47.43 317 | 75.70 293 | 72.95 313 | 66.66 212 | 66.56 275 | 82.29 264 | 48.06 257 | 75.87 316 | 44.97 309 | 74.51 265 | 83.41 291 |
|
pmmvs3 | | | 57.79 293 | 54.26 296 | 68.37 302 | 64.02 327 | 56.72 273 | 75.12 297 | 65.17 325 | 40.20 320 | 52.93 319 | 69.86 316 | 20.36 327 | 75.48 318 | 45.45 307 | 55.25 319 | 72.90 319 |
|
EMVS | | | 30.81 304 | 29.65 306 | 34.27 317 | 50.96 332 | 25.95 334 | 56.58 327 | 46.80 333 | 24.01 329 | 15.53 334 | 30.68 330 | 12.47 332 | 54.43 332 | 12.81 332 | 17.05 330 | 22.43 330 |
|
E-PMN | | | 31.77 303 | 30.64 305 | 35.15 316 | 52.87 331 | 27.67 332 | 57.09 326 | 47.86 332 | 24.64 328 | 16.40 333 | 33.05 329 | 11.23 333 | 54.90 331 | 14.46 331 | 18.15 329 | 22.87 329 |
|
PGM-MVS | | | 86.68 33 | 86.27 37 | 87.90 17 | 94.22 24 | 73.38 16 | 90.22 60 | 93.04 31 | 75.53 82 | 83.86 59 | 94.42 23 | 67.87 75 | 96.64 21 | 82.70 49 | 94.57 41 | 93.66 47 |
|
LCM-MVSNet-Re | | | 77.05 189 | 76.94 177 | 77.36 250 | 87.20 183 | 51.60 305 | 80.06 260 | 80.46 279 | 75.20 90 | 67.69 263 | 86.72 191 | 62.48 125 | 88.98 245 | 63.44 199 | 89.25 96 | 91.51 119 |
|
LCM-MVSNet | | | 54.25 295 | 49.68 300 | 67.97 303 | 53.73 330 | 45.28 320 | 66.85 319 | 80.78 273 | 35.96 324 | 39.45 325 | 62.23 321 | 8.70 336 | 78.06 308 | 48.24 294 | 51.20 323 | 80.57 308 |
|
MCST-MVS | | | 87.37 23 | 87.25 22 | 87.73 23 | 94.53 13 | 72.46 36 | 89.82 66 | 93.82 10 | 73.07 127 | 84.86 44 | 92.89 55 | 76.22 9 | 96.33 29 | 84.89 25 | 95.13 28 | 94.40 17 |
|
mvs_anonymous | | | 79.42 145 | 79.11 132 | 80.34 200 | 84.45 221 | 57.97 255 | 82.59 237 | 87.62 195 | 67.40 207 | 76.17 173 | 88.56 147 | 68.47 70 | 89.59 234 | 70.65 140 | 86.05 138 | 93.47 60 |
|
MVS_Test | | | 83.15 75 | 83.06 70 | 83.41 127 | 86.86 187 | 63.21 204 | 86.11 176 | 92.00 76 | 74.31 104 | 82.87 71 | 89.44 128 | 70.03 56 | 93.21 147 | 77.39 90 | 88.50 107 | 93.81 44 |
|
MDA-MVSNet-bldmvs | | | 66.68 278 | 63.66 282 | 75.75 263 | 79.28 299 | 60.56 233 | 73.92 301 | 78.35 292 | 64.43 238 | 50.13 321 | 79.87 286 | 44.02 281 | 83.67 286 | 46.10 304 | 56.86 316 | 83.03 297 |
|
CDPH-MVS | | | 85.76 45 | 85.29 51 | 87.17 37 | 93.49 39 | 71.08 54 | 88.58 103 | 92.42 61 | 68.32 200 | 84.61 47 | 93.48 41 | 72.32 37 | 96.15 36 | 79.00 73 | 95.43 21 | 94.28 22 |
|
test12 | | | | | 86.80 44 | 92.63 55 | 70.70 64 | | 91.79 87 | | 82.71 75 | | 71.67 42 | 96.16 35 | | 94.50 42 | 93.54 58 |
|
casdiffmvs | | | 85.11 56 | 85.14 52 | 85.01 76 | 87.20 183 | 65.77 153 | 87.75 130 | 92.83 45 | 77.84 34 | 84.36 53 | 92.38 61 | 72.15 39 | 93.93 118 | 81.27 58 | 90.48 81 | 95.33 1 |
|
diffmvs | | | 82.10 87 | 81.88 88 | 82.76 161 | 83.00 250 | 63.78 190 | 83.68 228 | 89.76 145 | 72.94 130 | 82.02 81 | 89.85 113 | 65.96 93 | 90.79 217 | 82.38 53 | 87.30 120 | 93.71 46 |
|
baseline2 | | | 75.70 210 | 73.83 216 | 81.30 186 | 83.26 241 | 61.79 223 | 82.57 238 | 80.65 275 | 66.81 209 | 66.88 271 | 83.42 252 | 57.86 170 | 92.19 184 | 63.47 198 | 79.57 208 | 89.91 175 |
|
baseline1 | | | 76.98 191 | 76.75 181 | 77.66 245 | 88.13 153 | 55.66 289 | 85.12 197 | 81.89 264 | 73.04 128 | 76.79 155 | 88.90 136 | 62.43 127 | 87.78 262 | 63.30 201 | 71.18 287 | 89.55 186 |
|
YYNet1 | | | 65.03 284 | 62.91 287 | 71.38 287 | 75.85 311 | 56.60 276 | 69.12 314 | 74.66 310 | 57.28 297 | 54.12 317 | 77.87 298 | 45.85 271 | 74.48 321 | 49.95 284 | 61.52 311 | 83.05 296 |
|
PMMVS2 | | | 40.82 302 | 38.86 304 | 46.69 314 | 53.84 329 | 16.45 336 | 48.61 328 | 49.92 331 | 37.49 323 | 31.67 326 | 60.97 322 | 8.14 337 | 56.42 330 | 28.42 324 | 30.72 326 | 67.19 321 |
|
MDA-MVSNet_test_wron | | | 65.03 284 | 62.92 286 | 71.37 288 | 75.93 310 | 56.73 272 | 69.09 315 | 74.73 308 | 57.28 297 | 54.03 318 | 77.89 297 | 45.88 270 | 74.39 322 | 49.89 285 | 61.55 310 | 82.99 298 |
|
tpmvs | | | 71.09 250 | 69.29 251 | 76.49 259 | 82.04 265 | 56.04 284 | 78.92 273 | 81.37 270 | 64.05 243 | 67.18 269 | 78.28 294 | 49.74 246 | 89.77 230 | 49.67 286 | 72.37 278 | 83.67 289 |
|
PM-MVS | | | 66.41 281 | 64.14 281 | 73.20 282 | 73.92 315 | 56.45 277 | 78.97 272 | 64.96 327 | 63.88 247 | 64.72 288 | 80.24 281 | 19.84 328 | 83.44 288 | 66.24 177 | 64.52 307 | 79.71 311 |
|
HQP_MVS | | | 83.64 67 | 83.14 68 | 85.14 72 | 90.08 91 | 68.71 101 | 91.25 38 | 92.44 58 | 79.12 23 | 78.92 114 | 91.00 93 | 60.42 157 | 95.38 59 | 78.71 76 | 86.32 134 | 91.33 124 |
|
plane_prior7 | | | | | | 90.08 91 | 68.51 108 | | | | | | | | | | |
|
plane_prior6 | | | | | | 89.84 96 | 68.70 103 | | | | | | 60.42 157 | | | | |
|
plane_prior5 | | | | | | | | | 92.44 58 | | | | | 95.38 59 | 78.71 76 | 86.32 134 | 91.33 124 |
|
plane_prior4 | | | | | | | | | | | | 91.00 93 | | | | | |
|
plane_prior3 | | | | | | | 68.60 106 | | | 78.44 30 | 78.92 114 | | | | | | |
|
plane_prior2 | | | | | | | | 91.25 38 | | 79.12 23 | | | | | | | |
|
plane_prior1 | | | | | | 89.90 95 | | | | | | | | | | | |
|
plane_prior | | | | | | | 68.71 101 | 90.38 55 | | 77.62 36 | | | | | | 86.16 137 | |
|
PS-CasMVS | | | 78.01 177 | 78.09 153 | 77.77 244 | 87.71 168 | 54.39 295 | 88.02 122 | 91.22 104 | 77.50 43 | 73.26 208 | 88.64 143 | 60.73 150 | 88.41 255 | 61.88 214 | 73.88 271 | 90.53 150 |
|
UniMVSNet_NR-MVSNet | | | 81.88 92 | 81.54 91 | 82.92 149 | 88.46 145 | 63.46 198 | 87.13 144 | 92.37 62 | 80.19 14 | 78.38 123 | 89.14 131 | 71.66 43 | 93.05 158 | 70.05 145 | 76.46 241 | 92.25 100 |
|
PEN-MVS | | | 77.73 180 | 77.69 165 | 77.84 242 | 87.07 186 | 53.91 297 | 87.91 128 | 91.18 106 | 77.56 40 | 73.14 210 | 88.82 139 | 61.23 143 | 89.17 241 | 59.95 229 | 72.37 278 | 90.43 153 |
|
TransMVSNet (Re) | | | 75.39 216 | 74.56 206 | 77.86 241 | 85.50 206 | 57.10 268 | 86.78 157 | 86.09 218 | 72.17 138 | 71.53 228 | 87.34 174 | 63.01 119 | 89.31 239 | 56.84 258 | 61.83 309 | 87.17 243 |
|
DTE-MVSNet | | | 76.99 190 | 76.80 179 | 77.54 249 | 86.24 196 | 53.06 301 | 87.52 135 | 90.66 118 | 77.08 51 | 72.50 216 | 88.67 142 | 60.48 156 | 89.52 235 | 57.33 255 | 70.74 289 | 90.05 170 |
|
DU-MVS | | | 81.12 106 | 80.52 104 | 82.90 150 | 87.80 164 | 63.46 198 | 87.02 148 | 91.87 84 | 79.01 26 | 78.38 123 | 89.07 133 | 65.02 99 | 93.05 158 | 70.05 145 | 76.46 241 | 92.20 102 |
|
UniMVSNet (Re) | | | 81.60 98 | 81.11 96 | 83.09 140 | 88.38 148 | 64.41 179 | 87.60 133 | 93.02 34 | 78.42 31 | 78.56 119 | 88.16 157 | 69.78 59 | 93.26 146 | 69.58 151 | 76.49 240 | 91.60 116 |
|
CP-MVSNet | | | 78.22 169 | 78.34 148 | 77.84 242 | 87.83 163 | 54.54 293 | 87.94 126 | 91.17 107 | 77.65 35 | 73.48 206 | 88.49 148 | 62.24 131 | 88.43 254 | 62.19 210 | 74.07 267 | 90.55 149 |
|
WR-MVS_H | | | 78.51 164 | 78.49 143 | 78.56 232 | 88.02 158 | 56.38 280 | 88.43 105 | 92.67 51 | 77.14 48 | 73.89 204 | 87.55 170 | 66.25 87 | 89.24 240 | 58.92 239 | 73.55 274 | 90.06 169 |
|
WR-MVS | | | 79.49 142 | 79.22 131 | 80.27 202 | 88.79 135 | 58.35 248 | 85.06 198 | 88.61 180 | 78.56 29 | 77.65 138 | 88.34 152 | 63.81 107 | 90.66 220 | 64.98 191 | 77.22 229 | 91.80 115 |
|
NR-MVSNet | | | 80.23 129 | 79.38 126 | 82.78 159 | 87.80 164 | 63.34 201 | 86.31 169 | 91.09 110 | 79.01 26 | 72.17 221 | 89.07 133 | 67.20 81 | 92.81 168 | 66.08 181 | 75.65 250 | 92.20 102 |
|
Baseline_NR-MVSNet | | | 78.15 173 | 78.33 149 | 77.61 247 | 85.79 200 | 56.21 283 | 86.78 157 | 85.76 220 | 73.60 119 | 77.93 134 | 87.57 169 | 65.02 99 | 88.99 244 | 67.14 173 | 75.33 256 | 87.63 231 |
|
TranMVSNet+NR-MVSNet | | | 80.84 110 | 80.31 108 | 82.42 165 | 87.85 162 | 62.33 216 | 87.74 131 | 91.33 102 | 80.55 11 | 77.99 133 | 89.86 112 | 65.23 97 | 92.62 169 | 67.05 174 | 75.24 259 | 92.30 98 |
|
TSAR-MVS + GP. | | | 85.71 46 | 85.33 48 | 86.84 42 | 91.34 70 | 72.50 33 | 89.07 86 | 87.28 202 | 76.41 66 | 85.80 30 | 90.22 106 | 74.15 25 | 95.37 62 | 81.82 54 | 91.88 65 | 92.65 89 |
|
abl_6 | | | 85.23 53 | 84.95 56 | 86.07 58 | 92.23 61 | 70.48 66 | 90.80 45 | 92.08 71 | 73.51 122 | 85.26 34 | 94.16 29 | 62.75 121 | 95.92 43 | 82.46 52 | 91.30 73 | 91.81 114 |
|
n2 | | | | | | | | | 0.00 342 | | | | | | | | |
|
nn | | | | | | | | | 0.00 342 | | | | | | | | |
|
mPP-MVS | | | 86.67 34 | 86.32 36 | 87.72 25 | 94.41 18 | 73.55 10 | 92.74 14 | 92.22 66 | 76.87 56 | 82.81 74 | 94.25 26 | 66.44 86 | 96.24 32 | 82.88 48 | 94.28 48 | 93.38 62 |
|
door-mid | | | | | | | | | 69.98 317 | | | | | | | | |
|
DI_MVS_plusplus_test | | | 79.89 136 | 78.58 141 | 83.85 118 | 82.89 254 | 65.32 162 | 86.12 175 | 89.55 150 | 69.64 178 | 70.55 234 | 85.82 220 | 57.24 179 | 93.81 123 | 76.85 95 | 88.55 104 | 92.41 95 |
|
XVG-OURS-SEG-HR | | | 80.81 113 | 79.76 117 | 83.96 115 | 85.60 204 | 68.78 96 | 83.54 232 | 90.50 123 | 70.66 160 | 76.71 158 | 91.66 71 | 60.69 152 | 91.26 204 | 76.94 94 | 81.58 186 | 91.83 112 |
|
DWT-MVSNet_test | | | 73.70 229 | 71.86 233 | 79.21 222 | 82.91 253 | 58.94 244 | 82.34 239 | 82.17 261 | 65.21 228 | 71.05 233 | 78.31 293 | 44.21 279 | 90.17 226 | 63.29 202 | 77.28 227 | 88.53 216 |
|
MVSFormer | | | 82.85 80 | 82.05 84 | 85.24 69 | 87.35 176 | 70.21 68 | 90.50 50 | 90.38 126 | 68.55 197 | 81.32 90 | 89.47 123 | 61.68 134 | 93.46 140 | 78.98 74 | 90.26 84 | 92.05 107 |
|
jason | | | 81.39 102 | 80.29 109 | 84.70 87 | 86.63 193 | 69.90 75 | 85.95 179 | 86.77 207 | 63.24 248 | 81.07 96 | 89.47 123 | 61.08 147 | 92.15 185 | 78.33 81 | 90.07 89 | 92.05 107 |
jason: jason. |
lupinMVS | | | 81.39 102 | 80.27 110 | 84.76 86 | 87.35 176 | 70.21 68 | 85.55 189 | 86.41 211 | 62.85 254 | 81.32 90 | 88.61 144 | 61.68 134 | 92.24 183 | 78.41 80 | 90.26 84 | 91.83 112 |
|
test_djsdf | | | 80.30 128 | 79.32 128 | 83.27 131 | 83.98 229 | 65.37 161 | 90.50 50 | 90.38 126 | 68.55 197 | 76.19 170 | 88.70 140 | 56.44 184 | 93.46 140 | 78.98 74 | 80.14 205 | 90.97 134 |
|
HPM-MVS_fast | | | 85.35 52 | 84.95 56 | 86.57 50 | 93.69 34 | 70.58 65 | 92.15 27 | 91.62 92 | 73.89 114 | 82.67 76 | 94.09 32 | 62.60 122 | 95.54 52 | 80.93 60 | 92.93 56 | 93.57 56 |
|
PatchFormer-LS_test | | | 74.50 220 | 73.05 224 | 78.86 227 | 82.95 252 | 59.55 242 | 81.65 247 | 82.30 260 | 67.44 206 | 71.62 227 | 78.15 296 | 52.34 211 | 88.92 249 | 65.05 190 | 75.90 247 | 88.12 222 |
|
K. test v3 | | | 71.19 249 | 68.51 256 | 79.21 222 | 83.04 249 | 57.78 260 | 84.35 219 | 76.91 301 | 72.90 131 | 62.99 297 | 82.86 257 | 39.27 302 | 91.09 212 | 61.65 217 | 52.66 321 | 88.75 210 |
|
lessismore_v0 | | | | | 78.97 225 | 81.01 280 | 57.15 267 | | 65.99 324 | | 61.16 301 | 82.82 258 | 39.12 303 | 91.34 203 | 59.67 231 | 46.92 324 | 88.43 218 |
|
SixPastTwentyTwo | | | 73.37 231 | 71.26 240 | 79.70 211 | 85.08 214 | 57.89 257 | 85.57 185 | 83.56 243 | 71.03 154 | 65.66 281 | 85.88 217 | 42.10 291 | 92.57 171 | 59.11 237 | 63.34 308 | 88.65 213 |
|
OurMVSNet-221017-0 | | | 74.26 223 | 72.42 229 | 79.80 210 | 83.76 233 | 59.59 240 | 85.92 181 | 86.64 208 | 66.39 217 | 66.96 270 | 87.58 168 | 39.46 301 | 91.60 196 | 65.76 184 | 69.27 293 | 88.22 220 |
|
HPM-MVS | | | 87.11 27 | 86.98 27 | 87.50 32 | 93.88 31 | 72.16 41 | 92.19 25 | 93.33 24 | 76.07 76 | 83.81 61 | 93.95 36 | 69.77 60 | 96.01 39 | 85.15 21 | 94.66 38 | 94.32 21 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
XVG-OURS | | | 80.41 125 | 79.23 130 | 83.97 114 | 85.64 203 | 69.02 90 | 83.03 236 | 90.39 125 | 71.09 153 | 77.63 139 | 91.49 79 | 54.62 196 | 91.35 202 | 75.71 104 | 83.47 164 | 91.54 118 |
|
XVG-ACMP-BASELINE | | | 76.11 205 | 74.27 211 | 81.62 178 | 83.20 243 | 64.67 173 | 83.60 231 | 89.75 146 | 69.75 175 | 71.85 224 | 87.09 184 | 32.78 317 | 92.11 186 | 69.99 147 | 80.43 201 | 88.09 223 |
|
LPG-MVS_test | | | 82.08 88 | 81.27 93 | 84.50 91 | 89.23 118 | 68.76 97 | 90.22 60 | 91.94 80 | 75.37 86 | 76.64 160 | 91.51 77 | 54.29 197 | 94.91 78 | 78.44 78 | 83.78 157 | 89.83 179 |
|
LGP-MVS_train | | | | | 84.50 91 | 89.23 118 | 68.76 97 | | 91.94 80 | 75.37 86 | 76.64 160 | 91.51 77 | 54.29 197 | 94.91 78 | 78.44 78 | 83.78 157 | 89.83 179 |
|
baseline | | | 84.93 58 | 84.98 54 | 84.80 85 | 87.30 181 | 65.39 160 | 87.30 141 | 92.88 42 | 77.62 36 | 84.04 58 | 92.26 62 | 71.81 41 | 93.96 112 | 81.31 57 | 90.30 83 | 95.03 4 |
|
test11 | | | | | | | | | 92.23 65 | | | | | | | | |
|
door | | | | | | | | | 69.44 320 | | | | | | | | |
|
EPNet_dtu | | | 75.46 213 | 74.86 202 | 77.23 254 | 82.57 260 | 54.60 292 | 86.89 152 | 83.09 253 | 71.64 143 | 66.25 279 | 85.86 218 | 55.99 185 | 88.04 259 | 54.92 264 | 86.55 131 | 89.05 196 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CHOSEN 1792x2688 | | | 77.63 182 | 75.69 190 | 83.44 124 | 89.98 93 | 68.58 107 | 78.70 275 | 87.50 198 | 56.38 301 | 75.80 178 | 86.84 187 | 58.67 165 | 91.40 201 | 61.58 218 | 85.75 142 | 90.34 156 |
|
EPNet | | | 83.72 66 | 82.92 73 | 86.14 57 | 84.22 224 | 69.48 83 | 91.05 42 | 85.27 223 | 81.30 7 | 76.83 154 | 91.65 72 | 66.09 89 | 95.56 51 | 76.00 103 | 93.85 52 | 93.38 62 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
HQP5-MVS | | | | | | | 66.98 133 | | | | | | | | | | |
|
HQP-NCC | | | | | | 89.33 110 | | 89.17 80 | | 76.41 66 | 77.23 148 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 110 | | 89.17 80 | | 76.41 66 | 77.23 148 | | | | | | |
|
APD-MVS | | | 87.44 19 | 87.52 18 | 87.19 36 | 94.24 23 | 72.39 37 | 91.86 30 | 92.83 45 | 73.01 129 | 88.58 12 | 94.52 15 | 73.36 27 | 96.49 27 | 84.26 33 | 95.01 29 | 92.70 85 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
BP-MVS | | | | | | | | | | | | | | | 77.47 88 | | |
|
HQP4-MVS | | | | | | | | | | | 77.24 147 | | | 95.11 69 | | | 91.03 131 |
|
HQP3-MVS | | | | | | | | | 92.19 68 | | | | | | | 85.99 139 | |
|
HQP2-MVS | | | | | | | | | | | | | 60.17 160 | | | | |
|
CNVR-MVS | | | 88.93 7 | 89.13 7 | 88.33 4 | 94.77 7 | 73.82 6 | 90.51 49 | 93.00 35 | 80.90 9 | 88.06 18 | 94.06 33 | 76.43 8 | 96.84 12 | 88.48 8 | 95.99 9 | 94.34 20 |
|
NCCC | | | 88.06 10 | 88.01 14 | 88.24 7 | 94.41 18 | 73.62 8 | 91.22 40 | 92.83 45 | 81.50 6 | 85.79 31 | 93.47 43 | 73.02 32 | 97.00 11 | 84.90 23 | 94.94 31 | 94.10 26 |
|
114514_t | | | 80.68 119 | 79.51 122 | 84.20 102 | 94.09 30 | 67.27 129 | 89.64 73 | 91.11 109 | 58.75 288 | 74.08 203 | 90.72 97 | 58.10 168 | 95.04 74 | 69.70 149 | 89.42 95 | 90.30 157 |
|
CP-MVS | | | 87.11 27 | 86.92 28 | 87.68 29 | 94.20 25 | 73.86 5 | 93.98 1 | 92.82 48 | 76.62 63 | 83.68 62 | 94.46 19 | 67.93 73 | 95.95 42 | 84.20 35 | 94.39 45 | 93.23 68 |
|
DSMNet-mixed | | | 57.77 294 | 56.90 295 | 60.38 308 | 67.70 325 | 35.61 328 | 69.18 312 | 53.97 330 | 32.30 327 | 57.49 312 | 79.88 285 | 40.39 300 | 68.57 327 | 38.78 319 | 72.37 278 | 76.97 316 |
|
tpm2 | | | 73.26 234 | 71.46 236 | 78.63 230 | 83.34 239 | 56.71 274 | 80.65 255 | 80.40 280 | 56.63 300 | 73.55 205 | 82.02 267 | 51.80 224 | 91.24 205 | 56.35 260 | 78.42 220 | 87.95 224 |
|
NP-MVS | | | | | | 89.62 99 | 68.32 110 | | | | | 90.24 104 | | | | | |
|
EG-PatchMatch MVS | | | 74.04 226 | 71.82 234 | 80.71 196 | 84.92 215 | 67.42 125 | 85.86 182 | 88.08 188 | 66.04 221 | 64.22 291 | 83.85 245 | 35.10 315 | 92.56 172 | 57.44 253 | 80.83 193 | 82.16 302 |
|
tpm cat1 | | | 70.57 254 | 68.31 258 | 77.35 251 | 82.41 262 | 57.95 256 | 78.08 280 | 80.22 283 | 52.04 312 | 68.54 259 | 77.66 300 | 52.00 219 | 87.84 261 | 51.77 274 | 72.07 282 | 86.25 259 |
|
SteuartSystems-ACMMP | | | 88.72 8 | 88.86 8 | 88.32 5 | 92.14 62 | 72.96 22 | 93.73 3 | 93.67 12 | 80.19 14 | 88.10 17 | 94.80 9 | 73.76 26 | 97.11 7 | 87.51 12 | 95.82 14 | 94.90 6 |
Skip Steuart: Steuart Systems R&D Blog. |
CostFormer | | | 75.24 217 | 73.90 214 | 79.27 220 | 82.65 259 | 58.27 250 | 80.80 252 | 82.73 257 | 61.57 265 | 75.33 189 | 83.13 254 | 55.52 186 | 91.07 213 | 64.98 191 | 78.34 221 | 88.45 217 |
|
CR-MVSNet | | | 73.37 231 | 71.27 239 | 79.67 213 | 81.32 277 | 65.19 165 | 75.92 290 | 80.30 281 | 59.92 277 | 72.73 214 | 81.19 270 | 52.50 208 | 86.69 268 | 59.84 230 | 77.71 223 | 87.11 246 |
|
JIA-IIPM | | | 66.32 282 | 62.82 289 | 76.82 257 | 77.09 308 | 61.72 224 | 65.34 320 | 75.38 304 | 58.04 291 | 64.51 289 | 62.32 320 | 42.05 292 | 86.51 271 | 51.45 277 | 69.22 294 | 82.21 301 |
|
Patchmtry | | | 70.74 252 | 69.16 252 | 75.49 268 | 80.72 281 | 54.07 296 | 74.94 299 | 80.30 281 | 58.34 289 | 70.01 243 | 81.19 270 | 52.50 208 | 86.54 270 | 53.37 270 | 71.09 288 | 85.87 269 |
|
PatchT | | | 68.46 270 | 67.85 265 | 70.29 294 | 80.70 282 | 43.93 322 | 72.47 303 | 74.88 306 | 60.15 275 | 70.55 234 | 76.57 304 | 49.94 243 | 81.59 295 | 50.58 279 | 74.83 262 | 85.34 272 |
|
tpmrst | | | 72.39 242 | 72.13 231 | 73.18 283 | 80.54 284 | 49.91 313 | 79.91 263 | 79.08 290 | 63.11 249 | 71.69 226 | 79.95 284 | 55.32 187 | 82.77 292 | 65.66 185 | 73.89 270 | 86.87 249 |
|
BH-w/o | | | 78.21 170 | 77.33 171 | 80.84 193 | 88.81 133 | 65.13 167 | 84.87 202 | 87.85 192 | 69.75 175 | 74.52 201 | 84.74 239 | 61.34 140 | 93.11 155 | 58.24 247 | 85.84 141 | 84.27 283 |
|
tpm | | | 72.37 244 | 71.71 235 | 74.35 277 | 82.19 264 | 52.00 302 | 79.22 269 | 77.29 299 | 64.56 237 | 72.95 212 | 83.68 250 | 51.35 227 | 83.26 290 | 58.33 246 | 75.80 248 | 87.81 228 |
|
DELS-MVS | | | 85.41 51 | 85.30 50 | 85.77 61 | 88.49 143 | 67.93 118 | 85.52 193 | 93.44 20 | 78.70 28 | 83.63 65 | 89.03 135 | 74.57 16 | 95.71 48 | 80.26 68 | 94.04 51 | 93.66 47 |
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 |
BH-untuned | | | 79.47 143 | 78.60 140 | 82.05 170 | 89.19 120 | 65.91 149 | 86.07 177 | 88.52 181 | 72.18 137 | 75.42 183 | 87.69 166 | 61.15 145 | 93.54 137 | 60.38 226 | 86.83 127 | 86.70 254 |
|
RPMNet | | | 71.62 246 | 68.94 254 | 79.67 213 | 81.32 277 | 65.19 165 | 75.92 290 | 78.30 293 | 57.60 294 | 72.73 214 | 76.45 305 | 52.30 212 | 86.69 268 | 48.14 295 | 77.71 223 | 87.11 246 |
|
MVSTER | | | 79.01 155 | 77.88 158 | 82.38 166 | 83.07 247 | 64.80 171 | 84.08 225 | 88.95 170 | 69.01 191 | 78.69 116 | 87.17 182 | 54.70 194 | 92.43 174 | 74.69 111 | 80.57 198 | 89.89 177 |
|
CPTT-MVS | | | 83.73 65 | 83.33 67 | 84.92 81 | 93.28 41 | 70.86 61 | 92.09 28 | 90.38 126 | 68.75 194 | 79.57 106 | 92.83 57 | 60.60 155 | 93.04 160 | 80.92 61 | 91.56 69 | 90.86 137 |
|
GBi-Net | | | 78.40 165 | 77.40 169 | 81.40 183 | 87.60 171 | 63.01 208 | 88.39 109 | 89.28 157 | 71.63 144 | 75.34 186 | 87.28 175 | 54.80 190 | 91.11 207 | 62.72 204 | 79.57 208 | 90.09 165 |
|
PVSNet_Blended_VisFu | | | 82.62 82 | 81.83 89 | 84.96 78 | 90.80 80 | 69.76 77 | 88.74 98 | 91.70 91 | 69.39 180 | 78.96 112 | 88.46 149 | 65.47 95 | 94.87 83 | 74.42 112 | 88.57 103 | 90.24 158 |
|
PVSNet_BlendedMVS | | | 80.60 121 | 80.02 111 | 82.36 167 | 88.85 129 | 65.40 158 | 86.16 174 | 92.00 76 | 69.34 182 | 78.11 130 | 86.09 214 | 66.02 91 | 94.27 99 | 71.52 134 | 82.06 181 | 87.39 236 |
|
UnsupCasMVSNet_eth | | | 67.33 275 | 65.99 276 | 71.37 288 | 73.48 317 | 51.47 307 | 75.16 295 | 85.19 224 | 65.20 229 | 60.78 302 | 80.93 277 | 42.35 288 | 77.20 311 | 57.12 256 | 53.69 320 | 85.44 271 |
|
UnsupCasMVSNet_bld | | | 63.70 289 | 61.53 292 | 70.21 295 | 73.69 316 | 51.39 308 | 72.82 302 | 81.89 264 | 55.63 304 | 57.81 311 | 71.80 314 | 38.67 304 | 78.61 304 | 49.26 288 | 52.21 322 | 80.63 307 |
|
PVSNet_Blended | | | 80.98 107 | 80.34 107 | 82.90 150 | 88.85 129 | 65.40 158 | 84.43 216 | 92.00 76 | 67.62 203 | 78.11 130 | 85.05 236 | 66.02 91 | 94.27 99 | 71.52 134 | 89.50 93 | 89.01 198 |
|
FMVSNet5 | | | 69.50 263 | 67.96 263 | 74.15 279 | 82.97 251 | 55.35 290 | 80.01 261 | 82.12 263 | 62.56 258 | 63.02 295 | 81.53 269 | 36.92 310 | 81.92 294 | 48.42 290 | 74.06 268 | 85.17 275 |
|
test1 | | | 78.40 165 | 77.40 169 | 81.40 183 | 87.60 171 | 63.01 208 | 88.39 109 | 89.28 157 | 71.63 144 | 75.34 186 | 87.28 175 | 54.80 190 | 91.11 207 | 62.72 204 | 79.57 208 | 90.09 165 |
|
new_pmnet | | | 50.91 298 | 50.29 299 | 52.78 312 | 68.58 324 | 34.94 330 | 63.71 322 | 56.63 329 | 39.73 321 | 44.95 323 | 65.47 318 | 21.93 326 | 58.48 329 | 34.98 322 | 56.62 317 | 64.92 322 |
|
FMVSNet3 | | | 77.88 179 | 76.85 178 | 80.97 192 | 86.84 189 | 62.36 215 | 86.52 165 | 88.77 174 | 71.13 151 | 75.34 186 | 86.66 198 | 54.07 200 | 91.10 210 | 62.72 204 | 79.57 208 | 89.45 187 |
|
dp | | | 66.80 277 | 65.43 277 | 70.90 293 | 79.74 295 | 48.82 316 | 75.12 297 | 74.77 307 | 59.61 279 | 64.08 292 | 77.23 301 | 42.89 285 | 80.72 298 | 48.86 289 | 66.58 302 | 83.16 294 |
|
FMVSNet2 | | | 78.20 171 | 77.21 172 | 81.20 187 | 87.60 171 | 62.89 212 | 87.47 137 | 89.02 166 | 71.63 144 | 75.29 190 | 87.28 175 | 54.80 190 | 91.10 210 | 62.38 208 | 79.38 212 | 89.61 183 |
|
FMVSNet1 | | | 77.44 184 | 76.12 189 | 81.40 183 | 86.81 190 | 63.01 208 | 88.39 109 | 89.28 157 | 70.49 163 | 74.39 202 | 87.28 175 | 49.06 254 | 91.11 207 | 60.91 223 | 78.52 217 | 90.09 165 |
|
N_pmnet | | | 52.79 297 | 53.26 297 | 51.40 313 | 78.99 301 | 7.68 338 | 69.52 310 | 3.89 338 | 51.63 314 | 57.01 313 | 74.98 309 | 40.83 297 | 65.96 328 | 37.78 320 | 64.67 306 | 80.56 309 |
|
cascas | | | 76.72 195 | 74.64 204 | 82.99 146 | 85.78 201 | 65.88 150 | 82.33 240 | 89.21 162 | 60.85 270 | 72.74 213 | 81.02 274 | 47.28 261 | 93.75 129 | 67.48 167 | 85.02 144 | 89.34 188 |
|
BH-RMVSNet | | | 79.61 139 | 78.44 145 | 83.14 138 | 89.38 109 | 65.93 148 | 84.95 201 | 87.15 203 | 73.56 120 | 78.19 128 | 89.79 114 | 56.67 183 | 93.36 143 | 59.53 233 | 86.74 128 | 90.13 162 |
|
UGNet | | | 80.83 112 | 79.59 121 | 84.54 90 | 88.04 157 | 68.09 115 | 89.42 75 | 88.16 184 | 76.95 53 | 76.22 169 | 89.46 125 | 49.30 251 | 93.94 115 | 68.48 159 | 90.31 82 | 91.60 116 |
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 |
WTY-MVS | | | 75.65 211 | 75.68 191 | 75.57 266 | 86.40 195 | 56.82 271 | 77.92 282 | 82.40 259 | 65.10 230 | 76.18 171 | 87.72 164 | 63.13 118 | 80.90 297 | 60.31 227 | 81.96 182 | 89.00 200 |
|
XXY-MVS | | | 75.41 215 | 75.56 192 | 74.96 271 | 83.59 235 | 57.82 259 | 80.59 256 | 83.87 239 | 66.54 216 | 74.93 198 | 88.31 153 | 63.24 112 | 80.09 300 | 62.16 211 | 76.85 235 | 86.97 248 |
|
sss | | | 73.60 230 | 73.64 217 | 73.51 281 | 82.80 255 | 55.01 291 | 76.12 288 | 81.69 267 | 62.47 259 | 74.68 200 | 85.85 219 | 57.32 176 | 78.11 307 | 60.86 224 | 80.93 191 | 87.39 236 |
|
Test_1112_low_res | | | 76.40 201 | 75.44 194 | 79.27 220 | 89.28 116 | 58.09 251 | 81.69 246 | 87.07 204 | 59.53 281 | 72.48 217 | 86.67 197 | 61.30 141 | 89.33 238 | 60.81 225 | 80.15 204 | 90.41 154 |
|
1112_ss | | | 77.40 186 | 76.43 185 | 80.32 201 | 89.11 126 | 60.41 234 | 83.65 229 | 87.72 194 | 62.13 262 | 73.05 211 | 86.72 191 | 62.58 124 | 89.97 228 | 62.11 213 | 80.80 194 | 90.59 148 |
|
ab-mvs-re | | | 7.23 309 | 9.64 311 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 86.72 191 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
ab-mvs | | | 79.51 141 | 78.97 135 | 81.14 189 | 88.46 145 | 60.91 228 | 83.84 226 | 89.24 161 | 70.36 164 | 79.03 111 | 88.87 138 | 63.23 113 | 90.21 225 | 65.12 188 | 82.57 177 | 92.28 99 |
|
TR-MVS | | | 77.44 184 | 76.18 188 | 81.20 187 | 88.24 151 | 63.24 203 | 84.61 210 | 86.40 212 | 67.55 204 | 77.81 135 | 86.48 206 | 54.10 199 | 93.15 152 | 57.75 251 | 82.72 175 | 87.20 242 |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 327 | 75.16 295 | | 55.10 305 | 66.53 276 | | 49.34 250 | | 53.98 267 | | 87.94 225 |
|
MDTV_nov1_ep13 | | | | 69.97 249 | | 83.18 244 | 53.48 299 | 77.10 286 | 80.18 284 | 60.45 271 | 69.33 254 | 80.44 279 | 48.89 255 | 86.90 267 | 51.60 276 | 78.51 218 | |
|
MIMVSNet1 | | | 68.58 268 | 66.78 274 | 73.98 280 | 80.07 289 | 51.82 303 | 80.77 253 | 84.37 231 | 64.40 239 | 59.75 307 | 82.16 266 | 36.47 311 | 83.63 287 | 42.73 313 | 70.33 290 | 86.48 257 |
|
MIMVSNet | | | 70.69 253 | 69.30 250 | 74.88 272 | 84.52 219 | 56.35 281 | 75.87 292 | 79.42 288 | 64.59 236 | 67.76 261 | 82.41 262 | 41.10 295 | 81.54 296 | 46.64 302 | 81.34 187 | 86.75 253 |
|
IterMVS-LS | | | 80.06 132 | 79.38 126 | 82.11 169 | 85.89 199 | 63.20 205 | 86.79 156 | 89.34 155 | 74.19 107 | 75.45 182 | 86.72 191 | 66.62 84 | 92.39 176 | 72.58 128 | 76.86 234 | 90.75 140 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 79.07 154 | 77.70 164 | 83.17 137 | 87.60 171 | 68.23 113 | 84.40 218 | 86.20 215 | 67.49 205 | 76.36 166 | 86.54 204 | 61.54 137 | 90.79 217 | 61.86 215 | 87.33 119 | 90.49 151 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 183 | |
|
IterMVS | | | 74.29 222 | 72.94 225 | 78.35 236 | 81.53 271 | 63.49 197 | 81.58 248 | 82.49 258 | 68.06 201 | 69.99 245 | 83.69 249 | 51.66 226 | 85.54 277 | 65.85 183 | 71.64 284 | 86.01 266 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DP-MVS Recon | | | 83.11 77 | 82.09 83 | 86.15 56 | 94.44 15 | 70.92 60 | 88.79 94 | 92.20 67 | 70.53 162 | 79.17 110 | 91.03 92 | 64.12 104 | 96.03 37 | 68.39 161 | 90.14 86 | 91.50 120 |
|
MVS_111021_LR | | | 82.61 83 | 82.11 82 | 84.11 104 | 88.82 132 | 71.58 48 | 85.15 196 | 86.16 216 | 74.69 98 | 80.47 101 | 91.04 90 | 62.29 129 | 90.55 221 | 80.33 67 | 90.08 88 | 90.20 159 |
|
DP-MVS | | | 76.78 194 | 74.57 205 | 83.42 125 | 93.29 40 | 69.46 86 | 88.55 104 | 83.70 240 | 63.98 245 | 70.20 239 | 88.89 137 | 54.01 201 | 94.80 85 | 46.66 300 | 81.88 184 | 86.01 266 |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 188 | |
|
HQP-MVS | | | 82.61 83 | 82.02 85 | 84.37 95 | 89.33 110 | 66.98 133 | 89.17 80 | 92.19 68 | 76.41 66 | 77.23 148 | 90.23 105 | 60.17 160 | 95.11 69 | 77.47 88 | 85.99 139 | 91.03 131 |
|
QAPM | | | 80.88 108 | 79.50 123 | 85.03 75 | 88.01 159 | 68.97 93 | 91.59 32 | 92.00 76 | 66.63 215 | 75.15 193 | 92.16 63 | 57.70 171 | 95.45 55 | 63.52 197 | 88.76 101 | 90.66 143 |
|
Vis-MVSNet | | | 83.46 70 | 82.80 75 | 85.43 65 | 90.25 87 | 68.74 99 | 90.30 57 | 90.13 136 | 76.33 72 | 80.87 98 | 92.89 55 | 61.00 148 | 94.20 104 | 72.45 130 | 90.97 75 | 93.35 64 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MVS-HIRNet | | | 59.14 292 | 57.67 294 | 63.57 306 | 81.65 269 | 43.50 323 | 71.73 305 | 65.06 326 | 39.59 322 | 51.43 320 | 57.73 323 | 38.34 306 | 82.58 293 | 39.53 318 | 73.95 269 | 64.62 323 |
|
IS-MVSNet | | | 83.15 75 | 82.81 74 | 84.18 103 | 89.94 94 | 63.30 202 | 91.59 32 | 88.46 182 | 79.04 25 | 79.49 107 | 92.16 63 | 65.10 98 | 94.28 98 | 67.71 164 | 91.86 66 | 94.95 5 |
|
HyFIR lowres test | | | 77.53 183 | 75.40 196 | 83.94 116 | 89.59 100 | 66.62 137 | 80.36 257 | 88.64 179 | 56.29 302 | 76.45 162 | 85.17 232 | 57.64 172 | 93.28 145 | 61.34 221 | 83.10 170 | 91.91 110 |
|
EPMVS | | | 69.02 266 | 68.16 260 | 71.59 286 | 79.61 296 | 49.80 315 | 77.40 284 | 66.93 323 | 62.82 255 | 70.01 243 | 79.05 289 | 45.79 272 | 77.86 309 | 56.58 259 | 75.26 258 | 87.13 245 |
|
PAPM_NR | | | 83.02 78 | 82.41 78 | 84.82 83 | 92.47 59 | 66.37 142 | 87.93 127 | 91.80 86 | 73.82 115 | 77.32 145 | 90.66 98 | 67.90 74 | 94.90 80 | 70.37 142 | 89.48 94 | 93.19 72 |
|
TAMVS | | | 78.89 159 | 77.51 168 | 83.03 144 | 87.80 164 | 67.79 121 | 84.72 205 | 85.05 226 | 67.63 202 | 76.75 157 | 87.70 165 | 62.25 130 | 90.82 216 | 58.53 244 | 87.13 122 | 90.49 151 |
|
PAPR | | | 81.66 97 | 80.89 99 | 83.99 113 | 90.27 86 | 64.00 185 | 86.76 159 | 91.77 90 | 68.84 193 | 77.13 152 | 89.50 121 | 67.63 76 | 94.88 82 | 67.55 166 | 88.52 106 | 93.09 74 |
|
RPSCF | | | 73.23 235 | 71.46 236 | 78.54 233 | 82.50 261 | 59.85 237 | 82.18 241 | 82.84 256 | 58.96 285 | 71.15 232 | 89.41 129 | 45.48 276 | 84.77 282 | 58.82 241 | 71.83 283 | 91.02 133 |
|
Vis-MVSNet (Re-imp) | | | 78.36 167 | 78.45 144 | 78.07 240 | 88.64 139 | 51.78 304 | 86.70 160 | 79.63 287 | 74.14 109 | 75.11 194 | 90.83 96 | 61.29 142 | 89.75 231 | 58.10 248 | 91.60 67 | 92.69 87 |
|
test_0402 | | | 72.79 240 | 70.44 245 | 79.84 209 | 88.13 153 | 65.99 147 | 85.93 180 | 84.29 232 | 65.57 227 | 67.40 267 | 85.49 227 | 46.92 263 | 92.61 170 | 35.88 321 | 74.38 266 | 80.94 306 |
|
MVS_111021_HR | | | 85.14 55 | 84.75 58 | 86.32 54 | 91.65 68 | 72.70 27 | 85.98 178 | 90.33 130 | 76.11 75 | 82.08 80 | 91.61 75 | 71.36 45 | 94.17 107 | 81.02 59 | 92.58 62 | 92.08 106 |
|
CSCG | | | 86.41 39 | 86.19 39 | 87.07 39 | 92.91 49 | 72.48 34 | 90.81 44 | 93.56 15 | 73.95 111 | 83.16 68 | 91.07 89 | 75.94 10 | 95.19 66 | 79.94 70 | 94.38 46 | 93.55 57 |
|
PatchMatch-RL | | | 72.38 243 | 70.90 242 | 76.80 258 | 88.60 140 | 67.38 127 | 79.53 265 | 76.17 303 | 62.75 256 | 69.36 253 | 82.00 268 | 45.51 275 | 84.89 281 | 53.62 269 | 80.58 197 | 78.12 314 |
|
API-MVS | | | 81.99 91 | 81.23 94 | 84.26 101 | 90.94 76 | 70.18 73 | 91.10 41 | 89.32 156 | 71.51 148 | 78.66 118 | 88.28 154 | 65.26 96 | 95.10 72 | 64.74 193 | 91.23 74 | 87.51 234 |
|
Test By Simon | | | | | | | | | | | | | 64.33 102 | | | | |
|
TDRefinement | | | 67.49 272 | 64.34 280 | 76.92 256 | 73.47 318 | 61.07 226 | 84.86 203 | 82.98 254 | 59.77 278 | 58.30 310 | 85.13 233 | 26.06 323 | 87.89 260 | 47.92 297 | 60.59 313 | 81.81 304 |
|
USDC | | | 70.33 257 | 68.37 257 | 76.21 261 | 80.60 283 | 56.23 282 | 79.19 270 | 86.49 210 | 60.89 269 | 61.29 300 | 85.47 228 | 31.78 321 | 89.47 237 | 53.37 270 | 76.21 244 | 82.94 299 |
|
EPP-MVSNet | | | 83.40 72 | 83.02 71 | 84.57 89 | 90.13 89 | 64.47 178 | 92.32 22 | 90.73 117 | 74.45 103 | 79.35 109 | 91.10 87 | 69.05 68 | 95.12 68 | 72.78 127 | 87.22 121 | 94.13 25 |
|
PMMVS | | | 69.34 264 | 68.67 255 | 71.35 290 | 75.67 312 | 62.03 220 | 75.17 294 | 73.46 311 | 50.00 315 | 68.68 256 | 79.05 289 | 52.07 218 | 78.13 306 | 61.16 222 | 82.77 173 | 73.90 318 |
|
PAPM | | | 77.68 181 | 76.40 186 | 81.51 180 | 87.29 182 | 61.85 222 | 83.78 227 | 89.59 149 | 64.74 235 | 71.23 230 | 88.70 140 | 62.59 123 | 93.66 132 | 52.66 273 | 87.03 124 | 89.01 198 |
|
ACMMP | | | 85.89 44 | 85.39 47 | 87.38 33 | 93.59 37 | 72.63 30 | 92.74 14 | 93.18 29 | 76.78 58 | 80.73 99 | 93.82 38 | 64.33 102 | 96.29 30 | 82.67 50 | 90.69 78 | 93.23 68 |
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 |
CNLPA | | | 78.08 174 | 76.79 180 | 81.97 173 | 90.40 85 | 71.07 55 | 87.59 134 | 84.55 230 | 66.03 222 | 72.38 219 | 89.64 117 | 57.56 173 | 86.04 274 | 59.61 232 | 83.35 165 | 88.79 209 |
|
PatchmatchNet | | | 73.12 236 | 71.33 238 | 78.49 235 | 83.18 244 | 60.85 229 | 79.63 264 | 78.57 291 | 64.13 242 | 71.73 225 | 79.81 287 | 51.20 229 | 85.97 275 | 57.40 254 | 76.36 243 | 88.66 212 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PHI-MVS | | | 86.43 37 | 86.17 40 | 87.24 35 | 90.88 78 | 70.96 56 | 92.27 23 | 94.07 7 | 72.45 132 | 85.22 35 | 91.90 68 | 69.47 62 | 96.42 28 | 83.28 42 | 95.94 10 | 94.35 19 |
|
F-COLMAP | | | 76.38 202 | 74.33 210 | 82.50 164 | 89.28 116 | 66.95 136 | 88.41 108 | 89.03 165 | 64.05 243 | 66.83 273 | 88.61 144 | 46.78 264 | 92.89 163 | 57.48 252 | 78.55 216 | 87.67 230 |
|
ANet_high | | | 50.57 299 | 46.10 301 | 63.99 305 | 48.67 333 | 39.13 326 | 70.99 308 | 80.85 272 | 61.39 267 | 31.18 327 | 57.70 324 | 17.02 330 | 73.65 324 | 31.22 323 | 15.89 331 | 79.18 312 |
|
wuyk23d | | | 16.82 308 | 15.94 310 | 19.46 319 | 58.74 328 | 31.45 331 | 39.22 330 | 3.74 339 | 6.84 332 | 6.04 335 | 2.70 335 | 1.27 340 | 24.29 335 | 10.54 333 | 14.40 333 | 2.63 332 |
|
OMC-MVS | | | 82.69 81 | 81.97 87 | 84.85 82 | 88.75 137 | 67.42 125 | 87.98 123 | 90.87 114 | 74.92 94 | 79.72 105 | 91.65 72 | 62.19 132 | 93.96 112 | 75.26 109 | 86.42 133 | 93.16 73 |
|
MG-MVS | | | 83.41 71 | 83.45 65 | 83.28 130 | 92.74 53 | 62.28 218 | 88.17 120 | 89.50 152 | 75.22 89 | 81.49 89 | 92.74 60 | 66.75 83 | 95.11 69 | 72.85 126 | 91.58 68 | 92.45 93 |
|
AdaColmap | | | 80.58 123 | 79.42 124 | 84.06 107 | 93.09 47 | 68.91 94 | 89.36 76 | 88.97 169 | 69.27 183 | 75.70 179 | 89.69 115 | 57.20 180 | 95.77 46 | 63.06 203 | 88.41 108 | 87.50 235 |
|
uanet | | | 0.00 313 | 0.00 315 | 0.00 323 | 0.00 340 | 0.00 341 | 0.00 334 | 0.00 342 | 0.00 336 | 0.00 338 | 0.00 338 | 0.00 343 | 0.00 339 | 0.00 336 | 0.00 336 | 0.00 335 |
|
ITE_SJBPF | | | | | 78.22 237 | 81.77 268 | 60.57 232 | | 83.30 248 | 69.25 184 | 67.54 264 | 87.20 180 | 36.33 312 | 87.28 266 | 54.34 266 | 74.62 264 | 86.80 251 |
|
DeepMVS_CX | | | | | 27.40 318 | 40.17 336 | 26.90 333 | | 24.59 337 | 17.44 331 | 23.95 330 | 48.61 326 | 9.77 334 | 26.48 334 | 18.06 328 | 24.47 327 | 28.83 328 |
|
TinyColmap | | | 67.30 276 | 64.81 278 | 74.76 274 | 81.92 267 | 56.68 275 | 80.29 259 | 81.49 269 | 60.33 272 | 56.27 316 | 83.22 253 | 24.77 324 | 87.66 263 | 45.52 306 | 69.47 292 | 79.95 310 |
|
MAR-MVS | | | 81.84 93 | 80.70 100 | 85.27 68 | 91.32 71 | 71.53 49 | 89.82 66 | 90.92 112 | 69.77 174 | 78.50 120 | 86.21 211 | 62.36 128 | 94.52 93 | 65.36 186 | 92.05 64 | 89.77 182 |
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 |
LF4IMVS | | | 64.02 288 | 62.19 290 | 69.50 297 | 70.90 323 | 53.29 300 | 76.13 287 | 77.18 300 | 52.65 311 | 58.59 308 | 80.98 275 | 23.55 325 | 76.52 313 | 53.06 272 | 66.66 301 | 78.68 313 |
|
MSDG | | | 73.36 233 | 70.99 241 | 80.49 197 | 84.51 220 | 65.80 151 | 80.71 254 | 86.13 217 | 65.70 225 | 65.46 282 | 83.74 248 | 44.60 277 | 90.91 215 | 51.13 278 | 76.89 233 | 84.74 279 |
|
LS3D | | | 76.95 192 | 74.82 203 | 83.37 128 | 90.45 83 | 67.36 128 | 89.15 84 | 86.94 205 | 61.87 264 | 69.52 251 | 90.61 99 | 51.71 225 | 94.53 92 | 46.38 303 | 86.71 129 | 88.21 221 |
|
CLD-MVS | | | 82.31 85 | 81.65 90 | 84.29 100 | 88.47 144 | 67.73 122 | 85.81 184 | 92.35 63 | 75.78 78 | 78.33 125 | 86.58 202 | 64.01 105 | 94.35 96 | 76.05 102 | 87.48 118 | 90.79 138 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
FPMVS | | | 53.68 296 | 51.64 298 | 59.81 309 | 65.08 326 | 51.03 309 | 69.48 311 | 69.58 319 | 41.46 319 | 40.67 324 | 72.32 313 | 16.46 331 | 70.00 326 | 24.24 326 | 65.42 304 | 58.40 324 |
|
Gipuma | | | 45.18 300 | 41.86 302 | 55.16 311 | 77.03 309 | 51.52 306 | 32.50 332 | 80.52 277 | 32.46 326 | 27.12 328 | 35.02 328 | 9.52 335 | 75.50 317 | 22.31 327 | 60.21 314 | 38.45 327 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |