DeepPCF-MVS | | 80.84 1 | 88.10 8 | 88.56 8 | 86.73 41 | 92.24 54 | 69.03 83 | 89.57 67 | 93.39 17 | 77.53 39 | 89.79 8 | 94.12 25 | 78.98 2 | 96.58 22 | 85.66 14 | 95.72 11 | 94.58 8 |
|
DeepC-MVS | | 79.81 2 | 87.08 26 | 86.88 26 | 87.69 26 | 91.16 66 | 72.32 38 | 90.31 48 | 93.94 6 | 77.12 44 | 82.82 64 | 94.23 21 | 72.13 34 | 97.09 6 | 84.83 22 | 95.37 18 | 93.65 48 |
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 27 | 86.62 28 | 87.76 21 | 93.52 32 | 72.37 37 | 91.26 30 | 93.04 25 | 76.62 57 | 84.22 48 | 93.36 38 | 71.44 37 | 96.76 12 | 80.82 56 | 95.33 21 | 94.16 21 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
3Dnovator+ | | 77.84 4 | 85.48 46 | 84.47 54 | 88.51 3 | 91.08 67 | 73.49 14 | 93.18 4 | 93.78 8 | 80.79 11 | 76.66 153 | 93.37 37 | 60.40 165 | 96.75 13 | 77.20 84 | 93.73 47 | 95.29 1 |
|
3Dnovator | | 76.31 5 | 83.38 68 | 82.31 75 | 86.59 45 | 87.94 156 | 72.94 23 | 90.64 40 | 92.14 61 | 77.21 42 | 75.47 179 | 92.83 50 | 58.56 172 | 94.72 79 | 73.24 125 | 92.71 53 | 92.13 96 |
|
ACMP | | 74.13 6 | 81.51 95 | 80.57 96 | 84.36 88 | 89.42 104 | 68.69 99 | 89.97 55 | 91.50 92 | 74.46 95 | 75.04 195 | 90.41 94 | 53.82 211 | 94.54 81 | 77.56 80 | 82.91 165 | 89.86 177 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PCF-MVS | | 73.52 7 | 80.38 123 | 78.84 137 | 85.01 72 | 87.71 172 | 68.99 86 | 83.65 243 | 91.46 93 | 63.00 267 | 77.77 135 | 90.28 95 | 66.10 78 | 95.09 64 | 61.40 220 | 88.22 102 | 90.94 124 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
ACMM | | 73.20 8 | 80.78 112 | 79.84 108 | 83.58 113 | 89.31 113 | 68.37 103 | 89.99 54 | 91.60 85 | 70.28 177 | 77.25 143 | 89.66 109 | 53.37 214 | 93.53 132 | 74.24 114 | 82.85 166 | 88.85 210 |
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 | 82.79 156 | 89.59 97 | 62.99 218 | 88.16 113 | 91.51 89 | 65.77 242 | 77.14 148 | 91.09 80 | 60.91 155 | 93.21 143 | 50.26 286 | 87.05 114 | 92.17 95 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
OpenMVS | | 72.83 10 | 79.77 139 | 78.33 150 | 84.09 97 | 85.17 209 | 69.91 68 | 90.57 41 | 90.97 104 | 66.70 228 | 72.17 227 | 91.91 60 | 54.70 202 | 93.96 102 | 61.81 217 | 90.95 67 | 88.41 232 |
|
PLC | | 70.83 11 | 78.05 173 | 76.37 184 | 83.08 132 | 91.88 61 | 67.80 114 | 88.19 111 | 89.46 155 | 64.33 257 | 69.87 260 | 88.38 142 | 53.66 212 | 93.58 128 | 58.86 240 | 82.73 168 | 87.86 241 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
HY-MVS | | 69.67 12 | 77.95 177 | 77.15 171 | 80.36 207 | 87.57 178 | 60.21 241 | 83.37 254 | 87.78 205 | 66.11 237 | 75.37 184 | 87.06 183 | 63.27 102 | 90.48 234 | 61.38 221 | 82.43 173 | 90.40 151 |
|
LTVRE_ROB | | 69.57 13 | 76.25 215 | 74.54 222 | 81.41 191 | 88.60 137 | 64.38 186 | 79.24 285 | 89.12 167 | 70.76 169 | 69.79 262 | 87.86 154 | 49.09 272 | 93.20 145 | 56.21 262 | 80.16 198 | 86.65 269 |
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 | 76.01 222 | 74.01 227 | 82.03 171 | 88.60 137 | 65.31 156 | 88.86 85 | 87.55 208 | 70.25 178 | 67.75 282 | 87.47 168 | 41.27 313 | 93.19 146 | 58.37 245 | 75.94 253 | 87.60 246 |
|
IB-MVS | | 68.01 15 | 75.85 224 | 73.36 232 | 83.31 121 | 84.76 216 | 66.03 138 | 83.38 247 | 85.06 235 | 70.21 179 | 69.40 264 | 81.05 292 | 45.76 291 | 94.66 80 | 65.10 192 | 75.49 260 | 89.25 194 |
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 | 75.89 223 | 73.93 228 | 81.77 177 | 88.71 135 | 66.61 132 | 88.62 95 | 89.01 171 | 69.81 182 | 66.78 292 | 86.70 193 | 41.95 312 | 91.51 210 | 55.64 263 | 78.14 221 | 87.17 257 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
COLMAP_ROB | | 66.92 17 | 73.01 258 | 70.41 265 | 80.81 202 | 87.13 186 | 65.63 146 | 88.30 107 | 84.19 243 | 62.96 268 | 63.80 312 | 87.69 161 | 38.04 326 | 92.56 170 | 46.66 309 | 74.91 268 | 84.24 302 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PVSNet | | 64.34 18 | 72.08 265 | 70.87 263 | 75.69 283 | 86.21 198 | 56.44 285 | 74.37 317 | 80.73 285 | 62.06 279 | 70.17 252 | 82.23 274 | 42.86 305 | 83.31 306 | 54.77 267 | 84.45 142 | 87.32 253 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 275 | 68.19 278 | 77.65 259 | 80.26 304 | 59.41 247 | 85.01 214 | 82.96 262 | 58.76 303 | 65.43 301 | 82.33 272 | 37.63 329 | 91.23 218 | 45.34 318 | 76.03 252 | 82.32 318 |
|
PVSNet_0 | | 57.27 20 | 61.67 310 | 59.27 311 | 68.85 320 | 79.61 313 | 57.44 270 | 68.01 337 | 73.44 337 | 55.93 321 | 58.54 327 | 70.41 338 | 44.58 296 | 77.55 328 | 47.01 304 | 35.91 350 | 71.55 344 |
|
CMPMVS | | 51.72 21 | 70.19 279 | 68.16 279 | 76.28 279 | 73.15 340 | 57.55 268 | 79.47 283 | 83.92 244 | 48.02 342 | 56.48 335 | 84.81 249 | 43.13 302 | 86.42 288 | 62.67 208 | 81.81 179 | 84.89 296 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PMVS | | 37.38 22 | 44.16 330 | 40.28 332 | 55.82 337 | 40.82 365 | 42.54 344 | 65.12 344 | 63.99 357 | 34.43 352 | 24.48 356 | 57.12 351 | 3.92 365 | 76.17 333 | 17.10 357 | 55.52 341 | 48.75 352 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE | | 26.22 23 | 30.37 338 | 25.89 340 | 43.81 345 | 44.55 364 | 35.46 355 | 28.87 360 | 39.07 364 | 18.20 359 | 18.58 360 | 40.18 355 | 2.68 366 | 47.37 361 | 17.07 358 | 23.78 354 | 48.60 353 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
0601test | | | 81.17 99 | 80.47 99 | 83.24 125 | 89.13 120 | 63.62 197 | 86.21 179 | 89.95 141 | 72.43 145 | 81.78 77 | 89.61 111 | 57.50 180 | 93.58 128 | 70.75 146 | 86.90 117 | 92.52 82 |
|
Anonymous20240529 | | | 80.19 129 | 78.89 136 | 84.10 96 | 90.60 75 | 64.75 170 | 88.95 82 | 90.90 106 | 65.97 241 | 80.59 91 | 91.17 78 | 49.97 264 | 93.73 125 | 69.16 161 | 82.70 170 | 93.81 39 |
|
Anonymous202405211 | | | 78.25 166 | 77.01 173 | 81.99 172 | 91.03 68 | 60.67 237 | 84.77 218 | 83.90 245 | 70.65 172 | 80.00 94 | 91.20 77 | 41.08 315 | 91.43 211 | 65.21 190 | 85.26 134 | 93.85 36 |
|
Anonymous20240521 | | | 76.96 201 | 76.26 190 | 79.07 237 | 86.63 193 | 56.37 288 | 87.57 126 | 91.09 102 | 72.19 149 | 71.23 239 | 88.10 152 | 54.30 205 | 91.20 219 | 58.34 246 | 76.89 237 | 89.65 188 |
|
our_test_3 | | | 69.14 285 | 67.00 291 | 75.57 285 | 79.80 310 | 58.80 249 | 77.96 298 | 77.81 313 | 59.55 296 | 62.90 316 | 78.25 313 | 47.43 278 | 83.97 301 | 51.71 278 | 67.58 309 | 83.93 306 |
|
ppachtmachnet_test | | | 70.04 280 | 67.34 290 | 78.14 252 | 79.80 310 | 61.13 231 | 79.19 287 | 80.59 286 | 59.16 300 | 65.27 302 | 79.29 306 | 46.75 284 | 87.29 281 | 49.33 290 | 66.72 314 | 86.00 286 |
|
SMA-MVS | | | 89.08 4 | 89.23 4 | 88.61 2 | 94.25 19 | 73.73 7 | 92.40 14 | 93.63 10 | 74.77 92 | 92.29 1 | 95.97 2 | 74.28 18 | 97.24 3 | 88.58 4 | 96.91 1 | 94.87 5 |
|
tfpn111 | | | 76.54 207 | 75.51 205 | 79.61 223 | 89.52 99 | 56.99 274 | 85.83 189 | 83.23 255 | 73.94 103 | 76.32 161 | 87.12 178 | 51.89 230 | 92.06 183 | 48.04 301 | 83.73 152 | 89.78 181 |
|
conf0.01 | | | 73.67 242 | 72.42 242 | 77.42 263 | 87.85 158 | 53.28 313 | 83.38 247 | 79.08 301 | 68.40 210 | 72.45 218 | 86.08 218 | 50.60 252 | 89.19 253 | 44.25 320 | 79.66 202 | 89.78 181 |
|
GSMVS | | | | | | | | | | | | | | | | | 88.96 208 |
|
ESAPD | | | 89.48 1 | 89.98 1 | 88.01 10 | 94.80 4 | 72.69 27 | 91.59 26 | 94.10 1 | 75.90 70 | 92.29 1 | 95.66 3 | 81.67 1 | 97.38 1 | 87.44 11 | 96.34 4 | 93.95 32 |
|
conf0.002 | | | 73.67 242 | 72.42 242 | 77.42 263 | 87.85 158 | 53.28 313 | 83.38 247 | 79.08 301 | 68.40 210 | 72.45 218 | 86.08 218 | 50.60 252 | 89.19 253 | 44.25 320 | 79.66 202 | 89.78 181 |
|
thresconf0.02 | | | 73.39 248 | 72.42 242 | 76.31 274 | 87.85 158 | 53.28 313 | 83.38 247 | 79.08 301 | 68.40 210 | 72.45 218 | 86.08 218 | 50.60 252 | 89.19 253 | 44.25 320 | 79.66 202 | 86.48 271 |
|
tfpn_n400 | | | 73.39 248 | 72.42 242 | 76.31 274 | 87.85 158 | 53.28 313 | 83.38 247 | 79.08 301 | 68.40 210 | 72.45 218 | 86.08 218 | 50.60 252 | 89.19 253 | 44.25 320 | 79.66 202 | 86.48 271 |
|
tfpnconf | | | 73.39 248 | 72.42 242 | 76.31 274 | 87.85 158 | 53.28 313 | 83.38 247 | 79.08 301 | 68.40 210 | 72.45 218 | 86.08 218 | 50.60 252 | 89.19 253 | 44.25 320 | 79.66 202 | 86.48 271 |
|
tfpnview11 | | | 73.39 248 | 72.42 242 | 76.31 274 | 87.85 158 | 53.28 313 | 83.38 247 | 79.08 301 | 68.40 210 | 72.45 218 | 86.08 218 | 50.60 252 | 89.19 253 | 44.25 320 | 79.66 202 | 86.48 271 |
|
tfpn1000 | | | 73.44 247 | 72.49 240 | 76.29 278 | 87.81 166 | 53.69 310 | 84.05 240 | 78.81 308 | 67.99 220 | 72.09 231 | 86.27 213 | 49.95 265 | 89.04 262 | 44.09 326 | 81.38 182 | 86.15 279 |
|
test_part2 | | | | | | 95.06 1 | 72.65 28 | | | | 91.80 3 | | | | | | |
|
tfpn_ndepth | | | 73.70 240 | 72.75 237 | 76.52 272 | 87.78 170 | 54.92 302 | 84.32 234 | 80.28 293 | 67.57 223 | 72.50 215 | 84.82 248 | 50.12 262 | 89.44 249 | 45.73 315 | 81.66 180 | 85.20 291 |
|
test_part1 | | | | | 0.00 355 | | 0.00 370 | 0.00 361 | 94.09 2 | | | | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
conf200view11 | | | 76.55 206 | 75.55 203 | 79.57 226 | 89.52 99 | 56.99 274 | 85.83 189 | 83.23 255 | 73.94 103 | 76.32 161 | 87.12 178 | 51.89 230 | 91.95 185 | 48.33 294 | 83.75 148 | 89.78 181 |
|
thres100view900 | | | 76.50 209 | 75.55 203 | 79.33 228 | 89.52 99 | 56.99 274 | 85.83 189 | 83.23 255 | 73.94 103 | 76.32 161 | 87.12 178 | 51.89 230 | 91.95 185 | 48.33 294 | 83.75 148 | 89.07 195 |
|
tfpnnormal | | | 74.39 234 | 73.16 234 | 78.08 253 | 86.10 199 | 58.05 257 | 84.65 223 | 87.53 209 | 70.32 176 | 71.22 241 | 85.63 232 | 54.97 197 | 89.86 240 | 43.03 329 | 75.02 267 | 86.32 276 |
|
tfpn200view9 | | | 76.42 212 | 75.37 209 | 79.55 227 | 89.13 120 | 57.65 266 | 85.17 209 | 83.60 248 | 73.41 120 | 76.45 156 | 86.39 207 | 52.12 224 | 91.95 185 | 48.33 294 | 83.75 148 | 89.07 195 |
|
view600 | | | 76.20 216 | 75.21 212 | 79.16 233 | 89.64 92 | 55.82 294 | 85.74 194 | 82.06 272 | 73.88 107 | 75.74 174 | 87.85 155 | 51.84 234 | 91.66 203 | 46.75 305 | 83.42 156 | 90.00 167 |
|
view800 | | | 76.20 216 | 75.21 212 | 79.16 233 | 89.64 92 | 55.82 294 | 85.74 194 | 82.06 272 | 73.88 107 | 75.74 174 | 87.85 155 | 51.84 234 | 91.66 203 | 46.75 305 | 83.42 156 | 90.00 167 |
|
conf0.05thres1000 | | | 76.20 216 | 75.21 212 | 79.16 233 | 89.64 92 | 55.82 294 | 85.74 194 | 82.06 272 | 73.88 107 | 75.74 174 | 87.85 155 | 51.84 234 | 91.66 203 | 46.75 305 | 83.42 156 | 90.00 167 |
|
tfpn | | | 76.20 216 | 75.21 212 | 79.16 233 | 89.64 92 | 55.82 294 | 85.74 194 | 82.06 272 | 73.88 107 | 75.74 174 | 87.85 155 | 51.84 234 | 91.66 203 | 46.75 305 | 83.42 156 | 90.00 167 |
|
v1.0 | | | 37.66 334 | 50.21 325 | 0.00 355 | 95.06 1 | 0.00 370 | 0.00 361 | 94.09 2 | 75.63 75 | 91.80 3 | 95.29 4 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
CHOSEN 280x420 | | | 66.51 299 | 64.71 298 | 71.90 305 | 81.45 290 | 63.52 201 | 57.98 351 | 68.95 350 | 53.57 330 | 62.59 317 | 76.70 321 | 46.22 286 | 75.29 337 | 55.25 265 | 79.68 201 | 76.88 340 |
|
CANet | | | 86.45 32 | 86.10 36 | 87.51 29 | 90.09 84 | 70.94 52 | 89.70 63 | 92.59 45 | 81.78 4 | 81.32 81 | 91.43 74 | 70.34 44 | 97.23 4 | 84.26 29 | 93.36 48 | 94.37 15 |
|
Fast-Effi-MVS+-dtu | | | 78.02 174 | 76.49 181 | 82.62 162 | 83.16 264 | 66.96 129 | 86.94 155 | 87.45 212 | 72.45 142 | 71.49 238 | 84.17 254 | 54.79 201 | 91.58 209 | 67.61 169 | 80.31 197 | 89.30 193 |
|
Effi-MVS+-dtu | | | 80.03 134 | 78.57 142 | 84.42 86 | 85.13 212 | 68.74 94 | 88.77 89 | 88.10 198 | 74.99 87 | 74.97 196 | 83.49 263 | 57.27 182 | 93.36 139 | 73.53 121 | 80.88 187 | 91.18 117 |
|
CANet_DTU | | | 80.61 114 | 79.87 107 | 82.83 151 | 85.60 205 | 63.17 214 | 87.36 136 | 88.65 189 | 76.37 63 | 75.88 171 | 88.44 141 | 53.51 213 | 93.07 153 | 73.30 124 | 89.74 81 | 92.25 91 |
|
MVS_0304 | | | 86.37 37 | 85.81 41 | 88.02 9 | 90.13 82 | 72.39 35 | 89.66 65 | 92.75 40 | 81.64 6 | 82.66 69 | 92.04 57 | 64.44 92 | 97.35 2 | 84.76 23 | 94.25 43 | 94.33 18 |
|
MP-MVS-pluss | | | 87.67 14 | 87.72 13 | 87.54 28 | 93.64 30 | 72.04 41 | 89.80 59 | 93.50 13 | 75.17 86 | 86.34 20 | 95.29 4 | 70.86 40 | 96.00 35 | 88.78 3 | 96.04 5 | 94.58 8 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
HSP-MVS | | | 89.28 2 | 89.76 2 | 87.85 20 | 94.28 18 | 73.46 15 | 92.90 8 | 92.73 41 | 80.27 13 | 91.35 5 | 94.16 23 | 78.35 3 | 96.77 11 | 89.59 1 | 94.22 44 | 93.33 59 |
|
sam_mvs1 | | | | | | | | | | | | | 51.32 242 | | | | 88.96 208 |
|
sam_mvs | | | | | | | | | | | | | 50.01 263 | | | | |
|
semantic-postprocess | | | | | 80.11 213 | 82.69 276 | 64.85 167 | | 83.47 252 | 69.16 195 | 70.49 248 | 84.15 255 | 50.83 250 | 88.15 275 | 69.23 159 | 72.14 290 | 87.34 252 |
|
TSAR-MVS + MP. | | | 88.02 12 | 88.11 10 | 87.72 24 | 93.68 29 | 72.13 40 | 91.41 29 | 92.35 54 | 74.62 94 | 88.90 9 | 93.85 30 | 75.75 9 | 96.00 35 | 87.80 6 | 94.63 33 | 95.04 2 |
|
xiu_mvs_v1_base_debu | | | 80.80 109 | 79.72 111 | 84.03 101 | 87.35 179 | 70.19 64 | 85.56 199 | 88.77 185 | 69.06 197 | 81.83 73 | 88.16 147 | 50.91 246 | 92.85 161 | 78.29 75 | 87.56 107 | 89.06 197 |
|
OPM-MVS | | | 83.50 64 | 82.95 66 | 85.14 68 | 88.79 132 | 70.95 51 | 89.13 79 | 91.52 88 | 77.55 38 | 80.96 88 | 91.75 63 | 60.71 157 | 94.50 84 | 79.67 64 | 86.51 124 | 89.97 174 |
|
ACMMP_Plus | | | 88.05 11 | 88.08 11 | 87.94 13 | 93.70 27 | 73.05 19 | 90.86 36 | 93.59 11 | 76.27 66 | 88.14 11 | 95.09 6 | 71.06 39 | 96.67 15 | 87.67 7 | 96.37 3 | 94.09 24 |
|
ambc | | | | | 75.24 289 | 73.16 339 | 50.51 330 | 63.05 347 | 87.47 211 | | 64.28 308 | 77.81 317 | 17.80 354 | 89.73 243 | 57.88 251 | 60.64 334 | 85.49 288 |
|
zzz-MVS | | | 87.53 16 | 87.41 17 | 87.90 17 | 94.18 23 | 74.25 2 | 90.23 50 | 92.02 64 | 79.45 19 | 85.88 22 | 94.80 7 | 68.07 61 | 96.21 29 | 86.69 12 | 95.34 19 | 93.23 61 |
|
MTGPA | | | | | | | | | 92.02 64 | | | | | | | | |
|
mvs-test1 | | | 80.88 102 | 79.40 121 | 85.29 64 | 85.13 212 | 69.75 72 | 89.28 70 | 88.10 198 | 74.99 87 | 76.44 159 | 86.72 188 | 57.27 182 | 94.26 92 | 73.53 121 | 83.18 163 | 91.87 101 |
|
Effi-MVS+ | | | 83.62 63 | 83.08 63 | 85.24 66 | 88.38 146 | 67.45 118 | 88.89 84 | 89.15 166 | 75.50 78 | 82.27 70 | 88.28 145 | 69.61 53 | 94.45 85 | 77.81 78 | 87.84 103 | 93.84 38 |
|
xiu_mvs_v2_base | | | 81.69 89 | 81.05 91 | 83.60 112 | 89.15 119 | 68.03 111 | 84.46 228 | 90.02 139 | 70.67 170 | 81.30 84 | 86.53 204 | 63.17 105 | 94.19 94 | 75.60 103 | 88.54 97 | 88.57 226 |
|
xiu_mvs_v1_base | | | 80.80 109 | 79.72 111 | 84.03 101 | 87.35 179 | 70.19 64 | 85.56 199 | 88.77 185 | 69.06 197 | 81.83 73 | 88.16 147 | 50.91 246 | 92.85 161 | 78.29 75 | 87.56 107 | 89.06 197 |
|
new-patchmatchnet | | | 61.73 309 | 61.73 309 | 61.70 332 | 72.74 341 | 24.50 363 | 69.16 332 | 78.03 312 | 61.40 282 | 56.72 334 | 75.53 326 | 38.42 324 | 76.48 332 | 45.95 314 | 57.67 337 | 84.13 304 |
|
pmmvs6 | | | 74.69 232 | 73.39 231 | 78.61 244 | 81.38 292 | 57.48 269 | 86.64 166 | 87.95 202 | 64.99 251 | 70.18 251 | 86.61 199 | 50.43 260 | 89.52 246 | 62.12 213 | 70.18 300 | 88.83 211 |
|
pmmvs5 | | | 71.55 267 | 70.20 267 | 75.61 284 | 77.83 322 | 56.39 286 | 81.74 264 | 80.89 282 | 57.76 310 | 67.46 285 | 84.49 252 | 49.26 271 | 85.32 296 | 57.08 258 | 75.29 264 | 85.11 295 |
|
test_post1 | | | | | | | | 78.90 291 | | | | 5.43 363 | 48.81 275 | 85.44 295 | 59.25 237 | | |
|
test_post | | | | | | | | | | | | 5.46 362 | 50.36 261 | 84.24 300 | | | |
|
Fast-Effi-MVS+ | | | 80.81 107 | 79.92 106 | 83.47 115 | 88.85 126 | 64.51 177 | 85.53 204 | 89.39 157 | 70.79 167 | 78.49 111 | 85.06 244 | 67.54 67 | 93.58 128 | 67.03 178 | 86.58 122 | 92.32 88 |
|
patchmatchnet-post | | | | | | | | | | | | 74.00 330 | 51.12 245 | 88.60 271 | | | |
|
Anonymous20231211 | | | 78.97 156 | 77.69 163 | 82.81 153 | 90.54 76 | 64.29 187 | 90.11 53 | 91.51 89 | 65.01 250 | 76.16 170 | 88.13 151 | 50.56 258 | 93.03 157 | 69.68 156 | 77.56 225 | 91.11 119 |
|
pmmvs-eth3d | | | 70.50 276 | 67.83 285 | 78.52 247 | 77.37 325 | 66.18 137 | 81.82 262 | 81.51 279 | 58.90 302 | 63.90 311 | 80.42 298 | 42.69 306 | 86.28 289 | 58.56 243 | 65.30 324 | 83.11 313 |
|
GG-mvs-BLEND | | | | | 75.38 288 | 81.59 288 | 55.80 298 | 79.32 284 | 69.63 345 | | 67.19 288 | 73.67 332 | 43.24 301 | 88.90 269 | 50.41 283 | 84.50 140 | 81.45 323 |
|
xiu_mvs_v1_base_debi | | | 80.80 109 | 79.72 111 | 84.03 101 | 87.35 179 | 70.19 64 | 85.56 199 | 88.77 185 | 69.06 197 | 81.83 73 | 88.16 147 | 50.91 246 | 92.85 161 | 78.29 75 | 87.56 107 | 89.06 197 |
|
Anonymous20231206 | | | 68.60 287 | 67.80 286 | 71.02 312 | 80.23 306 | 50.75 329 | 78.30 296 | 80.47 288 | 56.79 317 | 66.11 298 | 82.63 270 | 46.35 285 | 78.95 321 | 43.62 328 | 75.70 256 | 83.36 310 |
|
MTAPA | | | 87.23 22 | 87.00 22 | 87.90 17 | 94.18 23 | 74.25 2 | 86.58 168 | 92.02 64 | 79.45 19 | 85.88 22 | 94.80 7 | 68.07 61 | 96.21 29 | 86.69 12 | 95.34 19 | 93.23 61 |
|
MTMP | | | | | | | | 92.18 20 | 32.83 365 | | | | | | | | |
|
gm-plane-assit | | | | | | 81.40 291 | 53.83 309 | | | 62.72 273 | | 80.94 295 | | 92.39 174 | 63.40 202 | | |
|
test9_res | | | | | | | | | | | | | | | 84.90 19 | 95.70 14 | 92.87 74 |
|
MVP-Stereo | | | 76.12 220 | 74.46 224 | 81.13 198 | 85.37 208 | 69.79 70 | 84.42 231 | 87.95 202 | 65.03 249 | 67.46 285 | 85.33 239 | 53.28 215 | 91.73 195 | 58.01 250 | 83.27 161 | 81.85 321 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
TEST9 | | | | | | 93.26 37 | 72.96 20 | 88.75 91 | 91.89 73 | 68.44 209 | 85.00 32 | 93.10 42 | 74.36 17 | 95.41 50 | | | |
|
train_agg | | | 86.43 33 | 86.20 33 | 87.13 35 | 93.26 37 | 72.96 20 | 88.75 91 | 91.89 73 | 68.69 205 | 85.00 32 | 93.10 42 | 74.43 14 | 95.41 50 | 84.97 17 | 95.71 12 | 93.02 70 |
|
gg-mvs-nofinetune | | | 69.95 281 | 67.96 282 | 75.94 281 | 83.07 265 | 54.51 305 | 77.23 302 | 70.29 343 | 63.11 265 | 70.32 249 | 62.33 345 | 43.62 300 | 88.69 270 | 53.88 271 | 87.76 104 | 84.62 300 |
|
Patchmatch-test1 | | | 73.49 245 | 71.85 252 | 78.41 249 | 84.05 241 | 62.17 226 | 79.96 279 | 79.29 300 | 66.30 236 | 72.38 225 | 79.58 305 | 51.95 229 | 85.08 297 | 55.46 264 | 77.67 224 | 87.99 237 |
|
Patchmatch-test | | | 64.82 305 | 63.24 303 | 69.57 316 | 79.42 315 | 49.82 333 | 63.49 346 | 69.05 349 | 51.98 336 | 59.95 324 | 80.13 300 | 50.91 246 | 70.98 348 | 40.66 334 | 73.57 280 | 87.90 240 |
|
test_8 | | | | | | 93.13 39 | 72.57 31 | 88.68 94 | 91.84 76 | 68.69 205 | 84.87 38 | 93.10 42 | 74.43 14 | 95.16 58 | | | |
|
MS-PatchMatch | | | 73.83 239 | 72.67 238 | 77.30 267 | 83.87 244 | 66.02 139 | 81.82 262 | 84.66 238 | 61.37 284 | 68.61 276 | 82.82 268 | 47.29 279 | 88.21 274 | 59.27 236 | 84.32 143 | 77.68 334 |
|
Patchmatch-RL test | | | 70.24 278 | 67.78 287 | 77.61 260 | 77.43 324 | 59.57 244 | 71.16 322 | 70.33 342 | 62.94 269 | 68.65 274 | 72.77 333 | 50.62 251 | 85.49 294 | 69.58 157 | 66.58 316 | 87.77 243 |
|
agg_prior3 | | | 86.16 39 | 85.85 40 | 87.10 36 | 93.31 34 | 72.86 24 | 88.77 89 | 91.68 83 | 68.29 217 | 84.26 47 | 92.83 50 | 72.83 28 | 95.42 49 | 84.97 17 | 95.71 12 | 93.02 70 |
|
cdsmvs_eth3d_5k | | | 19.96 339 | 26.61 339 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 89.26 163 | 0.00 365 | 0.00 367 | 88.61 135 | 61.62 141 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
pcd_1.5k_mvsjas | | | 5.26 345 | 7.02 346 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 63.15 106 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
pcd1.5k->3k | | | 34.07 335 | 35.26 335 | 30.50 349 | 86.92 188 | 0.00 370 | 0.00 361 | 91.58 86 | 0.00 365 | 0.00 367 | 0.00 367 | 56.23 190 | 0.00 367 | 0.00 364 | 82.60 171 | 91.49 111 |
|
agg_prior1 | | | 86.22 38 | 86.09 37 | 86.62 44 | 92.85 45 | 71.94 42 | 88.59 96 | 91.78 79 | 68.96 202 | 84.41 44 | 93.18 41 | 74.94 10 | 94.93 67 | 84.75 24 | 95.33 21 | 93.01 72 |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 41 | 95.45 16 | 92.70 76 |
|
agg_prior | | | | | | 92.85 45 | 71.94 42 | | 91.78 79 | | 84.41 44 | | | 94.93 67 | | | |
|
tmp_tt | | | 18.61 340 | 21.40 341 | 10.23 352 | 4.82 367 | 10.11 366 | 34.70 358 | 30.74 366 | 1.48 362 | 23.91 358 | 26.07 360 | 28.42 340 | 13.41 364 | 27.12 352 | 15.35 360 | 7.17 360 |
|
canonicalmvs | | | 85.91 41 | 85.87 39 | 86.04 56 | 89.84 90 | 69.44 81 | 90.45 46 | 93.00 28 | 76.70 56 | 88.01 14 | 91.23 76 | 73.28 24 | 93.91 107 | 81.50 51 | 88.80 90 | 94.77 6 |
|
anonymousdsp | | | 78.60 161 | 77.15 171 | 82.98 138 | 80.51 303 | 67.08 125 | 87.24 143 | 89.53 152 | 65.66 244 | 75.16 191 | 87.19 176 | 52.52 216 | 92.25 179 | 77.17 85 | 79.34 212 | 89.61 189 |
|
alignmvs | | | 85.48 46 | 85.32 46 | 85.96 58 | 89.51 102 | 69.47 79 | 89.74 61 | 92.47 47 | 76.17 67 | 87.73 16 | 91.46 73 | 70.32 45 | 93.78 116 | 81.51 50 | 88.95 86 | 94.63 7 |
|
nrg030 | | | 83.88 58 | 83.53 57 | 84.96 73 | 86.77 192 | 69.28 82 | 90.46 45 | 92.67 42 | 74.79 91 | 82.95 61 | 91.33 75 | 72.70 29 | 93.09 152 | 80.79 57 | 79.28 213 | 92.50 83 |
|
v144192 | | | 79.47 145 | 78.37 148 | 82.78 157 | 83.35 256 | 63.96 195 | 86.96 154 | 90.36 123 | 69.99 180 | 77.50 138 | 85.67 230 | 60.66 159 | 93.77 118 | 74.27 113 | 76.58 246 | 90.62 136 |
|
FIs | | | 82.07 83 | 82.42 71 | 81.04 199 | 88.80 131 | 58.34 254 | 88.26 110 | 93.49 14 | 76.93 49 | 78.47 112 | 91.04 82 | 69.92 49 | 92.34 177 | 69.87 154 | 84.97 136 | 92.44 85 |
|
v1921920 | | | 79.22 150 | 78.03 154 | 82.80 154 | 83.30 258 | 63.94 196 | 86.80 160 | 90.33 125 | 69.91 181 | 77.48 139 | 85.53 235 | 58.44 173 | 93.75 120 | 73.60 120 | 76.85 240 | 90.71 131 |
|
UA-Net | | | 85.08 54 | 84.96 50 | 85.45 61 | 92.07 57 | 68.07 110 | 89.78 60 | 90.86 108 | 82.48 2 | 84.60 42 | 93.20 40 | 69.35 55 | 95.22 56 | 71.39 145 | 90.88 68 | 93.07 68 |
|
v1192 | | | 79.59 142 | 78.43 147 | 83.07 133 | 83.55 253 | 64.52 175 | 86.93 156 | 90.58 114 | 70.83 166 | 77.78 134 | 85.90 224 | 59.15 169 | 93.94 104 | 73.96 116 | 77.19 231 | 90.76 128 |
|
FC-MVSNet-test | | | 81.52 93 | 82.02 79 | 80.03 214 | 88.42 145 | 55.97 293 | 87.95 117 | 93.42 16 | 77.10 45 | 77.38 140 | 90.98 87 | 69.96 48 | 91.79 191 | 68.46 166 | 84.50 140 | 92.33 87 |
|
v1144 | | | 80.03 134 | 79.03 133 | 83.01 136 | 83.78 249 | 64.51 177 | 87.11 150 | 90.57 115 | 71.96 153 | 78.08 130 | 86.20 215 | 61.41 145 | 93.94 104 | 74.93 109 | 77.23 229 | 90.60 138 |
|
sosnet-low-res | | | 0.00 346 | 0.00 347 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
HFP-MVS | | | 87.58 15 | 87.47 16 | 87.94 13 | 94.58 8 | 73.54 12 | 93.04 5 | 93.24 19 | 76.78 52 | 84.91 34 | 94.44 15 | 70.78 41 | 96.61 18 | 84.53 25 | 94.89 28 | 93.66 43 |
|
v148 | | | 78.72 159 | 77.80 159 | 81.47 190 | 82.73 274 | 61.96 228 | 86.30 177 | 88.08 200 | 73.26 122 | 76.18 167 | 85.47 237 | 62.46 131 | 92.36 176 | 71.92 142 | 73.82 279 | 90.09 161 |
|
sosnet | | | 0.00 346 | 0.00 347 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
v748 | | | 77.97 176 | 76.65 180 | 81.92 175 | 82.29 281 | 63.28 209 | 87.53 131 | 90.35 124 | 73.50 119 | 70.76 244 | 85.55 234 | 58.28 174 | 92.81 165 | 68.81 164 | 72.76 286 | 89.67 187 |
|
uncertanet | | | 0.00 346 | 0.00 347 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
AllTest | | | 70.96 271 | 68.09 281 | 79.58 224 | 85.15 210 | 63.62 197 | 84.58 225 | 79.83 296 | 62.31 276 | 60.32 322 | 86.73 186 | 32.02 337 | 88.96 266 | 50.28 284 | 71.57 294 | 86.15 279 |
|
TestCases | | | | | 79.58 224 | 85.15 210 | 63.62 197 | | 79.83 296 | 62.31 276 | 60.32 322 | 86.73 186 | 32.02 337 | 88.96 266 | 50.28 284 | 71.57 294 | 86.15 279 |
|
v7n | | | 78.97 156 | 77.58 165 | 83.14 129 | 83.45 255 | 65.51 149 | 88.32 106 | 91.21 98 | 73.69 113 | 72.41 224 | 86.32 212 | 57.93 176 | 93.81 114 | 69.18 160 | 75.65 257 | 90.11 159 |
|
v1141 | | | 80.19 129 | 79.31 125 | 82.85 148 | 83.84 246 | 64.12 191 | 87.14 145 | 90.08 136 | 73.13 126 | 78.27 121 | 86.39 207 | 62.67 123 | 93.75 120 | 75.40 105 | 76.83 242 | 90.68 132 |
|
region2R | | | 87.42 19 | 87.20 20 | 88.09 7 | 94.63 7 | 73.55 10 | 93.03 7 | 93.12 24 | 76.73 55 | 84.45 43 | 94.52 10 | 69.09 57 | 96.70 14 | 84.37 28 | 94.83 30 | 94.03 27 |
|
testing_2 | | | 75.73 225 | 73.34 233 | 82.89 147 | 77.37 325 | 65.22 158 | 84.10 238 | 90.54 116 | 69.09 196 | 60.46 321 | 81.15 291 | 40.48 317 | 92.84 164 | 76.36 92 | 80.54 195 | 90.60 138 |
|
test_normal | | | 79.81 138 | 78.45 144 | 83.89 108 | 82.70 275 | 65.40 151 | 85.82 192 | 89.48 154 | 69.39 188 | 70.12 254 | 85.66 231 | 57.15 186 | 93.71 126 | 77.08 86 | 88.62 94 | 92.56 81 |
|
v1neww | | | 80.40 120 | 79.54 115 | 82.98 138 | 84.10 233 | 64.51 177 | 87.57 126 | 90.22 129 | 73.25 123 | 78.47 112 | 86.65 196 | 62.83 114 | 93.86 110 | 75.72 98 | 77.02 233 | 90.58 141 |
|
PS-MVSNAJss | | | 82.07 83 | 81.31 85 | 84.34 90 | 86.51 195 | 67.27 123 | 89.27 71 | 91.51 89 | 71.75 154 | 79.37 99 | 90.22 98 | 63.15 106 | 94.27 89 | 77.69 79 | 82.36 174 | 91.49 111 |
|
PS-MVSNAJ | | | 81.69 89 | 81.02 92 | 83.70 110 | 89.51 102 | 68.21 108 | 84.28 235 | 90.09 135 | 70.79 167 | 81.26 85 | 85.62 233 | 63.15 106 | 94.29 87 | 75.62 102 | 88.87 89 | 88.59 224 |
|
jajsoiax | | | 79.29 149 | 77.96 155 | 83.27 123 | 84.68 218 | 66.57 133 | 89.25 73 | 90.16 133 | 69.20 194 | 75.46 180 | 89.49 114 | 45.75 292 | 93.13 150 | 76.84 90 | 80.80 189 | 90.11 159 |
|
mvs_tets | | | 79.13 152 | 77.77 161 | 83.22 126 | 84.70 217 | 66.37 135 | 89.17 74 | 90.19 132 | 69.38 190 | 75.40 183 | 89.46 117 | 44.17 298 | 93.15 148 | 76.78 91 | 80.70 191 | 90.14 157 |
|
#test# | | | 87.33 21 | 87.13 21 | 87.94 13 | 94.58 8 | 73.54 12 | 92.34 16 | 93.24 19 | 75.23 83 | 84.91 34 | 94.44 15 | 70.78 41 | 96.61 18 | 83.75 33 | 94.89 28 | 93.66 43 |
|
EI-MVSNet-UG-set | | | 83.81 59 | 83.38 59 | 85.09 70 | 87.87 157 | 67.53 117 | 87.44 135 | 89.66 149 | 79.74 18 | 82.23 71 | 89.41 121 | 70.24 46 | 94.74 78 | 79.95 62 | 83.92 145 | 92.99 73 |
|
EI-MVSNet-Vis-set | | | 84.19 56 | 83.81 56 | 85.31 63 | 88.18 150 | 67.85 113 | 87.66 123 | 89.73 148 | 80.05 17 | 82.95 61 | 89.59 112 | 70.74 43 | 94.82 75 | 80.66 58 | 84.72 139 | 93.28 60 |
|
Regformer-3 | | | 85.23 51 | 85.07 49 | 85.70 60 | 88.95 124 | 69.01 85 | 88.29 108 | 89.91 144 | 80.95 9 | 85.01 31 | 90.01 103 | 72.45 31 | 94.19 94 | 82.50 45 | 87.57 105 | 93.90 35 |
|
Regformer-4 | | | 85.68 45 | 85.45 43 | 86.35 47 | 88.95 124 | 69.67 73 | 88.29 108 | 91.29 96 | 81.73 5 | 85.36 28 | 90.01 103 | 72.62 30 | 95.35 55 | 83.28 36 | 87.57 105 | 94.03 27 |
|
Regformer-1 | | | 86.41 35 | 86.33 30 | 86.64 43 | 89.33 108 | 70.93 53 | 88.43 99 | 91.39 94 | 82.14 3 | 86.65 19 | 90.09 100 | 74.39 16 | 95.01 66 | 83.97 32 | 90.63 70 | 93.97 31 |
|
Regformer-2 | | | 86.63 31 | 86.53 29 | 86.95 38 | 89.33 108 | 71.24 47 | 88.43 99 | 92.05 63 | 82.50 1 | 86.88 18 | 90.09 100 | 74.45 13 | 95.61 41 | 84.38 27 | 90.63 70 | 94.01 29 |
|
v7new | | | 80.40 120 | 79.54 115 | 82.98 138 | 84.10 233 | 64.51 177 | 87.57 126 | 90.22 129 | 73.25 123 | 78.47 112 | 86.65 196 | 62.83 114 | 93.86 110 | 75.72 98 | 77.02 233 | 90.58 141 |
|
HPM-MVS++ | | | 89.02 5 | 89.15 5 | 88.63 1 | 95.01 3 | 76.03 1 | 92.38 15 | 92.85 36 | 80.26 14 | 87.78 15 | 94.27 19 | 75.89 8 | 96.81 10 | 87.45 10 | 96.44 2 | 93.05 69 |
|
test_prior4 | | | | | | | 72.60 30 | 89.01 81 | | | | | | | | | |
|
XVS | | | 87.18 23 | 86.91 25 | 88.00 11 | 94.42 13 | 73.33 17 | 92.78 9 | 92.99 30 | 79.14 21 | 83.67 55 | 94.17 22 | 67.45 68 | 96.60 20 | 83.06 38 | 94.50 35 | 94.07 25 |
|
v1240 | | | 78.99 155 | 77.78 160 | 82.64 161 | 83.21 260 | 63.54 200 | 86.62 167 | 90.30 127 | 69.74 186 | 77.33 141 | 85.68 229 | 57.04 187 | 93.76 119 | 73.13 126 | 76.92 236 | 90.62 136 |
|
test_prior3 | | | 86.73 28 | 86.86 27 | 86.33 48 | 92.61 50 | 69.59 75 | 88.85 86 | 92.97 33 | 75.41 79 | 84.91 34 | 93.54 32 | 74.28 18 | 95.48 45 | 83.31 34 | 95.86 8 | 93.91 33 |
|
v18 | | | 77.67 186 | 76.35 188 | 81.64 185 | 84.09 235 | 64.47 183 | 87.27 141 | 89.01 171 | 72.59 141 | 69.39 265 | 82.04 278 | 62.85 112 | 91.80 190 | 72.72 129 | 67.20 311 | 88.63 218 |
|
pm-mvs1 | | | 77.25 198 | 76.68 179 | 78.93 239 | 84.22 225 | 58.62 251 | 86.41 173 | 88.36 195 | 71.37 161 | 73.31 206 | 88.01 153 | 61.22 150 | 89.15 260 | 64.24 198 | 73.01 283 | 89.03 203 |
|
test_prior2 | | | | | | | | 88.85 86 | | 75.41 79 | 84.91 34 | 93.54 32 | 74.28 18 | | 83.31 34 | 95.86 8 | |
|
X-MVStestdata | | | 80.37 124 | 77.83 158 | 88.00 11 | 94.42 13 | 73.33 17 | 92.78 9 | 92.99 30 | 79.14 21 | 83.67 55 | 12.47 361 | 67.45 68 | 96.60 20 | 83.06 38 | 94.50 35 | 94.07 25 |
|
test_prior | | | | | 86.33 48 | 92.61 50 | 69.59 75 | | 92.97 33 | | | | | 95.48 45 | | | 93.91 33 |
|
v17 | | | 77.68 184 | 76.35 188 | 81.69 182 | 84.15 230 | 64.65 172 | 87.33 138 | 88.99 173 | 72.70 139 | 69.25 269 | 82.07 277 | 62.82 116 | 91.79 191 | 72.69 131 | 67.15 312 | 88.63 218 |
|
v16 | | | 77.69 183 | 76.36 187 | 81.68 183 | 84.15 230 | 64.63 174 | 87.33 138 | 88.99 173 | 72.69 140 | 69.31 268 | 82.08 276 | 62.80 117 | 91.79 191 | 72.70 130 | 67.23 310 | 88.63 218 |
|
divwei89l23v2f112 | | | 80.19 129 | 79.31 125 | 82.85 148 | 83.84 246 | 64.11 193 | 87.13 148 | 90.08 136 | 73.13 126 | 78.27 121 | 86.39 207 | 62.69 121 | 93.75 120 | 75.40 105 | 76.82 243 | 90.68 132 |
|
v15 | | | 77.51 191 | 76.12 192 | 81.66 184 | 84.09 235 | 64.65 172 | 87.14 145 | 88.96 177 | 72.76 137 | 68.90 270 | 81.91 285 | 62.74 119 | 91.73 195 | 72.32 135 | 66.29 318 | 88.61 221 |
|
旧先验2 | | | | | | | | 86.56 169 | | 58.10 306 | 87.04 17 | | | 88.98 264 | 74.07 115 | | |
|
新几何2 | | | | | | | | 86.29 178 | | | | | | | | | |
|
新几何1 | | | | | 83.42 117 | 93.13 39 | 70.71 57 | | 85.48 231 | 57.43 313 | 81.80 76 | 91.98 59 | 63.28 101 | 92.27 178 | 64.60 197 | 92.99 49 | 87.27 254 |
|
旧先验1 | | | | | | 91.96 58 | 65.79 145 | | 86.37 223 | | | 93.08 46 | 69.31 56 | | | 92.74 52 | 88.74 215 |
|
无先验 | | | | | | | | 87.48 133 | 88.98 175 | 60.00 292 | | | | 94.12 97 | 67.28 173 | | 88.97 207 |
|
原ACMM2 | | | | | | | | 86.86 158 | | | | | | | | | |
|
原ACMM1 | | | | | 84.35 89 | 93.01 43 | 68.79 89 | | 92.44 48 | 63.96 262 | 81.09 86 | 91.57 69 | 66.06 80 | 95.45 47 | 67.19 175 | 94.82 31 | 88.81 212 |
|
v13 | | | 77.50 193 | 76.07 197 | 81.77 177 | 84.23 224 | 65.07 162 | 87.34 137 | 88.91 183 | 72.92 132 | 68.35 279 | 81.97 281 | 62.53 129 | 91.69 201 | 72.20 139 | 66.22 321 | 88.56 227 |
|
v12 | | | 77.51 191 | 76.09 196 | 81.76 179 | 84.22 225 | 64.99 163 | 87.30 140 | 88.93 182 | 72.92 132 | 68.48 278 | 81.97 281 | 62.54 128 | 91.70 200 | 72.24 138 | 66.21 322 | 88.58 225 |
|
test222 | | | | | | 91.50 63 | 68.26 106 | 84.16 236 | 83.20 259 | 54.63 325 | 79.74 95 | 91.63 67 | 58.97 170 | | | 91.42 62 | 86.77 266 |
|
testdata2 | | | | | | | | | | | | | | 91.01 227 | 62.37 210 | | |
|
segment_acmp | | | | | | | | | | | | | 73.08 25 | | | | |
|
testdata | | | | | 79.97 215 | 90.90 71 | 64.21 188 | | 84.71 237 | 59.27 299 | 85.40 27 | 92.91 47 | 62.02 138 | 89.08 261 | 68.95 162 | 91.37 63 | 86.63 270 |
|
testdata1 | | | | | | | | 84.14 237 | | 75.71 72 | | | | | | | |
|
v8 | | | 79.97 136 | 79.02 134 | 82.80 154 | 84.09 235 | 64.50 181 | 87.96 116 | 90.29 128 | 74.13 101 | 75.24 190 | 86.81 185 | 62.88 111 | 93.89 109 | 74.39 112 | 75.40 262 | 90.00 167 |
|
1314 | | | 76.53 208 | 75.30 211 | 80.21 212 | 83.93 243 | 62.32 224 | 84.66 220 | 88.81 184 | 60.23 290 | 70.16 253 | 84.07 256 | 55.30 196 | 90.73 231 | 67.37 172 | 83.21 162 | 87.59 247 |
|
1121 | | | 80.84 104 | 79.77 109 | 84.05 99 | 93.11 41 | 70.78 56 | 84.66 220 | 85.42 232 | 57.37 314 | 81.76 78 | 92.02 58 | 63.41 99 | 94.12 97 | 67.28 173 | 92.93 50 | 87.26 255 |
|
LFMVS | | | 81.82 88 | 81.23 87 | 83.57 114 | 91.89 60 | 63.43 206 | 89.84 56 | 81.85 277 | 77.04 47 | 83.21 58 | 93.10 42 | 52.26 222 | 93.43 138 | 71.98 140 | 89.95 79 | 93.85 36 |
|
v7 | | | 80.24 126 | 79.26 129 | 83.15 128 | 84.07 239 | 64.94 165 | 87.56 130 | 90.67 110 | 72.26 148 | 78.28 120 | 86.51 205 | 61.45 144 | 94.03 101 | 75.14 108 | 77.41 227 | 90.49 146 |
|
v6 | | | 80.40 120 | 79.54 115 | 82.98 138 | 84.09 235 | 64.50 181 | 87.57 126 | 90.22 129 | 73.25 123 | 78.47 112 | 86.63 198 | 62.84 113 | 93.86 110 | 75.73 97 | 77.02 233 | 90.58 141 |
|
VDD-MVS | | | 83.01 73 | 82.36 74 | 84.96 73 | 91.02 69 | 66.40 134 | 88.91 83 | 88.11 197 | 77.57 35 | 84.39 46 | 93.29 39 | 52.19 223 | 93.91 107 | 77.05 87 | 88.70 92 | 94.57 10 |
|
v11 | | | 77.45 194 | 76.06 198 | 81.59 188 | 84.22 225 | 64.52 175 | 87.11 150 | 89.02 169 | 72.76 137 | 68.76 272 | 81.90 286 | 62.09 137 | 91.71 199 | 71.98 140 | 66.73 313 | 88.56 227 |
|
VDDNet | | | 81.52 93 | 80.67 95 | 84.05 99 | 90.44 78 | 64.13 190 | 89.73 62 | 85.91 229 | 71.11 163 | 83.18 59 | 93.48 34 | 50.54 259 | 93.49 133 | 73.40 123 | 88.25 101 | 94.54 12 |
|
v52 | | | 77.94 179 | 76.37 184 | 82.67 159 | 79.39 317 | 65.52 147 | 86.43 171 | 89.94 142 | 72.28 146 | 72.15 229 | 84.94 247 | 55.70 193 | 93.44 136 | 73.64 118 | 72.84 285 | 89.06 197 |
|
V14 | | | 77.52 189 | 76.12 192 | 81.70 181 | 84.15 230 | 64.77 169 | 87.21 144 | 88.95 178 | 72.80 136 | 68.79 271 | 81.94 284 | 62.69 121 | 91.72 197 | 72.31 136 | 66.27 319 | 88.60 222 |
|
v10 | | | 79.74 140 | 78.67 138 | 82.97 142 | 84.06 240 | 64.95 164 | 87.88 121 | 90.62 113 | 73.11 129 | 75.11 193 | 86.56 202 | 61.46 143 | 94.05 100 | 73.68 117 | 75.55 259 | 89.90 175 |
|
V4 | | | 77.95 177 | 76.37 184 | 82.67 159 | 79.40 316 | 65.52 147 | 86.43 171 | 89.94 142 | 72.28 146 | 72.14 230 | 84.95 246 | 55.72 192 | 93.44 136 | 73.64 118 | 72.86 284 | 89.05 201 |
|
VPNet | | | 78.69 160 | 78.66 139 | 78.76 242 | 88.31 148 | 55.72 299 | 84.45 229 | 86.63 219 | 76.79 51 | 78.26 124 | 90.55 93 | 59.30 168 | 89.70 244 | 66.63 179 | 77.05 232 | 90.88 125 |
|
MVS | | | 78.19 170 | 76.99 174 | 81.78 176 | 85.66 203 | 66.99 126 | 84.66 220 | 90.47 118 | 55.08 324 | 72.02 232 | 85.27 240 | 63.83 97 | 94.11 99 | 66.10 183 | 89.80 80 | 84.24 302 |
|
v2v482 | | | 80.23 127 | 79.29 128 | 83.05 134 | 83.62 251 | 64.14 189 | 87.04 152 | 89.97 140 | 73.61 114 | 78.18 127 | 87.22 174 | 61.10 152 | 93.82 113 | 76.11 93 | 76.78 245 | 91.18 117 |
|
v1 | | | 80.19 129 | 79.31 125 | 82.85 148 | 83.83 248 | 64.12 191 | 87.14 145 | 90.07 138 | 73.13 126 | 78.27 121 | 86.38 211 | 62.72 120 | 93.75 120 | 75.41 104 | 76.82 243 | 90.68 132 |
|
V42 | | | 79.38 148 | 78.24 152 | 82.83 151 | 81.10 297 | 65.50 150 | 85.55 202 | 89.82 145 | 71.57 159 | 78.21 125 | 86.12 216 | 60.66 159 | 93.18 147 | 75.64 101 | 75.46 261 | 89.81 180 |
|
V9 | | | 77.52 189 | 76.11 195 | 81.73 180 | 84.19 229 | 64.89 166 | 87.26 142 | 88.94 181 | 72.87 135 | 68.65 274 | 81.96 283 | 62.65 124 | 91.72 197 | 72.27 137 | 66.24 320 | 88.60 222 |
|
SD-MVS | | | 88.06 9 | 88.50 9 | 86.71 42 | 92.60 52 | 72.71 25 | 91.81 25 | 93.19 22 | 77.87 32 | 90.32 7 | 94.00 28 | 74.83 11 | 93.78 116 | 87.63 8 | 94.27 42 | 93.65 48 |
|
GA-MVS | | | 76.87 203 | 75.17 216 | 81.97 173 | 82.75 273 | 62.58 221 | 81.44 269 | 86.35 224 | 72.16 152 | 74.74 198 | 82.89 266 | 46.20 287 | 92.02 184 | 68.85 163 | 81.09 185 | 91.30 115 |
|
MSLP-MVS++ | | | 85.43 48 | 85.76 42 | 84.45 85 | 91.93 59 | 70.24 61 | 90.71 39 | 92.86 35 | 77.46 41 | 84.22 48 | 92.81 53 | 67.16 72 | 92.94 159 | 80.36 59 | 94.35 40 | 90.16 156 |
|
APDe-MVS | | | 89.15 3 | 89.63 3 | 87.73 22 | 94.49 11 | 71.69 44 | 93.83 2 | 93.96 5 | 75.70 73 | 91.06 6 | 96.03 1 | 76.84 4 | 97.03 7 | 89.09 2 | 95.65 15 | 94.47 13 |
|
APD-MVS_3200maxsize | | | 85.97 40 | 85.88 38 | 86.22 51 | 92.69 48 | 69.53 77 | 91.93 23 | 92.99 30 | 73.54 117 | 85.94 21 | 94.51 13 | 65.80 83 | 95.61 41 | 83.04 40 | 92.51 55 | 93.53 54 |
|
ADS-MVSNet2 | | | 66.20 302 | 63.33 302 | 74.82 292 | 79.92 308 | 58.75 250 | 67.55 339 | 75.19 325 | 53.37 331 | 65.25 303 | 75.86 324 | 42.32 308 | 80.53 315 | 41.57 332 | 68.91 304 | 85.18 292 |
|
EI-MVSNet | | | 80.52 118 | 79.98 105 | 82.12 168 | 84.28 222 | 63.19 213 | 86.41 173 | 88.95 178 | 74.18 99 | 78.69 106 | 87.54 166 | 66.62 74 | 92.43 172 | 72.57 133 | 80.57 193 | 90.74 130 |
|
Regformer | | | 0.00 346 | 0.00 347 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
CVMVSNet | | | 72.99 259 | 72.58 239 | 74.25 297 | 84.28 222 | 50.85 328 | 86.41 173 | 83.45 253 | 44.56 344 | 73.23 208 | 87.54 166 | 49.38 268 | 85.70 292 | 65.90 185 | 78.44 218 | 86.19 278 |
|
pmmvs4 | | | 74.03 238 | 71.91 250 | 80.39 206 | 81.96 284 | 68.32 104 | 81.45 268 | 82.14 270 | 59.32 298 | 69.87 260 | 85.13 242 | 52.40 219 | 88.13 276 | 60.21 229 | 74.74 270 | 84.73 299 |
|
EU-MVSNet | | | 68.53 289 | 67.61 289 | 71.31 311 | 78.51 321 | 47.01 338 | 84.47 226 | 84.27 242 | 42.27 345 | 66.44 296 | 84.79 250 | 40.44 318 | 83.76 302 | 58.76 242 | 68.54 308 | 83.17 311 |
|
VNet | | | 82.21 81 | 82.41 72 | 81.62 186 | 90.82 73 | 60.93 233 | 84.47 226 | 89.78 146 | 76.36 64 | 84.07 50 | 91.88 62 | 64.71 91 | 90.26 235 | 70.68 147 | 88.89 87 | 93.66 43 |
|
test-LLR | | | 72.94 260 | 72.43 241 | 74.48 294 | 81.35 293 | 58.04 258 | 78.38 293 | 77.46 315 | 66.66 229 | 69.95 258 | 79.00 309 | 48.06 276 | 79.24 319 | 66.13 181 | 84.83 137 | 86.15 279 |
|
TESTMET0.1,1 | | | 69.89 282 | 69.00 272 | 72.55 303 | 79.27 319 | 56.85 277 | 78.38 293 | 74.71 331 | 57.64 311 | 68.09 280 | 77.19 320 | 37.75 327 | 76.70 330 | 63.92 199 | 84.09 144 | 84.10 305 |
|
test-mter | | | 71.41 268 | 70.39 266 | 74.48 294 | 81.35 293 | 58.04 258 | 78.38 293 | 77.46 315 | 60.32 289 | 69.95 258 | 79.00 309 | 36.08 333 | 79.24 319 | 66.13 181 | 84.83 137 | 86.15 279 |
|
VPA-MVSNet | | | 80.60 115 | 80.55 97 | 80.76 203 | 88.07 152 | 60.80 236 | 86.86 158 | 91.58 86 | 75.67 74 | 80.24 93 | 89.45 119 | 63.34 100 | 90.25 236 | 70.51 149 | 79.22 214 | 91.23 116 |
|
ACMMPR | | | 87.44 17 | 87.23 19 | 88.08 8 | 94.64 6 | 73.59 9 | 93.04 5 | 93.20 21 | 76.78 52 | 84.66 40 | 94.52 10 | 68.81 59 | 96.65 16 | 84.53 25 | 94.90 27 | 94.00 30 |
|
testgi | | | 66.67 298 | 66.53 294 | 67.08 324 | 75.62 332 | 41.69 347 | 75.93 306 | 76.50 320 | 66.11 237 | 65.20 305 | 86.59 200 | 35.72 334 | 74.71 338 | 43.71 327 | 73.38 282 | 84.84 297 |
|
test20.03 | | | 67.45 293 | 66.95 292 | 68.94 318 | 75.48 334 | 44.84 340 | 77.50 300 | 77.67 314 | 66.66 229 | 63.01 314 | 83.80 258 | 47.02 281 | 78.40 323 | 42.53 331 | 68.86 306 | 83.58 308 |
|
thres600view7 | | | 76.50 209 | 75.44 206 | 79.68 220 | 89.40 105 | 57.16 271 | 85.53 204 | 83.23 255 | 73.79 112 | 76.26 164 | 87.09 181 | 51.89 230 | 91.89 189 | 48.05 300 | 83.72 153 | 90.00 167 |
|
1111 | | | 57.11 318 | 56.82 319 | 57.97 336 | 69.10 347 | 28.28 358 | 68.90 335 | 74.54 334 | 54.01 327 | 53.71 340 | 74.51 328 | 23.09 347 | 67.90 353 | 32.28 344 | 61.26 333 | 77.73 333 |
|
.test1245 | | | 45.55 328 | 50.02 326 | 32.14 348 | 69.10 347 | 28.28 358 | 68.90 335 | 74.54 334 | 54.01 327 | 53.71 340 | 74.51 328 | 23.09 347 | 67.90 353 | 32.28 344 | 0.02 362 | 0.25 363 |
|
ADS-MVSNet | | | 64.36 306 | 62.88 306 | 68.78 321 | 79.92 308 | 47.17 337 | 67.55 339 | 71.18 341 | 53.37 331 | 65.25 303 | 75.86 324 | 42.32 308 | 73.99 342 | 41.57 332 | 68.91 304 | 85.18 292 |
|
MP-MVS | | | 87.71 13 | 87.64 14 | 87.93 16 | 94.36 17 | 73.88 4 | 92.71 13 | 92.65 44 | 77.57 35 | 83.84 52 | 94.40 18 | 72.24 33 | 96.28 27 | 85.65 15 | 95.30 23 | 93.62 50 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
testmvs | | | 6.04 344 | 8.02 345 | 0.10 354 | 0.08 368 | 0.03 369 | 69.74 327 | 0.04 370 | 0.05 363 | 0.31 365 | 1.68 365 | 0.02 371 | 0.04 365 | 0.24 362 | 0.02 362 | 0.25 363 |
|
thres400 | | | 76.50 209 | 75.37 209 | 79.86 216 | 89.13 120 | 57.65 266 | 85.17 209 | 83.60 248 | 73.41 120 | 76.45 156 | 86.39 207 | 52.12 224 | 91.95 185 | 48.33 294 | 83.75 148 | 90.00 167 |
|
test123 | | | 6.12 343 | 8.11 344 | 0.14 353 | 0.06 369 | 0.09 368 | 71.05 323 | 0.03 371 | 0.04 364 | 0.25 366 | 1.30 366 | 0.05 370 | 0.03 366 | 0.21 363 | 0.01 364 | 0.29 362 |
|
thres200 | | | 75.55 227 | 74.47 223 | 78.82 241 | 87.78 170 | 57.85 263 | 83.07 255 | 83.51 251 | 72.44 144 | 75.84 172 | 84.42 253 | 52.08 226 | 91.75 194 | 47.41 303 | 83.64 154 | 86.86 264 |
|
test0.0.03 1 | | | 68.00 291 | 67.69 288 | 68.90 319 | 77.55 323 | 47.43 336 | 75.70 310 | 72.95 338 | 66.66 229 | 66.56 293 | 82.29 273 | 48.06 276 | 75.87 334 | 44.97 319 | 74.51 272 | 83.41 309 |
|
test12356 | | | 49.28 327 | 48.51 328 | 51.59 341 | 62.06 353 | 19.11 364 | 60.40 348 | 72.45 339 | 47.60 343 | 40.64 350 | 65.68 343 | 13.84 358 | 68.72 351 | 27.29 351 | 46.67 349 | 66.94 347 |
|
testus | | | 59.00 314 | 57.91 313 | 62.25 331 | 72.25 342 | 39.09 350 | 69.74 327 | 75.02 326 | 53.04 333 | 57.21 332 | 73.72 331 | 18.76 353 | 70.33 349 | 32.86 342 | 68.57 307 | 77.35 335 |
|
pmmvs3 | | | 57.79 316 | 54.26 320 | 68.37 322 | 64.02 352 | 56.72 280 | 75.12 314 | 65.17 354 | 40.20 347 | 52.93 342 | 69.86 340 | 20.36 350 | 75.48 336 | 45.45 317 | 55.25 342 | 72.90 343 |
|
testmv | | | 53.85 321 | 51.03 323 | 62.31 330 | 61.46 354 | 38.88 351 | 70.95 326 | 74.69 332 | 51.11 339 | 41.26 348 | 66.85 342 | 14.28 357 | 72.13 346 | 29.19 349 | 49.51 347 | 75.93 341 |
|
EMVS | | | 30.81 337 | 29.65 338 | 34.27 347 | 50.96 361 | 25.95 362 | 56.58 354 | 46.80 363 | 24.01 358 | 15.53 362 | 30.68 359 | 12.47 359 | 54.43 360 | 12.81 360 | 17.05 358 | 22.43 359 |
|
E-PMN | | | 31.77 336 | 30.64 337 | 35.15 346 | 52.87 360 | 27.67 360 | 57.09 353 | 47.86 362 | 24.64 356 | 16.40 361 | 33.05 358 | 11.23 360 | 54.90 359 | 14.46 359 | 18.15 356 | 22.87 358 |
|
test2356 | | | 59.50 312 | 58.08 312 | 63.74 328 | 71.23 344 | 41.88 345 | 67.59 338 | 72.42 340 | 53.72 329 | 57.65 330 | 70.74 337 | 26.31 342 | 72.40 345 | 32.03 346 | 71.06 297 | 76.93 338 |
|
test1235678 | | | 58.74 315 | 56.89 318 | 64.30 326 | 69.70 346 | 41.87 346 | 71.05 323 | 74.87 328 | 54.06 326 | 50.63 345 | 71.53 336 | 25.30 344 | 74.10 341 | 31.80 347 | 63.10 328 | 76.93 338 |
|
PGM-MVS | | | 86.68 29 | 86.27 32 | 87.90 17 | 94.22 21 | 73.38 16 | 90.22 51 | 93.04 25 | 75.53 77 | 83.86 51 | 94.42 17 | 67.87 65 | 96.64 17 | 82.70 43 | 94.57 34 | 93.66 43 |
|
LCM-MVSNet-Re | | | 77.05 199 | 76.94 175 | 77.36 265 | 87.20 185 | 51.60 323 | 80.06 277 | 80.46 289 | 75.20 85 | 67.69 283 | 86.72 188 | 62.48 130 | 88.98 264 | 63.44 201 | 89.25 85 | 91.51 109 |
|
LCM-MVSNet | | | 54.25 320 | 49.68 327 | 67.97 323 | 53.73 359 | 45.28 339 | 66.85 342 | 80.78 284 | 35.96 351 | 39.45 351 | 62.23 347 | 8.70 363 | 78.06 326 | 48.24 298 | 51.20 346 | 80.57 326 |
|
MCST-MVS | | | 87.37 20 | 87.25 18 | 87.73 22 | 94.53 10 | 72.46 34 | 89.82 57 | 93.82 7 | 73.07 130 | 84.86 39 | 92.89 48 | 76.22 6 | 96.33 25 | 84.89 21 | 95.13 24 | 94.40 14 |
|
mvs_anonymous | | | 79.42 147 | 79.11 132 | 80.34 208 | 84.45 221 | 57.97 260 | 82.59 257 | 87.62 207 | 67.40 227 | 76.17 169 | 88.56 138 | 68.47 60 | 89.59 245 | 70.65 148 | 86.05 129 | 93.47 55 |
|
MVS_Test | | | 83.15 69 | 83.06 64 | 83.41 119 | 86.86 189 | 63.21 211 | 86.11 182 | 92.00 67 | 74.31 97 | 82.87 63 | 89.44 120 | 70.03 47 | 93.21 143 | 77.39 83 | 88.50 99 | 93.81 39 |
|
MDA-MVSNet-bldmvs | | | 66.68 297 | 63.66 301 | 75.75 282 | 79.28 318 | 60.56 239 | 73.92 318 | 78.35 310 | 64.43 255 | 50.13 346 | 79.87 303 | 44.02 299 | 83.67 303 | 46.10 313 | 56.86 338 | 83.03 315 |
|
CDPH-MVS | | | 85.76 43 | 85.29 48 | 87.17 34 | 93.49 33 | 71.08 48 | 88.58 97 | 92.42 51 | 68.32 216 | 84.61 41 | 93.48 34 | 72.32 32 | 96.15 32 | 79.00 66 | 95.43 17 | 94.28 20 |
|
test12 | | | | | 86.80 40 | 92.63 49 | 70.70 58 | | 91.79 78 | | 82.71 67 | | 71.67 35 | 96.16 31 | | 94.50 35 | 93.54 53 |
|
casdiffmvs | | | 83.96 57 | 83.25 61 | 86.07 54 | 88.48 141 | 69.60 74 | 89.26 72 | 92.40 52 | 68.07 218 | 82.82 64 | 90.03 102 | 69.77 51 | 94.86 74 | 81.79 48 | 86.64 121 | 93.75 41 |
|
diffmvs | | | 81.48 96 | 81.21 89 | 82.31 167 | 83.28 259 | 62.72 220 | 85.09 212 | 88.63 191 | 74.99 87 | 78.31 119 | 88.81 130 | 65.80 83 | 91.36 213 | 79.03 65 | 86.95 116 | 92.84 75 |
|
casdiffmvs1 | | | 84.76 55 | 84.33 55 | 86.04 56 | 89.40 105 | 68.78 90 | 89.67 64 | 92.54 46 | 66.43 234 | 85.41 26 | 90.75 89 | 72.88 27 | 94.76 77 | 81.64 49 | 90.24 75 | 94.57 10 |
|
diffmvs1 | | | 82.63 76 | 82.51 70 | 82.96 143 | 83.87 244 | 63.47 203 | 85.19 208 | 89.42 156 | 75.58 76 | 81.38 80 | 89.89 105 | 67.42 70 | 91.69 201 | 81.01 53 | 88.88 88 | 93.71 42 |
|
YYNet1 | | | 65.03 303 | 62.91 305 | 71.38 307 | 75.85 330 | 56.60 283 | 69.12 333 | 74.66 333 | 57.28 315 | 54.12 338 | 77.87 316 | 45.85 289 | 74.48 339 | 49.95 287 | 61.52 332 | 83.05 314 |
|
PMMVS2 | | | 40.82 331 | 38.86 333 | 46.69 344 | 53.84 358 | 16.45 365 | 48.61 355 | 49.92 361 | 37.49 350 | 31.67 353 | 60.97 348 | 8.14 364 | 56.42 358 | 28.42 350 | 30.72 351 | 67.19 346 |
|
MDA-MVSNet_test_wron | | | 65.03 303 | 62.92 304 | 71.37 308 | 75.93 329 | 56.73 279 | 69.09 334 | 74.73 330 | 57.28 315 | 54.03 339 | 77.89 315 | 45.88 288 | 74.39 340 | 49.89 288 | 61.55 331 | 82.99 316 |
|
tpmvs | | | 71.09 270 | 69.29 270 | 76.49 273 | 82.04 283 | 56.04 292 | 78.92 290 | 81.37 281 | 64.05 259 | 67.18 289 | 78.28 312 | 49.74 267 | 89.77 241 | 49.67 289 | 72.37 287 | 83.67 307 |
|
PM-MVS | | | 66.41 300 | 64.14 300 | 73.20 301 | 73.92 335 | 56.45 284 | 78.97 289 | 64.96 356 | 63.88 263 | 64.72 306 | 80.24 299 | 19.84 351 | 83.44 305 | 66.24 180 | 64.52 326 | 79.71 329 |
|
HQP_MVS | | | 83.64 62 | 83.14 62 | 85.14 68 | 90.08 85 | 68.71 96 | 91.25 31 | 92.44 48 | 79.12 23 | 78.92 104 | 91.00 85 | 60.42 163 | 95.38 52 | 78.71 69 | 86.32 126 | 91.33 113 |
|
plane_prior7 | | | | | | 90.08 85 | 68.51 102 | | | | | | | | | | |
|
plane_prior6 | | | | | | 89.84 90 | 68.70 98 | | | | | | 60.42 163 | | | | |
|
plane_prior5 | | | | | | | | | 92.44 48 | | | | | 95.38 52 | 78.71 69 | 86.32 126 | 91.33 113 |
|
plane_prior4 | | | | | | | | | | | | 91.00 85 | | | | | |
|
plane_prior3 | | | | | | | 68.60 100 | | | 78.44 30 | 78.92 104 | | | | | | |
|
plane_prior2 | | | | | | | | 91.25 31 | | 79.12 23 | | | | | | | |
|
plane_prior1 | | | | | | 89.90 89 | | | | | | | | | | | |
|
plane_prior | | | | | | | 68.71 96 | 90.38 47 | | 77.62 34 | | | | | | 86.16 128 | |
|
PS-CasMVS | | | 78.01 175 | 78.09 153 | 77.77 258 | 87.71 172 | 54.39 306 | 88.02 114 | 91.22 97 | 77.50 40 | 73.26 207 | 88.64 134 | 60.73 156 | 88.41 273 | 61.88 215 | 73.88 278 | 90.53 145 |
|
UniMVSNet_NR-MVSNet | | | 81.88 86 | 81.54 84 | 82.92 144 | 88.46 143 | 63.46 204 | 87.13 148 | 92.37 53 | 80.19 15 | 78.38 116 | 89.14 123 | 71.66 36 | 93.05 154 | 70.05 151 | 76.46 248 | 92.25 91 |
|
PEN-MVS | | | 77.73 182 | 77.69 163 | 77.84 256 | 87.07 187 | 53.91 308 | 87.91 120 | 91.18 99 | 77.56 37 | 73.14 209 | 88.82 129 | 61.23 149 | 89.17 259 | 59.95 230 | 72.37 287 | 90.43 149 |
|
TransMVSNet (Re) | | | 75.39 230 | 74.56 221 | 77.86 255 | 85.50 207 | 57.10 273 | 86.78 162 | 86.09 228 | 72.17 151 | 71.53 237 | 87.34 169 | 63.01 110 | 89.31 251 | 56.84 259 | 61.83 330 | 87.17 257 |
|
DTE-MVSNet | | | 76.99 200 | 76.80 177 | 77.54 262 | 86.24 197 | 53.06 319 | 87.52 132 | 90.66 112 | 77.08 46 | 72.50 215 | 88.67 133 | 60.48 162 | 89.52 246 | 57.33 256 | 70.74 298 | 90.05 166 |
|
DU-MVS | | | 81.12 100 | 80.52 98 | 82.90 145 | 87.80 167 | 63.46 204 | 87.02 153 | 91.87 75 | 79.01 26 | 78.38 116 | 89.07 124 | 65.02 89 | 93.05 154 | 70.05 151 | 76.46 248 | 92.20 93 |
|
UniMVSNet (Re) | | | 81.60 92 | 81.11 90 | 83.09 131 | 88.38 146 | 64.41 185 | 87.60 124 | 93.02 27 | 78.42 31 | 78.56 109 | 88.16 147 | 69.78 50 | 93.26 142 | 69.58 157 | 76.49 247 | 91.60 106 |
|
CP-MVSNet | | | 78.22 167 | 78.34 149 | 77.84 256 | 87.83 165 | 54.54 304 | 87.94 118 | 91.17 100 | 77.65 33 | 73.48 205 | 88.49 139 | 62.24 135 | 88.43 272 | 62.19 211 | 74.07 274 | 90.55 144 |
|
WR-MVS_H | | | 78.51 162 | 78.49 143 | 78.56 245 | 88.02 154 | 56.38 287 | 88.43 99 | 92.67 42 | 77.14 43 | 73.89 203 | 87.55 165 | 66.25 77 | 89.24 252 | 58.92 239 | 73.55 281 | 90.06 165 |
|
WR-MVS | | | 79.49 144 | 79.22 131 | 80.27 211 | 88.79 132 | 58.35 253 | 85.06 213 | 88.61 192 | 78.56 29 | 77.65 136 | 88.34 143 | 63.81 98 | 90.66 232 | 64.98 194 | 77.22 230 | 91.80 105 |
|
NR-MVSNet | | | 80.23 127 | 79.38 122 | 82.78 157 | 87.80 167 | 63.34 207 | 86.31 176 | 91.09 102 | 79.01 26 | 72.17 227 | 89.07 124 | 67.20 71 | 92.81 165 | 66.08 184 | 75.65 257 | 92.20 93 |
|
Baseline_NR-MVSNet | | | 78.15 171 | 78.33 150 | 77.61 260 | 85.79 201 | 56.21 291 | 86.78 162 | 85.76 230 | 73.60 115 | 77.93 132 | 87.57 164 | 65.02 89 | 88.99 263 | 67.14 176 | 75.33 263 | 87.63 245 |
|
TranMVSNet+NR-MVSNet | | | 80.84 104 | 80.31 101 | 82.42 164 | 87.85 158 | 62.33 223 | 87.74 122 | 91.33 95 | 80.55 12 | 77.99 131 | 89.86 106 | 65.23 87 | 92.62 167 | 67.05 177 | 75.24 266 | 92.30 89 |
|
TSAR-MVS + GP. | | | 85.71 44 | 85.33 45 | 86.84 39 | 91.34 64 | 72.50 32 | 89.07 80 | 87.28 213 | 76.41 59 | 85.80 24 | 90.22 98 | 74.15 21 | 95.37 54 | 81.82 47 | 91.88 57 | 92.65 79 |
|
abl_6 | | | 85.23 51 | 84.95 51 | 86.07 54 | 92.23 55 | 70.48 60 | 90.80 38 | 92.08 62 | 73.51 118 | 85.26 29 | 94.16 23 | 62.75 118 | 95.92 38 | 82.46 46 | 91.30 64 | 91.81 104 |
|
n2 | | | | | | | | | 0.00 372 | | | | | | | | |
|
nn | | | | | | | | | 0.00 372 | | | | | | | | |
|
mPP-MVS | | | 86.67 30 | 86.32 31 | 87.72 24 | 94.41 15 | 73.55 10 | 92.74 11 | 92.22 57 | 76.87 50 | 82.81 66 | 94.25 20 | 66.44 76 | 96.24 28 | 82.88 42 | 94.28 41 | 93.38 56 |
|
door-mid | | | | | | | | | 69.98 344 | | | | | | | | |
|
DI_MVS_plusplus_test | | | 79.89 137 | 78.58 141 | 83.85 109 | 82.89 271 | 65.32 155 | 86.12 181 | 89.55 151 | 69.64 187 | 70.55 245 | 85.82 228 | 57.24 184 | 93.81 114 | 76.85 89 | 88.55 96 | 92.41 86 |
|
XVG-OURS-SEG-HR | | | 80.81 107 | 79.76 110 | 83.96 106 | 85.60 205 | 68.78 90 | 83.54 246 | 90.50 117 | 70.66 171 | 76.71 152 | 91.66 64 | 60.69 158 | 91.26 216 | 76.94 88 | 81.58 181 | 91.83 102 |
|
DWT-MVSNet_test | | | 73.70 240 | 71.86 251 | 79.21 231 | 82.91 270 | 58.94 248 | 82.34 258 | 82.17 269 | 65.21 246 | 71.05 243 | 78.31 311 | 44.21 297 | 90.17 238 | 63.29 203 | 77.28 228 | 88.53 229 |
|
MVSFormer | | | 82.85 74 | 82.05 78 | 85.24 66 | 87.35 179 | 70.21 62 | 90.50 43 | 90.38 120 | 68.55 207 | 81.32 81 | 89.47 115 | 61.68 139 | 93.46 134 | 78.98 67 | 90.26 73 | 92.05 98 |
|
jason | | | 81.39 97 | 80.29 102 | 84.70 80 | 86.63 193 | 69.90 69 | 85.95 185 | 86.77 217 | 63.24 264 | 81.07 87 | 89.47 115 | 61.08 153 | 92.15 181 | 78.33 74 | 90.07 78 | 92.05 98 |
jason: jason. |
lupinMVS | | | 81.39 97 | 80.27 103 | 84.76 79 | 87.35 179 | 70.21 62 | 85.55 202 | 86.41 221 | 62.85 270 | 81.32 81 | 88.61 135 | 61.68 139 | 92.24 180 | 78.41 73 | 90.26 73 | 91.83 102 |
|
test_djsdf | | | 80.30 125 | 79.32 124 | 83.27 123 | 83.98 242 | 65.37 154 | 90.50 43 | 90.38 120 | 68.55 207 | 76.19 166 | 88.70 131 | 56.44 189 | 93.46 134 | 78.98 67 | 80.14 200 | 90.97 123 |
|
Test4 | | | 77.83 181 | 75.90 199 | 83.62 111 | 80.24 305 | 65.25 157 | 85.27 207 | 90.67 110 | 69.03 200 | 66.48 295 | 83.75 259 | 43.07 303 | 93.00 158 | 75.93 96 | 88.66 93 | 92.62 80 |
|
HPM-MVS_fast | | | 85.35 50 | 84.95 51 | 86.57 46 | 93.69 28 | 70.58 59 | 92.15 21 | 91.62 84 | 73.89 106 | 82.67 68 | 94.09 26 | 62.60 125 | 95.54 44 | 80.93 54 | 92.93 50 | 93.57 51 |
|
PatchFormer-LS_test | | | 74.50 233 | 73.05 235 | 78.86 240 | 82.95 269 | 59.55 246 | 81.65 266 | 82.30 268 | 67.44 226 | 71.62 236 | 78.15 314 | 52.34 220 | 88.92 268 | 65.05 193 | 75.90 254 | 88.12 235 |
|
testpf | | | 56.51 319 | 57.58 316 | 53.30 339 | 71.99 343 | 41.19 348 | 46.89 356 | 69.32 348 | 58.06 307 | 52.87 343 | 69.45 341 | 27.99 341 | 72.73 344 | 59.59 234 | 62.07 329 | 45.98 354 |
|
K. test v3 | | | 71.19 269 | 68.51 275 | 79.21 231 | 83.04 267 | 57.78 265 | 84.35 233 | 76.91 319 | 72.90 134 | 62.99 315 | 82.86 267 | 39.27 321 | 91.09 225 | 61.65 218 | 52.66 344 | 88.75 214 |
|
lessismore_v0 | | | | | 78.97 238 | 81.01 298 | 57.15 272 | | 65.99 353 | | 61.16 319 | 82.82 268 | 39.12 322 | 91.34 215 | 59.67 232 | 46.92 348 | 88.43 231 |
|
SixPastTwentyTwo | | | 73.37 252 | 71.26 259 | 79.70 219 | 85.08 214 | 57.89 262 | 85.57 198 | 83.56 250 | 71.03 165 | 65.66 299 | 85.88 225 | 42.10 310 | 92.57 169 | 59.11 238 | 63.34 327 | 88.65 217 |
|
OurMVSNet-221017-0 | | | 74.26 236 | 72.42 242 | 79.80 218 | 83.76 250 | 59.59 243 | 85.92 187 | 86.64 218 | 66.39 235 | 66.96 290 | 87.58 163 | 39.46 320 | 91.60 208 | 65.76 187 | 69.27 302 | 88.22 233 |
|
HPM-MVS | | | 87.11 24 | 86.98 23 | 87.50 30 | 93.88 26 | 72.16 39 | 92.19 19 | 93.33 18 | 76.07 69 | 83.81 53 | 93.95 29 | 69.77 51 | 96.01 34 | 85.15 16 | 94.66 32 | 94.32 19 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
XVG-OURS | | | 80.41 119 | 79.23 130 | 83.97 105 | 85.64 204 | 69.02 84 | 83.03 256 | 90.39 119 | 71.09 164 | 77.63 137 | 91.49 72 | 54.62 204 | 91.35 214 | 75.71 100 | 83.47 155 | 91.54 108 |
|
XVG-ACMP-BASELINE | | | 76.11 221 | 74.27 226 | 81.62 186 | 83.20 261 | 64.67 171 | 83.60 245 | 89.75 147 | 69.75 184 | 71.85 233 | 87.09 181 | 32.78 336 | 92.11 182 | 69.99 153 | 80.43 196 | 88.09 236 |
|
LPG-MVS_test | | | 82.08 82 | 81.27 86 | 84.50 83 | 89.23 116 | 68.76 92 | 90.22 51 | 91.94 71 | 75.37 81 | 76.64 154 | 91.51 70 | 54.29 206 | 94.91 69 | 78.44 71 | 83.78 146 | 89.83 178 |
|
LGP-MVS_train | | | | | 84.50 83 | 89.23 116 | 68.76 92 | | 91.94 71 | 75.37 81 | 76.64 154 | 91.51 70 | 54.29 206 | 94.91 69 | 78.44 71 | 83.78 146 | 89.83 178 |
|
test11 | | | | | | | | | 92.23 56 | | | | | | | | |
|
door | | | | | | | | | 69.44 347 | | | | | | | | |
|
EPNet_dtu | | | 75.46 228 | 74.86 217 | 77.23 268 | 82.57 278 | 54.60 303 | 86.89 157 | 83.09 260 | 71.64 155 | 66.25 297 | 85.86 226 | 55.99 191 | 88.04 277 | 54.92 266 | 86.55 123 | 89.05 201 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CHOSEN 1792x2688 | | | 77.63 187 | 75.69 200 | 83.44 116 | 89.98 87 | 68.58 101 | 78.70 292 | 87.50 210 | 56.38 319 | 75.80 173 | 86.84 184 | 58.67 171 | 91.40 212 | 61.58 219 | 85.75 133 | 90.34 152 |
|
EPNet | | | 83.72 61 | 82.92 67 | 86.14 53 | 84.22 225 | 69.48 78 | 91.05 35 | 85.27 233 | 81.30 8 | 76.83 150 | 91.65 65 | 66.09 79 | 95.56 43 | 76.00 95 | 93.85 46 | 93.38 56 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
HQP5-MVS | | | | | | | 66.98 127 | | | | | | | | | | |
|
HQP-NCC | | | | | | 89.33 108 | | 89.17 74 | | 76.41 59 | 77.23 145 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 108 | | 89.17 74 | | 76.41 59 | 77.23 145 | | | | | | |
|
APD-MVS | | | 87.44 17 | 87.52 15 | 87.19 33 | 94.24 20 | 72.39 35 | 91.86 24 | 92.83 37 | 73.01 131 | 88.58 10 | 94.52 10 | 73.36 23 | 96.49 23 | 84.26 29 | 95.01 25 | 92.70 76 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
BP-MVS | | | | | | | | | | | | | | | 77.47 81 | | |
|
HQP4-MVS | | | | | | | | | | | 77.24 144 | | | 95.11 60 | | | 91.03 120 |
|
HQP3-MVS | | | | | | | | | 92.19 59 | | | | | | | 85.99 130 | |
|
HQP2-MVS | | | | | | | | | | | | | 60.17 166 | | | | |
|
LP | | | 61.36 311 | 57.78 314 | 72.09 304 | 75.54 333 | 58.53 252 | 67.16 341 | 75.22 324 | 51.90 337 | 54.13 337 | 69.97 339 | 37.73 328 | 80.45 316 | 32.74 343 | 55.63 340 | 77.29 336 |
|
CNVR-MVS | | | 88.93 6 | 89.13 6 | 88.33 4 | 94.77 5 | 73.82 6 | 90.51 42 | 93.00 28 | 80.90 10 | 88.06 13 | 94.06 27 | 76.43 5 | 96.84 9 | 88.48 5 | 95.99 6 | 94.34 17 |
|
NCCC | | | 88.06 9 | 88.01 12 | 88.24 6 | 94.41 15 | 73.62 8 | 91.22 33 | 92.83 37 | 81.50 7 | 85.79 25 | 93.47 36 | 73.02 26 | 97.00 8 | 84.90 19 | 94.94 26 | 94.10 23 |
|
114514_t | | | 80.68 113 | 79.51 118 | 84.20 93 | 94.09 25 | 67.27 123 | 89.64 66 | 91.11 101 | 58.75 304 | 74.08 202 | 90.72 90 | 58.10 175 | 95.04 65 | 69.70 155 | 89.42 84 | 90.30 153 |
|
CP-MVS | | | 87.11 24 | 86.92 24 | 87.68 27 | 94.20 22 | 73.86 5 | 93.98 1 | 92.82 39 | 76.62 57 | 83.68 54 | 94.46 14 | 67.93 63 | 95.95 37 | 84.20 31 | 94.39 38 | 93.23 61 |
|
DSMNet-mixed | | | 57.77 317 | 56.90 317 | 60.38 333 | 67.70 350 | 35.61 353 | 69.18 331 | 53.97 359 | 32.30 355 | 57.49 331 | 79.88 302 | 40.39 319 | 68.57 352 | 38.78 336 | 72.37 287 | 76.97 337 |
|
tpm2 | | | 73.26 255 | 71.46 255 | 78.63 243 | 83.34 257 | 56.71 281 | 80.65 273 | 80.40 290 | 56.63 318 | 73.55 204 | 82.02 279 | 51.80 238 | 91.24 217 | 56.35 261 | 78.42 219 | 87.95 238 |
|
NP-MVS | | | | | | 89.62 96 | 68.32 104 | | | | | 90.24 96 | | | | | |
|
EG-PatchMatch MVS | | | 74.04 237 | 71.82 253 | 80.71 204 | 84.92 215 | 67.42 119 | 85.86 188 | 88.08 200 | 66.04 239 | 64.22 309 | 83.85 257 | 35.10 335 | 92.56 170 | 57.44 254 | 80.83 188 | 82.16 320 |
|
tpm cat1 | | | 70.57 274 | 68.31 277 | 77.35 266 | 82.41 280 | 57.95 261 | 78.08 297 | 80.22 294 | 52.04 335 | 68.54 277 | 77.66 318 | 52.00 228 | 87.84 279 | 51.77 277 | 72.07 291 | 86.25 277 |
|
SteuartSystems-ACMMP | | | 88.72 7 | 88.86 7 | 88.32 5 | 92.14 56 | 72.96 20 | 93.73 3 | 93.67 9 | 80.19 15 | 88.10 12 | 94.80 7 | 73.76 22 | 97.11 5 | 87.51 9 | 95.82 10 | 94.90 4 |
Skip Steuart: Steuart Systems R&D Blog. |
tpmp4_e23 | | | 73.45 246 | 71.17 260 | 80.31 210 | 83.55 253 | 59.56 245 | 81.88 261 | 82.33 267 | 57.94 309 | 70.51 247 | 81.62 287 | 51.19 244 | 91.63 207 | 53.96 270 | 77.51 226 | 89.75 186 |
|
CostFormer | | | 75.24 231 | 73.90 229 | 79.27 229 | 82.65 277 | 58.27 255 | 80.80 270 | 82.73 264 | 61.57 281 | 75.33 188 | 83.13 265 | 55.52 194 | 91.07 226 | 64.98 194 | 78.34 220 | 88.45 230 |
|
CR-MVSNet | | | 73.37 252 | 71.27 258 | 79.67 221 | 81.32 295 | 65.19 159 | 75.92 307 | 80.30 291 | 59.92 293 | 72.73 213 | 81.19 289 | 52.50 217 | 86.69 284 | 59.84 231 | 77.71 222 | 87.11 260 |
|
JIA-IIPM | | | 66.32 301 | 62.82 307 | 76.82 270 | 77.09 327 | 61.72 230 | 65.34 343 | 75.38 323 | 58.04 308 | 64.51 307 | 62.32 346 | 42.05 311 | 86.51 287 | 51.45 280 | 69.22 303 | 82.21 319 |
|
Patchmtry | | | 70.74 272 | 69.16 271 | 75.49 287 | 80.72 299 | 54.07 307 | 74.94 316 | 80.30 291 | 58.34 305 | 70.01 255 | 81.19 289 | 52.50 217 | 86.54 286 | 53.37 273 | 71.09 296 | 85.87 287 |
|
PatchT | | | 68.46 290 | 67.85 284 | 70.29 314 | 80.70 300 | 43.93 342 | 72.47 320 | 74.88 327 | 60.15 291 | 70.55 245 | 76.57 322 | 49.94 266 | 81.59 311 | 50.58 282 | 74.83 269 | 85.34 290 |
|
tpmrst | | | 72.39 262 | 72.13 249 | 73.18 302 | 80.54 302 | 49.91 332 | 79.91 280 | 79.08 301 | 63.11 265 | 71.69 235 | 79.95 301 | 55.32 195 | 82.77 308 | 65.66 188 | 73.89 277 | 86.87 263 |
|
BH-w/o | | | 78.21 168 | 77.33 169 | 80.84 201 | 88.81 130 | 65.13 161 | 84.87 216 | 87.85 204 | 69.75 184 | 74.52 200 | 84.74 251 | 61.34 146 | 93.11 151 | 58.24 248 | 85.84 132 | 84.27 301 |
|
tpm | | | 72.37 264 | 71.71 254 | 74.35 296 | 82.19 282 | 52.00 320 | 79.22 286 | 77.29 317 | 64.56 254 | 72.95 211 | 83.68 262 | 51.35 241 | 83.26 307 | 58.33 247 | 75.80 255 | 87.81 242 |
|
DELS-MVS | | | 85.41 49 | 85.30 47 | 85.77 59 | 88.49 140 | 67.93 112 | 85.52 206 | 93.44 15 | 78.70 28 | 83.63 57 | 89.03 126 | 74.57 12 | 95.71 40 | 80.26 61 | 94.04 45 | 93.66 43 |
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 | | | 79.47 145 | 78.60 140 | 82.05 170 | 89.19 118 | 65.91 142 | 86.07 183 | 88.52 193 | 72.18 150 | 75.42 182 | 87.69 161 | 61.15 151 | 93.54 131 | 60.38 227 | 86.83 118 | 86.70 268 |
|
RPMNet | | | 71.62 266 | 68.94 273 | 79.67 221 | 81.32 295 | 65.19 159 | 75.92 307 | 78.30 311 | 57.60 312 | 72.73 213 | 76.45 323 | 52.30 221 | 86.69 284 | 48.14 299 | 77.71 222 | 87.11 260 |
|
no-one | | | 51.08 324 | 45.79 330 | 66.95 325 | 57.92 357 | 50.49 331 | 59.63 350 | 76.04 322 | 48.04 341 | 31.85 352 | 56.10 352 | 19.12 352 | 80.08 318 | 36.89 338 | 26.52 352 | 70.29 345 |
|
MVSTER | | | 79.01 154 | 77.88 157 | 82.38 165 | 83.07 265 | 64.80 168 | 84.08 239 | 88.95 178 | 69.01 201 | 78.69 106 | 87.17 177 | 54.70 202 | 92.43 172 | 74.69 110 | 80.57 193 | 89.89 176 |
|
CPTT-MVS | | | 83.73 60 | 83.33 60 | 84.92 76 | 93.28 36 | 70.86 55 | 92.09 22 | 90.38 120 | 68.75 204 | 79.57 97 | 92.83 50 | 60.60 161 | 93.04 156 | 80.92 55 | 91.56 61 | 90.86 126 |
|
GBi-Net | | | 78.40 163 | 77.40 167 | 81.40 192 | 87.60 174 | 63.01 215 | 88.39 103 | 89.28 160 | 71.63 156 | 75.34 185 | 87.28 170 | 54.80 198 | 91.11 220 | 62.72 205 | 79.57 208 | 90.09 161 |
|
PVSNet_Blended_VisFu | | | 82.62 77 | 81.83 82 | 84.96 73 | 90.80 74 | 69.76 71 | 88.74 93 | 91.70 82 | 69.39 188 | 78.96 103 | 88.46 140 | 65.47 85 | 94.87 73 | 74.42 111 | 88.57 95 | 90.24 154 |
|
PVSNet_BlendedMVS | | | 80.60 115 | 80.02 104 | 82.36 166 | 88.85 126 | 65.40 151 | 86.16 180 | 92.00 67 | 69.34 191 | 78.11 128 | 86.09 217 | 66.02 81 | 94.27 89 | 71.52 143 | 82.06 175 | 87.39 250 |
|
UnsupCasMVSNet_eth | | | 67.33 294 | 65.99 295 | 71.37 308 | 73.48 337 | 51.47 325 | 75.16 312 | 85.19 234 | 65.20 247 | 60.78 320 | 80.93 296 | 42.35 307 | 77.20 329 | 57.12 257 | 53.69 343 | 85.44 289 |
|
UnsupCasMVSNet_bld | | | 63.70 308 | 61.53 310 | 70.21 315 | 73.69 336 | 51.39 326 | 72.82 319 | 81.89 276 | 55.63 322 | 57.81 329 | 71.80 335 | 38.67 323 | 78.61 322 | 49.26 291 | 52.21 345 | 80.63 325 |
|
PVSNet_Blended | | | 80.98 101 | 80.34 100 | 82.90 145 | 88.85 126 | 65.40 151 | 84.43 230 | 92.00 67 | 67.62 222 | 78.11 128 | 85.05 245 | 66.02 81 | 94.27 89 | 71.52 143 | 89.50 82 | 89.01 204 |
|
FMVSNet5 | | | 69.50 283 | 67.96 282 | 74.15 298 | 82.97 268 | 55.35 300 | 80.01 278 | 82.12 271 | 62.56 274 | 63.02 313 | 81.53 288 | 36.92 330 | 81.92 310 | 48.42 293 | 74.06 275 | 85.17 294 |
|
test1 | | | 78.40 163 | 77.40 167 | 81.40 192 | 87.60 174 | 63.01 215 | 88.39 103 | 89.28 160 | 71.63 156 | 75.34 185 | 87.28 170 | 54.80 198 | 91.11 220 | 62.72 205 | 79.57 208 | 90.09 161 |
|
new_pmnet | | | 50.91 325 | 50.29 324 | 52.78 340 | 68.58 349 | 34.94 356 | 63.71 345 | 56.63 358 | 39.73 348 | 44.95 347 | 65.47 344 | 21.93 349 | 58.48 357 | 34.98 340 | 56.62 339 | 64.92 348 |
|
FMVSNet3 | | | 77.88 180 | 76.85 176 | 80.97 200 | 86.84 190 | 62.36 222 | 86.52 170 | 88.77 185 | 71.13 162 | 75.34 185 | 86.66 195 | 54.07 209 | 91.10 223 | 62.72 205 | 79.57 208 | 89.45 191 |
|
dp | | | 66.80 296 | 65.43 296 | 70.90 313 | 79.74 312 | 48.82 335 | 75.12 314 | 74.77 329 | 59.61 295 | 64.08 310 | 77.23 319 | 42.89 304 | 80.72 314 | 48.86 292 | 66.58 316 | 83.16 312 |
|
FMVSNet2 | | | 78.20 169 | 77.21 170 | 81.20 195 | 87.60 174 | 62.89 219 | 87.47 134 | 89.02 169 | 71.63 156 | 75.29 189 | 87.28 170 | 54.80 198 | 91.10 223 | 62.38 209 | 79.38 211 | 89.61 189 |
|
FMVSNet1 | | | 77.44 195 | 76.12 192 | 81.40 192 | 86.81 191 | 63.01 215 | 88.39 103 | 89.28 160 | 70.49 174 | 74.39 201 | 87.28 170 | 49.06 273 | 91.11 220 | 60.91 224 | 78.52 216 | 90.09 161 |
|
N_pmnet | | | 52.79 323 | 53.26 321 | 51.40 342 | 78.99 320 | 7.68 367 | 69.52 329 | 3.89 368 | 51.63 338 | 57.01 333 | 74.98 327 | 40.83 316 | 65.96 355 | 37.78 337 | 64.67 325 | 80.56 327 |
|
cascas | | | 76.72 205 | 74.64 219 | 82.99 137 | 85.78 202 | 65.88 143 | 82.33 259 | 89.21 165 | 60.85 286 | 72.74 212 | 81.02 293 | 47.28 280 | 93.75 120 | 67.48 171 | 85.02 135 | 89.34 192 |
|
BH-RMVSNet | | | 79.61 141 | 78.44 146 | 83.14 129 | 89.38 107 | 65.93 141 | 84.95 215 | 87.15 214 | 73.56 116 | 78.19 126 | 89.79 107 | 56.67 188 | 93.36 139 | 59.53 235 | 86.74 119 | 90.13 158 |
|
UGNet | | | 80.83 106 | 79.59 114 | 84.54 82 | 88.04 153 | 68.09 109 | 89.42 68 | 88.16 196 | 76.95 48 | 76.22 165 | 89.46 117 | 49.30 270 | 93.94 104 | 68.48 165 | 90.31 72 | 91.60 106 |
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 | | | 75.65 226 | 75.68 201 | 75.57 285 | 86.40 196 | 56.82 278 | 77.92 299 | 82.40 266 | 65.10 248 | 76.18 167 | 87.72 159 | 63.13 109 | 80.90 313 | 60.31 228 | 81.96 176 | 89.00 206 |
|
XXY-MVS | | | 75.41 229 | 75.56 202 | 74.96 290 | 83.59 252 | 57.82 264 | 80.59 274 | 83.87 246 | 66.54 233 | 74.93 197 | 88.31 144 | 63.24 103 | 80.09 317 | 62.16 212 | 76.85 240 | 86.97 262 |
|
sss | | | 73.60 244 | 73.64 230 | 73.51 300 | 82.80 272 | 55.01 301 | 76.12 305 | 81.69 278 | 62.47 275 | 74.68 199 | 85.85 227 | 57.32 181 | 78.11 325 | 60.86 225 | 80.93 186 | 87.39 250 |
|
Test_1112_low_res | | | 76.40 213 | 75.44 206 | 79.27 229 | 89.28 114 | 58.09 256 | 81.69 265 | 87.07 215 | 59.53 297 | 72.48 217 | 86.67 194 | 61.30 147 | 89.33 250 | 60.81 226 | 80.15 199 | 90.41 150 |
|
1112_ss | | | 77.40 197 | 76.43 182 | 80.32 209 | 89.11 123 | 60.41 240 | 83.65 243 | 87.72 206 | 62.13 278 | 73.05 210 | 86.72 188 | 62.58 127 | 89.97 239 | 62.11 214 | 80.80 189 | 90.59 140 |
|
ab-mvs-re | | | 7.23 342 | 9.64 343 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 86.72 188 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
ab-mvs | | | 79.51 143 | 78.97 135 | 81.14 197 | 88.46 143 | 60.91 234 | 83.84 241 | 89.24 164 | 70.36 175 | 79.03 102 | 88.87 128 | 63.23 104 | 90.21 237 | 65.12 191 | 82.57 172 | 92.28 90 |
|
TR-MVS | | | 77.44 195 | 76.18 191 | 81.20 195 | 88.24 149 | 63.24 210 | 84.61 224 | 86.40 222 | 67.55 224 | 77.81 133 | 86.48 206 | 54.10 208 | 93.15 148 | 57.75 252 | 82.72 169 | 87.20 256 |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 352 | 75.16 312 | | 55.10 323 | 66.53 294 | | 49.34 269 | | 53.98 269 | | 87.94 239 |
|
MDTV_nov1_ep13 | | | | 69.97 268 | | 83.18 262 | 53.48 311 | 77.10 303 | 80.18 295 | 60.45 287 | 69.33 267 | 80.44 297 | 48.89 274 | 86.90 283 | 51.60 279 | 78.51 217 | |
|
MIMVSNet1 | | | 68.58 288 | 66.78 293 | 73.98 299 | 80.07 307 | 51.82 321 | 80.77 271 | 84.37 240 | 64.40 256 | 59.75 325 | 82.16 275 | 36.47 331 | 83.63 304 | 42.73 330 | 70.33 299 | 86.48 271 |
|
MIMVSNet | | | 70.69 273 | 69.30 269 | 74.88 291 | 84.52 219 | 56.35 289 | 75.87 309 | 79.42 299 | 64.59 253 | 67.76 281 | 82.41 271 | 41.10 314 | 81.54 312 | 46.64 311 | 81.34 183 | 86.75 267 |
|
IterMVS-LS | | | 80.06 133 | 79.38 122 | 82.11 169 | 85.89 200 | 63.20 212 | 86.79 161 | 89.34 158 | 74.19 98 | 75.45 181 | 86.72 188 | 66.62 74 | 92.39 174 | 72.58 132 | 76.86 239 | 90.75 129 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 79.07 153 | 77.70 162 | 83.17 127 | 87.60 174 | 68.23 107 | 84.40 232 | 86.20 225 | 67.49 225 | 76.36 160 | 86.54 203 | 61.54 142 | 90.79 230 | 61.86 216 | 87.33 111 | 90.49 146 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 177 | |
|
IterMVS | | | 74.29 235 | 72.94 236 | 78.35 250 | 81.53 289 | 63.49 202 | 81.58 267 | 82.49 265 | 68.06 219 | 69.99 257 | 83.69 261 | 51.66 240 | 85.54 293 | 65.85 186 | 71.64 293 | 86.01 284 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DP-MVS Recon | | | 83.11 71 | 82.09 77 | 86.15 52 | 94.44 12 | 70.92 54 | 88.79 88 | 92.20 58 | 70.53 173 | 79.17 101 | 91.03 84 | 64.12 95 | 96.03 33 | 68.39 167 | 90.14 76 | 91.50 110 |
|
MVS_111021_LR | | | 82.61 78 | 82.11 76 | 84.11 95 | 88.82 129 | 71.58 45 | 85.15 211 | 86.16 226 | 74.69 93 | 80.47 92 | 91.04 82 | 62.29 133 | 90.55 233 | 80.33 60 | 90.08 77 | 90.20 155 |
|
DP-MVS | | | 76.78 204 | 74.57 220 | 83.42 117 | 93.29 35 | 69.46 80 | 88.55 98 | 83.70 247 | 63.98 261 | 70.20 250 | 88.89 127 | 54.01 210 | 94.80 76 | 46.66 309 | 81.88 178 | 86.01 284 |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 184 | |
|
HQP-MVS | | | 82.61 78 | 82.02 79 | 84.37 87 | 89.33 108 | 66.98 127 | 89.17 74 | 92.19 59 | 76.41 59 | 77.23 145 | 90.23 97 | 60.17 166 | 95.11 60 | 77.47 81 | 85.99 130 | 91.03 120 |
|
QAPM | | | 80.88 102 | 79.50 119 | 85.03 71 | 88.01 155 | 68.97 87 | 91.59 26 | 92.00 67 | 66.63 232 | 75.15 192 | 92.16 55 | 57.70 177 | 95.45 47 | 63.52 200 | 88.76 91 | 90.66 135 |
|
Vis-MVSNet | | | 83.46 65 | 82.80 69 | 85.43 62 | 90.25 81 | 68.74 94 | 90.30 49 | 90.13 134 | 76.33 65 | 80.87 89 | 92.89 48 | 61.00 154 | 94.20 93 | 72.45 134 | 90.97 66 | 93.35 58 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MVS-HIRNet | | | 59.14 313 | 57.67 315 | 63.57 329 | 81.65 287 | 43.50 343 | 71.73 321 | 65.06 355 | 39.59 349 | 51.43 344 | 57.73 349 | 38.34 325 | 82.58 309 | 39.53 335 | 73.95 276 | 64.62 349 |
|
IS-MVSNet | | | 83.15 69 | 82.81 68 | 84.18 94 | 89.94 88 | 63.30 208 | 91.59 26 | 88.46 194 | 79.04 25 | 79.49 98 | 92.16 55 | 65.10 88 | 94.28 88 | 67.71 168 | 91.86 58 | 94.95 3 |
|
HyFIR lowres test | | | 77.53 188 | 75.40 208 | 83.94 107 | 89.59 97 | 66.62 131 | 80.36 275 | 88.64 190 | 56.29 320 | 76.45 156 | 85.17 241 | 57.64 178 | 93.28 141 | 61.34 222 | 83.10 164 | 91.91 100 |
|
EPMVS | | | 69.02 286 | 68.16 279 | 71.59 306 | 79.61 313 | 49.80 334 | 77.40 301 | 66.93 352 | 62.82 271 | 70.01 255 | 79.05 307 | 45.79 290 | 77.86 327 | 56.58 260 | 75.26 265 | 87.13 259 |
|
PAPM_NR | | | 83.02 72 | 82.41 72 | 84.82 78 | 92.47 53 | 66.37 135 | 87.93 119 | 91.80 77 | 73.82 111 | 77.32 142 | 90.66 91 | 67.90 64 | 94.90 71 | 70.37 150 | 89.48 83 | 93.19 65 |
|
TAMVS | | | 78.89 158 | 77.51 166 | 83.03 135 | 87.80 167 | 67.79 115 | 84.72 219 | 85.05 236 | 67.63 221 | 76.75 151 | 87.70 160 | 62.25 134 | 90.82 229 | 58.53 244 | 87.13 113 | 90.49 146 |
|
PAPR | | | 81.66 91 | 80.89 93 | 83.99 104 | 90.27 80 | 64.00 194 | 86.76 164 | 91.77 81 | 68.84 203 | 77.13 149 | 89.50 113 | 67.63 66 | 94.88 72 | 67.55 170 | 88.52 98 | 93.09 67 |
|
RPSCF | | | 73.23 256 | 71.46 255 | 78.54 246 | 82.50 279 | 59.85 242 | 82.18 260 | 82.84 263 | 58.96 301 | 71.15 242 | 89.41 121 | 45.48 294 | 84.77 299 | 58.82 241 | 71.83 292 | 91.02 122 |
|
Vis-MVSNet (Re-imp) | | | 78.36 165 | 78.45 144 | 78.07 254 | 88.64 136 | 51.78 322 | 86.70 165 | 79.63 298 | 74.14 100 | 75.11 193 | 90.83 88 | 61.29 148 | 89.75 242 | 58.10 249 | 91.60 59 | 92.69 78 |
|
test_0402 | | | 72.79 261 | 70.44 264 | 79.84 217 | 88.13 151 | 65.99 140 | 85.93 186 | 84.29 241 | 65.57 245 | 67.40 287 | 85.49 236 | 46.92 282 | 92.61 168 | 35.88 339 | 74.38 273 | 80.94 324 |
|
MVS_111021_HR | | | 85.14 53 | 84.75 53 | 86.32 50 | 91.65 62 | 72.70 26 | 85.98 184 | 90.33 125 | 76.11 68 | 82.08 72 | 91.61 68 | 71.36 38 | 94.17 96 | 81.02 52 | 92.58 54 | 92.08 97 |
|
CSCG | | | 86.41 35 | 86.19 34 | 87.07 37 | 92.91 44 | 72.48 33 | 90.81 37 | 93.56 12 | 73.95 102 | 83.16 60 | 91.07 81 | 75.94 7 | 95.19 57 | 79.94 63 | 94.38 39 | 93.55 52 |
|
PatchMatch-RL | | | 72.38 263 | 70.90 262 | 76.80 271 | 88.60 137 | 67.38 121 | 79.53 282 | 76.17 321 | 62.75 272 | 69.36 266 | 82.00 280 | 45.51 293 | 84.89 298 | 53.62 272 | 80.58 192 | 78.12 332 |
|
API-MVS | | | 81.99 85 | 81.23 87 | 84.26 92 | 90.94 70 | 70.18 67 | 91.10 34 | 89.32 159 | 71.51 160 | 78.66 108 | 88.28 145 | 65.26 86 | 95.10 63 | 64.74 196 | 91.23 65 | 87.51 248 |
|
Test By Simon | | | | | | | | | | | | | 64.33 93 | | | | |
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TDRefinement | | | 67.49 292 | 64.34 299 | 76.92 269 | 73.47 338 | 61.07 232 | 84.86 217 | 82.98 261 | 59.77 294 | 58.30 328 | 85.13 242 | 26.06 343 | 87.89 278 | 47.92 302 | 60.59 335 | 81.81 322 |
|
USDC | | | 70.33 277 | 68.37 276 | 76.21 280 | 80.60 301 | 56.23 290 | 79.19 287 | 86.49 220 | 60.89 285 | 61.29 318 | 85.47 237 | 31.78 339 | 89.47 248 | 53.37 273 | 76.21 251 | 82.94 317 |
|
EPP-MVSNet | | | 83.40 67 | 83.02 65 | 84.57 81 | 90.13 82 | 64.47 183 | 92.32 17 | 90.73 109 | 74.45 96 | 79.35 100 | 91.10 79 | 69.05 58 | 95.12 59 | 72.78 128 | 87.22 112 | 94.13 22 |
|
PMMVS | | | 69.34 284 | 68.67 274 | 71.35 310 | 75.67 331 | 62.03 227 | 75.17 311 | 73.46 336 | 50.00 340 | 68.68 273 | 79.05 307 | 52.07 227 | 78.13 324 | 61.16 223 | 82.77 167 | 73.90 342 |
|
PAPM | | | 77.68 184 | 76.40 183 | 81.51 189 | 87.29 184 | 61.85 229 | 83.78 242 | 89.59 150 | 64.74 252 | 71.23 239 | 88.70 131 | 62.59 126 | 93.66 127 | 52.66 276 | 87.03 115 | 89.01 204 |
|
ACMMP | | | 85.89 42 | 85.39 44 | 87.38 31 | 93.59 31 | 72.63 29 | 92.74 11 | 93.18 23 | 76.78 52 | 80.73 90 | 93.82 31 | 64.33 93 | 96.29 26 | 82.67 44 | 90.69 69 | 93.23 61 |
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 | | | 78.08 172 | 76.79 178 | 81.97 173 | 90.40 79 | 71.07 49 | 87.59 125 | 84.55 239 | 66.03 240 | 72.38 225 | 89.64 110 | 57.56 179 | 86.04 290 | 59.61 233 | 83.35 160 | 88.79 213 |
|
PatchmatchNet | | | 73.12 257 | 71.33 257 | 78.49 248 | 83.18 262 | 60.85 235 | 79.63 281 | 78.57 309 | 64.13 258 | 71.73 234 | 79.81 304 | 51.20 243 | 85.97 291 | 57.40 255 | 76.36 250 | 88.66 216 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PHI-MVS | | | 86.43 33 | 86.17 35 | 87.24 32 | 90.88 72 | 70.96 50 | 92.27 18 | 94.07 4 | 72.45 142 | 85.22 30 | 91.90 61 | 69.47 54 | 96.42 24 | 83.28 36 | 95.94 7 | 94.35 16 |
|
F-COLMAP | | | 76.38 214 | 74.33 225 | 82.50 163 | 89.28 114 | 66.95 130 | 88.41 102 | 89.03 168 | 64.05 259 | 66.83 291 | 88.61 135 | 46.78 283 | 92.89 160 | 57.48 253 | 78.55 215 | 87.67 244 |
|
ANet_high | | | 50.57 326 | 46.10 329 | 63.99 327 | 48.67 362 | 39.13 349 | 70.99 325 | 80.85 283 | 61.39 283 | 31.18 354 | 57.70 350 | 17.02 355 | 73.65 343 | 31.22 348 | 15.89 359 | 79.18 330 |
|
PNet_i23d | | | 38.26 333 | 35.42 334 | 46.79 343 | 58.74 355 | 35.48 354 | 59.65 349 | 51.25 360 | 32.45 354 | 23.44 359 | 47.53 354 | 2.04 367 | 58.96 356 | 25.60 353 | 18.09 357 | 45.92 355 |
|
wuyk23d | | | 16.82 341 | 15.94 342 | 19.46 351 | 58.74 355 | 31.45 357 | 39.22 357 | 3.74 369 | 6.84 361 | 6.04 364 | 2.70 364 | 1.27 368 | 24.29 363 | 10.54 361 | 14.40 361 | 2.63 361 |
|
OMC-MVS | | | 82.69 75 | 81.97 81 | 84.85 77 | 88.75 134 | 67.42 119 | 87.98 115 | 90.87 107 | 74.92 90 | 79.72 96 | 91.65 65 | 62.19 136 | 93.96 102 | 75.26 107 | 86.42 125 | 93.16 66 |
|
MG-MVS | | | 83.41 66 | 83.45 58 | 83.28 122 | 92.74 47 | 62.28 225 | 88.17 112 | 89.50 153 | 75.22 84 | 81.49 79 | 92.74 54 | 66.75 73 | 95.11 60 | 72.85 127 | 91.58 60 | 92.45 84 |
|
wuykxyi23d | | | 39.76 332 | 33.18 336 | 59.51 335 | 46.98 363 | 44.01 341 | 57.70 352 | 67.74 351 | 24.13 357 | 13.98 363 | 34.33 357 | 1.27 368 | 71.33 347 | 34.23 341 | 18.23 355 | 63.18 350 |
|
AdaColmap | | | 80.58 117 | 79.42 120 | 84.06 98 | 93.09 42 | 68.91 88 | 89.36 69 | 88.97 176 | 69.27 192 | 75.70 178 | 89.69 108 | 57.20 185 | 95.77 39 | 63.06 204 | 88.41 100 | 87.50 249 |
|
uanet | | | 0.00 346 | 0.00 347 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
ITE_SJBPF | | | | | 78.22 251 | 81.77 286 | 60.57 238 | | 83.30 254 | 69.25 193 | 67.54 284 | 87.20 175 | 36.33 332 | 87.28 282 | 54.34 268 | 74.62 271 | 86.80 265 |
|
DeepMVS_CX | | | | | 27.40 350 | 40.17 366 | 26.90 361 | | 24.59 367 | 17.44 360 | 23.95 357 | 48.61 353 | 9.77 361 | 26.48 362 | 18.06 356 | 24.47 353 | 28.83 357 |
|
TinyColmap | | | 67.30 295 | 64.81 297 | 74.76 293 | 81.92 285 | 56.68 282 | 80.29 276 | 81.49 280 | 60.33 288 | 56.27 336 | 83.22 264 | 24.77 345 | 87.66 280 | 45.52 316 | 69.47 301 | 79.95 328 |
|
MAR-MVS | | | 81.84 87 | 80.70 94 | 85.27 65 | 91.32 65 | 71.53 46 | 89.82 57 | 90.92 105 | 69.77 183 | 78.50 110 | 86.21 214 | 62.36 132 | 94.52 83 | 65.36 189 | 92.05 56 | 89.77 185 |
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 | | | 64.02 307 | 62.19 308 | 69.50 317 | 70.90 345 | 53.29 312 | 76.13 304 | 77.18 318 | 52.65 334 | 58.59 326 | 80.98 294 | 23.55 346 | 76.52 331 | 53.06 275 | 66.66 315 | 78.68 331 |
|
MSDG | | | 73.36 254 | 70.99 261 | 80.49 205 | 84.51 220 | 65.80 144 | 80.71 272 | 86.13 227 | 65.70 243 | 65.46 300 | 83.74 260 | 44.60 295 | 90.91 228 | 51.13 281 | 76.89 237 | 84.74 298 |
|
LS3D | | | 76.95 202 | 74.82 218 | 83.37 120 | 90.45 77 | 67.36 122 | 89.15 78 | 86.94 216 | 61.87 280 | 69.52 263 | 90.61 92 | 51.71 239 | 94.53 82 | 46.38 312 | 86.71 120 | 88.21 234 |
|
CLD-MVS | | | 82.31 80 | 81.65 83 | 84.29 91 | 88.47 142 | 67.73 116 | 85.81 193 | 92.35 54 | 75.78 71 | 78.33 118 | 86.58 201 | 64.01 96 | 94.35 86 | 76.05 94 | 87.48 110 | 90.79 127 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
FPMVS | | | 53.68 322 | 51.64 322 | 59.81 334 | 65.08 351 | 51.03 327 | 69.48 330 | 69.58 346 | 41.46 346 | 40.67 349 | 72.32 334 | 16.46 356 | 70.00 350 | 24.24 354 | 65.42 323 | 58.40 351 |
|
Gipuma | | | 45.18 329 | 41.86 331 | 55.16 338 | 77.03 328 | 51.52 324 | 32.50 359 | 80.52 287 | 32.46 353 | 27.12 355 | 35.02 356 | 9.52 362 | 75.50 335 | 22.31 355 | 60.21 336 | 38.45 356 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |