DeepC-MVS_fast | | 96.59 1 | 98.81 22 | 98.54 26 | 99.62 15 | 99.90 41 | 98.85 29 | 99.24 209 | 98.47 99 | 98.14 4 | 99.08 73 | 99.91 13 | 93.09 113 | 100.00 1 | 99.04 52 | 99.99 20 | 100.00 1 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DeepPCF-MVS | | 95.94 2 | 97.71 82 | 98.98 10 | 93.92 258 | 99.63 84 | 81.76 327 | 99.96 23 | 98.56 76 | 99.47 1 | 99.19 70 | 99.99 1 | 94.16 86 | 100.00 1 | 99.92 9 | 99.93 62 | 100.00 1 |
|
PLC | | 95.54 3 | 97.93 70 | 97.89 66 | 98.05 133 | 99.82 63 | 94.77 182 | 99.92 65 | 98.46 101 | 93.93 121 | 97.20 132 | 99.27 123 | 95.44 44 | 99.97 51 | 97.41 117 | 99.51 104 | 99.41 156 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
DeepC-MVS | | 94.51 4 | 96.92 107 | 96.40 112 | 98.45 114 | 99.16 106 | 95.90 148 | 99.66 150 | 98.06 178 | 96.37 47 | 94.37 181 | 99.49 107 | 83.29 222 | 99.90 75 | 97.63 113 | 99.61 97 | 99.55 136 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PCF-MVS | | 94.20 5 | 95.18 155 | 94.10 168 | 98.43 116 | 98.55 136 | 95.99 146 | 97.91 293 | 97.31 250 | 90.35 225 | 89.48 237 | 99.22 129 | 85.19 208 | 99.89 79 | 90.40 235 | 98.47 127 | 99.41 156 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
IB-MVS | | 92.85 6 | 94.99 160 | 93.94 171 | 98.16 127 | 97.72 185 | 95.69 158 | 99.99 4 | 98.81 47 | 94.28 105 | 92.70 201 | 96.90 220 | 95.08 50 | 99.17 151 | 96.07 139 | 73.88 327 | 99.60 126 |
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 |
HY-MVS | | 92.50 7 | 97.79 79 | 97.17 89 | 99.63 12 | 98.98 114 | 99.32 6 | 97.49 299 | 99.52 13 | 95.69 64 | 98.32 107 | 97.41 203 | 93.32 106 | 99.77 111 | 98.08 97 | 95.75 183 | 99.81 95 |
|
TAPA-MVS | | 92.12 8 | 94.42 176 | 93.60 178 | 96.90 168 | 99.33 102 | 91.78 242 | 99.78 122 | 98.00 181 | 89.89 233 | 94.52 178 | 99.47 108 | 91.97 135 | 99.18 150 | 69.90 330 | 99.52 102 | 99.73 105 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
ACMP | | 92.05 9 | 92.74 208 | 92.42 205 | 93.73 262 | 95.91 242 | 88.72 287 | 99.81 114 | 97.53 225 | 94.13 108 | 87.00 279 | 98.23 187 | 74.07 290 | 98.47 182 | 96.22 138 | 88.86 227 | 93.99 273 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMM | | 91.95 10 | 92.88 205 | 92.52 203 | 93.98 257 | 95.75 248 | 89.08 285 | 99.77 125 | 97.52 227 | 93.00 148 | 89.95 223 | 97.99 193 | 76.17 276 | 98.46 185 | 93.63 190 | 88.87 226 | 94.39 235 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
3Dnovator+ | | 91.53 11 | 96.31 131 | 95.24 146 | 99.52 26 | 96.88 219 | 98.64 49 | 99.72 142 | 98.24 156 | 95.27 75 | 88.42 261 | 98.98 145 | 82.76 224 | 99.94 68 | 97.10 125 | 99.83 77 | 99.96 67 |
|
3Dnovator | | 91.47 12 | 96.28 134 | 95.34 144 | 99.08 71 | 96.82 222 | 97.47 96 | 99.45 185 | 98.81 47 | 95.52 68 | 89.39 238 | 99.00 142 | 81.97 227 | 99.95 60 | 97.27 120 | 99.83 77 | 99.84 92 |
|
PVSNet | | 91.05 13 | 97.13 99 | 96.69 103 | 98.45 114 | 99.52 92 | 95.81 150 | 99.95 40 | 99.65 10 | 94.73 87 | 99.04 75 | 99.21 130 | 84.48 213 | 99.95 60 | 94.92 152 | 98.74 122 | 99.58 133 |
|
COLMAP_ROB | | 90.47 14 | 92.18 221 | 91.49 222 | 94.25 245 | 99.00 113 | 88.04 298 | 98.42 275 | 96.70 299 | 82.30 314 | 88.43 259 | 99.01 140 | 76.97 267 | 99.85 94 | 86.11 278 | 96.50 168 | 94.86 221 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
OpenMVS | | 90.15 15 | 94.77 164 | 93.59 179 | 98.33 121 | 96.07 236 | 97.48 95 | 99.56 168 | 98.57 74 | 90.46 222 | 86.51 285 | 98.95 151 | 78.57 260 | 99.94 68 | 93.86 178 | 99.74 86 | 97.57 210 |
|
ACMH+ | | 89.98 16 | 90.35 257 | 89.54 253 | 92.78 282 | 95.99 239 | 86.12 306 | 98.81 251 | 97.18 258 | 89.38 236 | 83.14 306 | 97.76 197 | 68.42 311 | 98.43 187 | 89.11 246 | 86.05 253 | 93.78 289 |
|
ACMH | | 89.72 17 | 90.64 250 | 89.63 250 | 93.66 266 | 95.64 255 | 88.64 290 | 98.55 265 | 97.45 234 | 89.03 241 | 81.62 310 | 97.61 199 | 69.75 305 | 98.41 189 | 89.37 243 | 87.62 244 | 93.92 279 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
LTVRE_ROB | | 88.28 18 | 90.29 260 | 89.05 264 | 94.02 253 | 95.08 263 | 90.15 273 | 97.19 303 | 97.43 237 | 84.91 300 | 83.99 303 | 97.06 215 | 74.00 291 | 98.28 205 | 84.08 288 | 87.71 242 | 93.62 296 |
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 |
PVSNet_0 | | 88.03 19 | 91.80 229 | 90.27 240 | 96.38 185 | 98.27 150 | 90.46 267 | 99.94 55 | 99.61 11 | 93.99 117 | 86.26 291 | 97.39 205 | 71.13 302 | 99.89 79 | 98.77 69 | 67.05 336 | 98.79 194 |
|
OpenMVS_ROB | | 79.82 20 | 83.77 303 | 81.68 304 | 90.03 306 | 88.30 332 | 82.82 319 | 98.46 270 | 95.22 327 | 73.92 335 | 76.00 327 | 91.29 324 | 55.00 337 | 96.94 270 | 68.40 332 | 88.51 235 | 90.34 328 |
|
CMPMVS | | 61.59 21 | 84.75 298 | 85.14 291 | 83.57 320 | 90.32 326 | 62.54 342 | 96.98 306 | 97.59 219 | 74.33 334 | 69.95 335 | 96.66 229 | 64.17 323 | 98.32 201 | 87.88 259 | 88.41 236 | 89.84 332 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MVE | | 53.74 22 | 51.54 318 | 47.86 321 | 62.60 330 | 59.56 350 | 50.93 347 | 79.41 344 | 77.69 351 | 35.69 347 | 36.27 349 | 61.76 348 | 5.79 357 | 69.63 347 | 37.97 347 | 36.61 343 | 67.24 342 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
PMVS | | 49.05 23 | 53.75 316 | 51.34 319 | 60.97 331 | 40.80 353 | 34.68 353 | 74.82 345 | 89.62 347 | 37.55 345 | 28.67 351 | 72.12 341 | 7.09 355 | 81.63 345 | 43.17 346 | 68.21 334 | 66.59 343 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
SED-MVS | | | 99.28 5 | 99.11 6 | 99.77 6 | 99.93 26 | 99.30 8 | 99.96 23 | 98.43 112 | 97.27 20 | 99.80 16 | 99.94 4 | 96.71 20 | 100.00 1 | 100.00 1 | 100.00 1 | 100.00 1 |
|
IU-MVS | | | | | | 99.93 26 | 99.31 7 | | 98.41 127 | 97.71 8 | 99.84 8 | | | | 100.00 1 | 100.00 1 | 100.00 1 |
|
OPU-MVS | | | | | 99.93 2 | 99.89 44 | 99.80 2 | 99.96 23 | | | | 99.80 57 | 97.44 11 | 100.00 1 | 100.00 1 | 99.98 33 | 100.00 1 |
|
test_241102_TWO | | | | | | | | | 98.43 112 | 97.27 20 | 99.80 16 | 99.94 4 | 97.18 17 | 100.00 1 | 100.00 1 | 100.00 1 | 100.00 1 |
|
test_241102_ONE | | | | | | 99.93 26 | 99.30 8 | | 98.43 112 | 97.26 22 | 99.80 16 | 99.88 22 | 96.71 20 | 100.00 1 | | | |
|
xxxxxxxxxxxxxcwj | | | 98.98 14 | 98.79 15 | 99.54 23 | 99.82 63 | 98.79 33 | 99.96 23 | 97.52 227 | 97.66 10 | 99.81 12 | 99.89 19 | 94.70 64 | 99.86 90 | 99.84 13 | 99.93 62 | 99.96 67 |
|
SF-MVS | | | 98.67 30 | 98.40 35 | 99.50 29 | 99.77 69 | 98.67 44 | 99.90 73 | 98.21 160 | 93.53 135 | 99.81 12 | 99.89 19 | 94.70 64 | 99.86 90 | 99.84 13 | 99.93 62 | 99.96 67 |
|
ETH3D cwj APD-0.16 | | | 98.40 51 | 98.07 56 | 99.40 41 | 99.59 86 | 98.41 59 | 99.86 98 | 98.24 156 | 92.18 181 | 99.73 26 | 99.87 26 | 93.47 102 | 99.85 94 | 99.74 24 | 99.95 50 | 99.93 78 |
|
cl-mvsnet2 | | | 93.77 188 | 93.25 191 | 95.33 206 | 99.49 95 | 94.43 186 | 99.61 161 | 98.09 175 | 90.38 223 | 89.16 247 | 95.61 256 | 90.56 156 | 97.34 241 | 91.93 208 | 84.45 266 | 94.21 250 |
|
miper_ehance_all_eth | | | 93.16 199 | 92.60 199 | 94.82 222 | 97.57 191 | 93.56 202 | 99.50 177 | 97.07 267 | 88.75 250 | 88.85 251 | 95.52 262 | 90.97 151 | 96.74 279 | 90.77 228 | 84.45 266 | 94.17 252 |
|
miper_enhance_ethall | | | 94.36 179 | 93.98 170 | 95.49 201 | 98.68 134 | 95.24 169 | 99.73 139 | 97.29 251 | 93.28 142 | 89.86 226 | 95.97 247 | 94.37 74 | 97.05 262 | 92.20 206 | 84.45 266 | 94.19 251 |
|
ZNCC-MVS | | | 98.31 55 | 98.03 57 | 99.17 57 | 99.88 48 | 97.59 86 | 99.94 55 | 98.44 104 | 94.31 103 | 98.50 99 | 99.82 52 | 93.06 114 | 99.99 36 | 98.30 88 | 99.99 20 | 99.93 78 |
|
ETH3 D test6400 | | | 98.81 22 | 98.54 26 | 99.59 18 | 99.93 26 | 98.93 22 | 99.93 61 | 98.46 101 | 94.56 92 | 99.84 8 | 99.92 11 | 94.32 79 | 99.86 90 | 99.96 8 | 99.98 33 | 100.00 1 |
|
cl-mvsnet_ | | | 92.31 218 | 91.58 218 | 94.52 233 | 97.33 202 | 92.77 215 | 99.57 166 | 96.78 296 | 86.97 277 | 87.56 271 | 95.51 263 | 89.43 167 | 96.62 284 | 88.60 249 | 82.44 278 | 94.16 257 |
|
cl-mvsnet1 | | | 92.32 217 | 91.60 217 | 94.47 237 | 97.31 203 | 92.74 217 | 99.58 164 | 96.75 297 | 86.99 276 | 87.64 269 | 95.54 260 | 89.55 166 | 96.50 288 | 88.58 250 | 82.44 278 | 94.17 252 |
|
eth_miper_zixun_eth | | | 92.41 216 | 91.93 212 | 93.84 261 | 97.28 205 | 90.68 261 | 98.83 250 | 96.97 279 | 88.57 255 | 89.19 246 | 95.73 253 | 89.24 172 | 96.69 282 | 89.97 240 | 81.55 284 | 94.15 258 |
|
9.14 | | | | 98.38 38 | | 99.87 50 | | 99.91 69 | 98.33 145 | 93.22 143 | 99.78 22 | 99.89 19 | 94.57 67 | 99.85 94 | 99.84 13 | 99.97 44 | |
|
testtj | | | 98.89 18 | 98.69 18 | 99.52 26 | 99.94 14 | 98.56 52 | 99.90 73 | 98.55 80 | 95.14 77 | 99.72 29 | 99.84 46 | 95.46 43 | 100.00 1 | 99.65 31 | 99.99 20 | 99.99 20 |
|
uanet_test | | | 0.00 326 | 0.00 328 | 0.00 337 | 0.00 356 | 0.00 357 | 0.00 348 | 0.00 358 | 0.00 352 | 0.00 353 | 0.00 353 | 0.00 358 | 0.00 354 | 0.00 351 | 0.00 351 | 0.00 350 |
|
ETH3D-3000-0.1 | | | 98.68 29 | 98.42 31 | 99.47 34 | 99.83 61 | 98.57 51 | 99.90 73 | 98.37 139 | 93.81 126 | 99.81 12 | 99.90 17 | 94.34 75 | 99.86 90 | 99.84 13 | 99.98 33 | 99.97 62 |
|
save fliter | | | | | | 99.82 63 | 98.79 33 | 99.96 23 | 98.40 129 | 97.66 10 | | | | | | | |
|
ET-MVSNet_ETH3D | | | 94.37 177 | 93.28 190 | 97.64 147 | 98.30 145 | 97.99 73 | 99.99 4 | 97.61 215 | 94.35 100 | 71.57 333 | 99.45 111 | 96.23 27 | 95.34 316 | 96.91 132 | 85.14 262 | 99.59 127 |
|
UniMVSNet_ETH3D | | | 90.06 266 | 88.58 271 | 94.49 236 | 94.67 270 | 88.09 297 | 97.81 295 | 97.57 220 | 83.91 306 | 88.44 257 | 97.41 203 | 57.44 335 | 97.62 232 | 91.41 214 | 88.59 233 | 97.77 208 |
|
EIA-MVS | | | 97.53 86 | 97.46 78 | 97.76 143 | 98.04 162 | 94.84 178 | 99.98 8 | 97.61 215 | 94.41 98 | 97.90 119 | 99.59 99 | 92.40 126 | 98.87 159 | 98.04 98 | 99.13 115 | 99.59 127 |
|
miper_lstm_enhance | | | 91.81 226 | 91.39 224 | 93.06 278 | 97.34 200 | 89.18 284 | 99.38 193 | 96.79 295 | 86.70 280 | 87.47 273 | 95.22 280 | 90.00 161 | 95.86 311 | 88.26 254 | 81.37 286 | 94.15 258 |
|
ETV-MVS | | | 97.92 71 | 97.80 68 | 98.25 125 | 98.14 158 | 96.48 125 | 99.98 8 | 97.63 209 | 95.61 66 | 99.29 64 | 99.46 110 | 92.55 125 | 98.82 161 | 99.02 54 | 98.54 125 | 99.46 149 |
|
CS-MVS | | | 97.84 74 | 97.69 70 | 98.31 122 | 98.28 147 | 96.27 131 | 100.00 1 | 97.52 227 | 95.29 73 | 99.25 67 | 99.65 95 | 91.18 147 | 98.94 158 | 98.96 55 | 99.04 117 | 99.73 105 |
|
D2MVS | | | 92.76 207 | 92.59 201 | 93.27 272 | 95.13 261 | 89.54 281 | 99.69 145 | 99.38 21 | 92.26 179 | 87.59 270 | 94.61 299 | 85.05 210 | 97.79 226 | 91.59 213 | 88.01 239 | 92.47 314 |
|
MSP-MVS | | | 99.30 4 | 99.16 3 | 99.73 8 | 99.93 26 | 99.29 10 | 99.95 40 | 98.32 147 | 97.28 18 | 99.83 10 | 99.91 13 | 97.22 15 | 100.00 1 | 99.99 5 | 100.00 1 | 99.89 87 |
|
test_0728_THIRD | | | | | | | | | | 96.48 40 | 99.83 10 | 99.91 13 | 97.87 4 | 100.00 1 | 99.92 9 | 100.00 1 | 100.00 1 |
|
test_0728_SECOND | | | | | 99.82 5 | 99.94 14 | 99.47 5 | 99.95 40 | 98.43 112 | | | | | 100.00 1 | 99.99 5 | 100.00 1 | 100.00 1 |
|
test0726 | | | | | | 99.93 26 | 99.29 10 | 99.96 23 | 98.42 123 | 97.28 18 | 99.86 4 | 99.94 4 | 97.22 15 | | | | |
|
SR-MVS | | | 98.46 45 | 98.30 46 | 98.93 84 | 99.88 48 | 97.04 110 | 99.84 105 | 98.35 142 | 94.92 80 | 99.32 60 | 99.80 57 | 93.35 104 | 99.78 110 | 99.30 42 | 99.95 50 | 99.96 67 |
|
DPM-MVS | | | 98.83 21 | 98.46 30 | 99.97 1 | 99.33 102 | 99.92 1 | 99.96 23 | 98.44 104 | 97.96 7 | 99.55 42 | 99.94 4 | 97.18 17 | 100.00 1 | 93.81 182 | 99.94 56 | 99.98 51 |
|
GST-MVS | | | 98.27 57 | 97.97 61 | 99.17 57 | 99.92 35 | 97.57 87 | 99.93 61 | 98.39 132 | 94.04 116 | 98.80 85 | 99.74 77 | 92.98 115 | 100.00 1 | 98.16 91 | 99.76 85 | 99.93 78 |
|
test_yl | | | 97.83 75 | 97.37 81 | 99.21 51 | 99.18 104 | 97.98 74 | 99.64 157 | 99.27 25 | 91.43 204 | 97.88 120 | 98.99 143 | 95.84 35 | 99.84 103 | 98.82 64 | 95.32 190 | 99.79 97 |
|
thisisatest0530 | | | 97.10 100 | 96.72 102 | 98.22 126 | 97.60 190 | 96.70 119 | 99.92 65 | 98.54 84 | 91.11 211 | 97.07 136 | 98.97 147 | 97.47 9 | 99.03 153 | 93.73 187 | 96.09 173 | 98.92 186 |
|
Anonymous20240529 | | | 92.10 222 | 90.65 232 | 96.47 179 | 98.82 127 | 90.61 263 | 98.72 256 | 98.67 58 | 75.54 331 | 93.90 188 | 98.58 175 | 66.23 317 | 99.90 75 | 94.70 163 | 90.67 214 | 98.90 189 |
|
Anonymous202405211 | | | 93.10 201 | 91.99 211 | 96.40 183 | 99.10 108 | 89.65 279 | 98.88 244 | 97.93 189 | 83.71 307 | 94.00 186 | 98.75 165 | 68.79 307 | 99.88 85 | 95.08 150 | 91.71 213 | 99.68 112 |
|
DCV-MVSNet | | | 97.83 75 | 97.37 81 | 99.21 51 | 99.18 104 | 97.98 74 | 99.64 157 | 99.27 25 | 91.43 204 | 97.88 120 | 98.99 143 | 95.84 35 | 99.84 103 | 98.82 64 | 95.32 190 | 99.79 97 |
|
tttt0517 | | | 96.85 108 | 96.49 109 | 97.92 137 | 97.48 196 | 95.89 149 | 99.85 101 | 98.54 84 | 90.72 220 | 96.63 145 | 98.93 153 | 97.47 9 | 99.02 154 | 93.03 200 | 95.76 182 | 98.85 190 |
|
our_test_3 | | | 90.39 255 | 89.48 257 | 93.12 275 | 92.40 306 | 89.57 280 | 99.33 199 | 96.35 307 | 87.84 264 | 85.30 297 | 94.99 288 | 84.14 216 | 96.09 304 | 80.38 309 | 84.56 265 | 93.71 295 |
|
thisisatest0515 | | | 97.41 91 | 97.02 95 | 98.59 102 | 97.71 187 | 97.52 90 | 99.97 16 | 98.54 84 | 91.83 191 | 97.45 128 | 99.04 137 | 97.50 8 | 99.10 152 | 94.75 160 | 96.37 170 | 99.16 176 |
|
ppachtmachnet_test | | | 89.58 272 | 88.35 274 | 93.25 273 | 92.40 306 | 90.44 268 | 99.33 199 | 96.73 298 | 85.49 296 | 85.90 295 | 95.77 250 | 81.09 237 | 96.00 309 | 76.00 324 | 82.49 277 | 93.30 303 |
|
SMA-MVS | | | 98.76 26 | 98.48 29 | 99.62 15 | 99.87 50 | 98.87 27 | 99.86 98 | 98.38 136 | 93.19 144 | 99.77 23 | 99.94 4 | 95.54 40 | 100.00 1 | 99.74 24 | 99.99 20 | 100.00 1 |
|
GSMVS | | | | | | | | | | | | | | | | | 99.59 127 |
|
DPE-MVS | | | 99.26 6 | 99.10 7 | 99.74 7 | 99.89 44 | 99.24 14 | 99.87 87 | 98.44 104 | 97.48 15 | 99.64 34 | 99.94 4 | 96.68 22 | 99.99 36 | 99.99 5 | 100.00 1 | 99.99 20 |
|
test_part2 | | | | | | 99.89 44 | 99.25 13 | | | | 99.49 48 | | | | | | |
|
test_part1 | | | | | 0.00 337 | | 0.00 357 | 0.00 348 | 98.41 127 | | | | 0.00 358 | 0.00 354 | 0.00 351 | 0.00 351 | 0.00 350 |
|
thres100view900 | | | 96.74 115 | 95.92 130 | 99.18 54 | 98.90 124 | 98.77 36 | 99.74 134 | 99.71 5 | 92.59 168 | 95.84 161 | 98.86 160 | 89.25 170 | 99.50 140 | 93.84 179 | 94.57 194 | 99.27 169 |
|
tfpnnormal | | | 89.29 276 | 87.61 282 | 94.34 243 | 94.35 274 | 94.13 192 | 98.95 238 | 98.94 36 | 83.94 304 | 84.47 301 | 95.51 263 | 74.84 285 | 97.39 238 | 77.05 321 | 80.41 296 | 91.48 322 |
|
tfpn200view9 | | | 96.79 111 | 95.99 120 | 99.19 53 | 98.94 117 | 98.82 31 | 99.78 122 | 99.71 5 | 92.86 150 | 96.02 158 | 98.87 158 | 89.33 168 | 99.50 140 | 93.84 179 | 94.57 194 | 99.27 169 |
|
cl_fuxian | | | 92.53 213 | 91.87 214 | 94.52 233 | 97.40 197 | 92.99 213 | 99.40 188 | 96.93 284 | 87.86 263 | 88.69 254 | 95.44 266 | 89.95 162 | 96.44 290 | 90.45 232 | 80.69 295 | 94.14 261 |
|
CHOSEN 280x420 | | | 99.01 12 | 99.03 8 | 98.95 83 | 99.38 100 | 98.87 27 | 98.46 270 | 99.42 20 | 97.03 27 | 99.02 76 | 99.09 134 | 99.35 1 | 98.21 209 | 99.73 27 | 99.78 84 | 99.77 101 |
|
CANet | | | 98.27 57 | 97.82 67 | 99.63 12 | 99.72 79 | 99.10 17 | 99.98 8 | 98.51 93 | 97.00 28 | 98.52 97 | 99.71 82 | 87.80 183 | 99.95 60 | 99.75 22 | 99.38 109 | 99.83 93 |
|
Fast-Effi-MVS+-dtu | | | 93.72 191 | 93.86 174 | 93.29 271 | 97.06 210 | 86.16 305 | 99.80 119 | 96.83 291 | 92.66 163 | 92.58 202 | 97.83 196 | 81.39 233 | 97.67 230 | 89.75 242 | 96.87 163 | 96.05 220 |
|
Effi-MVS+-dtu | | | 94.53 174 | 95.30 145 | 92.22 286 | 97.77 178 | 82.54 321 | 99.59 163 | 97.06 268 | 94.92 80 | 95.29 171 | 95.37 272 | 85.81 201 | 97.89 224 | 94.80 157 | 97.07 158 | 96.23 218 |
|
CANet_DTU | | | 96.76 113 | 96.15 116 | 98.60 100 | 98.78 130 | 97.53 89 | 99.84 105 | 97.63 209 | 97.25 23 | 99.20 68 | 99.64 96 | 81.36 234 | 99.98 42 | 92.77 202 | 98.89 118 | 98.28 199 |
|
MVS_0304 | | | 89.28 277 | 88.31 275 | 92.21 287 | 97.05 211 | 86.53 304 | 97.76 296 | 99.57 12 | 85.58 295 | 93.86 189 | 92.71 318 | 51.04 341 | 96.30 296 | 84.49 287 | 92.72 212 | 93.79 288 |
|
MP-MVS-pluss | | | 98.07 66 | 97.64 72 | 99.38 44 | 99.74 73 | 98.41 59 | 99.74 134 | 98.18 165 | 93.35 139 | 96.45 150 | 99.85 33 | 92.64 123 | 99.97 51 | 98.91 61 | 99.89 70 | 99.77 101 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
DVP-MVS | | | 99.09 8 | 99.12 5 | 98.98 80 | 99.93 26 | 97.24 102 | 99.95 40 | 98.42 123 | 97.50 14 | 99.52 47 | 99.88 22 | 97.43 12 | 99.71 124 | 99.50 34 | 99.98 33 | 100.00 1 |
|
sam_mvs1 | | | | | | | | | | | | | 94.72 63 | | | | 99.59 127 |
|
sam_mvs | | | | | | | | | | | | | 94.25 81 | | | | |
|
IterMVS-SCA-FT | | | 90.85 246 | 90.16 244 | 92.93 279 | 96.72 228 | 89.96 274 | 98.89 242 | 96.99 275 | 88.95 246 | 86.63 283 | 95.67 254 | 76.48 272 | 95.00 320 | 87.04 269 | 84.04 272 | 93.84 285 |
|
TSAR-MVS + MP. | | | 98.93 15 | 98.77 16 | 99.41 39 | 99.74 73 | 98.67 44 | 99.77 125 | 98.38 136 | 96.73 35 | 99.88 3 | 99.74 77 | 94.89 61 | 99.59 135 | 99.80 18 | 99.98 33 | 99.97 62 |
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 | | | 97.43 87 | 97.06 90 | 98.55 104 | 97.74 181 | 98.14 66 | 99.31 202 | 97.86 197 | 96.43 41 | 99.62 37 | 99.69 87 | 85.56 204 | 99.68 128 | 99.05 48 | 98.31 131 | 97.83 205 |
|
OPM-MVS | | | 93.21 198 | 92.80 195 | 94.44 239 | 93.12 295 | 90.85 259 | 99.77 125 | 97.61 215 | 96.19 51 | 91.56 207 | 98.65 169 | 75.16 284 | 98.47 182 | 93.78 185 | 89.39 221 | 93.99 273 |
|
ACMMP_NAP | | | 98.49 43 | 98.14 52 | 99.54 23 | 99.66 83 | 98.62 50 | 99.85 101 | 98.37 139 | 94.68 89 | 99.53 44 | 99.83 49 | 92.87 116 | 100.00 1 | 98.66 76 | 99.84 76 | 99.99 20 |
|
ambc | | | | | 83.23 321 | 77.17 342 | 62.61 341 | 87.38 341 | 94.55 335 | | 76.72 325 | 86.65 333 | 30.16 345 | 96.36 293 | 84.85 286 | 69.86 329 | 90.73 327 |
|
zzz-MVS | | | 98.33 54 | 98.00 59 | 99.30 47 | 99.85 53 | 97.93 77 | 99.80 119 | 98.28 151 | 95.76 60 | 97.18 133 | 99.88 22 | 92.74 120 | 100.00 1 | 98.67 73 | 99.88 72 | 99.99 20 |
|
MTGPA | | | | | | | | | 98.28 151 | | | | | | | | |
|
mvs-test1 | | | 95.53 149 | 95.97 125 | 94.20 246 | 97.77 178 | 85.44 311 | 99.95 40 | 97.06 268 | 94.92 80 | 96.58 146 | 98.72 166 | 85.81 201 | 98.98 155 | 94.80 157 | 98.11 135 | 98.18 200 |
|
Effi-MVS+ | | | 96.30 132 | 95.69 136 | 98.16 127 | 97.85 173 | 96.26 133 | 97.41 300 | 97.21 255 | 90.37 224 | 98.65 93 | 98.58 175 | 86.61 196 | 98.70 172 | 97.11 124 | 97.37 152 | 99.52 143 |
|
xiu_mvs_v2_base | | | 98.23 61 | 97.97 61 | 99.02 77 | 98.69 133 | 98.66 46 | 99.52 174 | 98.08 177 | 97.05 26 | 99.86 4 | 99.86 29 | 90.65 154 | 99.71 124 | 99.39 40 | 98.63 124 | 98.69 196 |
|
xiu_mvs_v1_base | | | 97.43 87 | 97.06 90 | 98.55 104 | 97.74 181 | 98.14 66 | 99.31 202 | 97.86 197 | 96.43 41 | 99.62 37 | 99.69 87 | 85.56 204 | 99.68 128 | 99.05 48 | 98.31 131 | 97.83 205 |
|
new-patchmatchnet | | | 81.19 305 | 79.34 308 | 86.76 318 | 82.86 339 | 80.36 334 | 97.92 292 | 95.27 326 | 82.09 315 | 72.02 332 | 86.87 332 | 62.81 327 | 90.74 338 | 71.10 328 | 63.08 338 | 89.19 336 |
|
pmmvs6 | | | 85.69 290 | 83.84 295 | 91.26 296 | 90.00 328 | 84.41 316 | 97.82 294 | 96.15 311 | 75.86 329 | 81.29 311 | 95.39 270 | 61.21 330 | 96.87 274 | 83.52 295 | 73.29 328 | 92.50 313 |
|
pmmvs5 | | | 90.17 264 | 89.09 262 | 93.40 269 | 92.10 310 | 89.77 278 | 99.74 134 | 95.58 321 | 85.88 289 | 87.24 278 | 95.74 251 | 73.41 293 | 96.48 289 | 88.54 251 | 83.56 274 | 93.95 276 |
|
test_post1 | | | | | | | | 95.78 321 | | | | 59.23 350 | 93.20 111 | 97.74 228 | 91.06 220 | | |
|
test_post | | | | | | | | | | | | 63.35 347 | 94.43 68 | 98.13 211 | | | |
|
Fast-Effi-MVS+ | | | 95.02 159 | 94.19 165 | 97.52 151 | 97.88 169 | 94.55 184 | 99.97 16 | 97.08 266 | 88.85 249 | 94.47 180 | 97.96 194 | 84.59 212 | 98.41 189 | 89.84 241 | 97.10 157 | 99.59 127 |
|
patchmatchnet-post | | | | | | | | | | | | 91.70 323 | 95.12 48 | 97.95 221 | | | |
|
Anonymous20231211 | | | 89.86 268 | 88.44 273 | 94.13 249 | 98.93 119 | 90.68 261 | 98.54 267 | 98.26 155 | 76.28 327 | 86.73 281 | 95.54 260 | 70.60 303 | 97.56 233 | 90.82 227 | 80.27 299 | 94.15 258 |
|
pmmvs-eth3d | | | 84.03 302 | 81.97 302 | 90.20 304 | 84.15 337 | 87.09 302 | 98.10 288 | 94.73 333 | 83.05 309 | 74.10 331 | 87.77 330 | 65.56 320 | 94.01 326 | 81.08 307 | 69.24 332 | 89.49 334 |
|
GG-mvs-BLEND | | | | | 98.54 107 | 98.21 153 | 98.01 72 | 93.87 327 | 98.52 87 | | 97.92 118 | 97.92 195 | 99.02 2 | 97.94 223 | 98.17 90 | 99.58 99 | 99.67 114 |
|
xiu_mvs_v1_base_debi | | | 97.43 87 | 97.06 90 | 98.55 104 | 97.74 181 | 98.14 66 | 99.31 202 | 97.86 197 | 96.43 41 | 99.62 37 | 99.69 87 | 85.56 204 | 99.68 128 | 99.05 48 | 98.31 131 | 97.83 205 |
|
Anonymous20231206 | | | 86.32 288 | 85.42 289 | 89.02 311 | 89.11 331 | 80.53 333 | 99.05 229 | 95.28 325 | 85.43 297 | 82.82 307 | 93.92 308 | 74.40 288 | 93.44 333 | 66.99 334 | 81.83 283 | 93.08 308 |
|
MTAPA | | | 98.29 56 | 97.96 64 | 99.30 47 | 99.85 53 | 97.93 77 | 99.39 192 | 98.28 151 | 95.76 60 | 97.18 133 | 99.88 22 | 92.74 120 | 100.00 1 | 98.67 73 | 99.88 72 | 99.99 20 |
|
MTMP | | | | | | | | 99.87 87 | 96.49 305 | | | | | | | | |
|
gm-plane-assit | | | | | | 96.97 215 | 93.76 199 | | | 91.47 202 | | 98.96 149 | | 98.79 163 | 94.92 152 | | |
|
test9_res | | | | | | | | | | | | | | | 99.71 29 | 99.99 20 | 100.00 1 |
|
MVP-Stereo | | | 90.93 242 | 90.45 236 | 92.37 285 | 91.25 320 | 88.76 286 | 98.05 290 | 96.17 310 | 87.27 271 | 84.04 302 | 95.30 275 | 78.46 262 | 97.27 249 | 83.78 292 | 99.70 90 | 91.09 323 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
TEST9 | | | | | | 99.92 35 | 98.92 23 | 99.96 23 | 98.43 112 | 93.90 123 | 99.71 30 | 99.86 29 | 95.88 34 | 99.85 94 | | | |
|
train_agg | | | 98.88 19 | 98.65 20 | 99.59 18 | 99.92 35 | 98.92 23 | 99.96 23 | 98.43 112 | 94.35 100 | 99.71 30 | 99.86 29 | 95.94 31 | 99.85 94 | 99.69 30 | 99.98 33 | 99.99 20 |
|
gg-mvs-nofinetune | | | 93.51 194 | 91.86 215 | 98.47 112 | 97.72 185 | 97.96 76 | 92.62 331 | 98.51 93 | 74.70 333 | 97.33 130 | 69.59 343 | 98.91 3 | 97.79 226 | 97.77 110 | 99.56 100 | 99.67 114 |
|
SCA | | | 94.69 166 | 93.81 175 | 97.33 161 | 97.10 208 | 94.44 185 | 98.86 248 | 98.32 147 | 93.30 141 | 96.17 157 | 95.59 258 | 76.48 272 | 97.95 221 | 91.06 220 | 97.43 148 | 99.59 127 |
|
Patchmatch-test | | | 92.65 212 | 91.50 221 | 96.10 191 | 96.85 220 | 90.49 266 | 91.50 336 | 97.19 256 | 82.76 312 | 90.23 218 | 95.59 258 | 95.02 53 | 98.00 217 | 77.41 318 | 96.98 161 | 99.82 94 |
|
test_8 | | | | | | 99.92 35 | 98.88 26 | 99.96 23 | 98.43 112 | 94.35 100 | 99.69 32 | 99.85 33 | 95.94 31 | 99.85 94 | | | |
|
MS-PatchMatch | | | 90.65 249 | 90.30 239 | 91.71 293 | 94.22 276 | 85.50 310 | 98.24 281 | 97.70 205 | 88.67 252 | 86.42 288 | 96.37 238 | 67.82 313 | 98.03 216 | 83.62 293 | 99.62 94 | 91.60 321 |
|
Patchmatch-RL test | | | 86.90 287 | 85.98 288 | 89.67 308 | 84.45 336 | 75.59 336 | 89.71 339 | 92.43 340 | 86.89 278 | 77.83 322 | 90.94 325 | 94.22 82 | 93.63 331 | 87.75 260 | 69.61 330 | 99.79 97 |
|
cdsmvs_eth3d_5k | | | 23.43 322 | 31.24 324 | 0.00 337 | 0.00 356 | 0.00 357 | 0.00 348 | 98.09 175 | 0.00 352 | 0.00 353 | 99.67 91 | 83.37 221 | 0.00 354 | 0.00 351 | 0.00 351 | 0.00 350 |
|
pcd_1.5k_mvsjas | | | 7.60 325 | 10.13 327 | 0.00 337 | 0.00 356 | 0.00 357 | 0.00 348 | 0.00 358 | 0.00 352 | 0.00 353 | 0.00 353 | 91.20 144 | 0.00 354 | 0.00 351 | 0.00 351 | 0.00 350 |
|
agg_prior1 | | | 98.88 19 | 98.66 19 | 99.54 23 | 99.93 26 | 98.77 36 | 99.96 23 | 98.43 112 | 94.63 91 | 99.63 35 | 99.85 33 | 95.79 37 | 99.85 94 | 99.72 28 | 99.99 20 | 99.99 20 |
|
agg_prior2 | | | | | | | | | | | | | | | 99.48 35 | 100.00 1 | 100.00 1 |
|
agg_prior | | | | | | 99.93 26 | 98.77 36 | | 98.43 112 | | 99.63 35 | | | 99.85 94 | | | |
|
tmp_tt | | | 65.23 314 | 62.94 316 | 72.13 327 | 44.90 352 | 50.03 348 | 81.05 343 | 89.42 348 | 38.45 344 | 48.51 346 | 99.90 17 | 54.09 338 | 78.70 346 | 91.84 211 | 18.26 347 | 87.64 338 |
|
canonicalmvs | | | 97.09 102 | 96.32 113 | 99.39 43 | 98.93 119 | 98.95 21 | 99.72 142 | 97.35 246 | 94.45 95 | 97.88 120 | 99.42 112 | 86.71 194 | 99.52 137 | 98.48 82 | 93.97 203 | 99.72 108 |
|
anonymousdsp | | | 91.79 231 | 90.92 229 | 94.41 242 | 90.76 323 | 92.93 214 | 98.93 240 | 97.17 259 | 89.08 239 | 87.46 274 | 95.30 275 | 78.43 263 | 96.92 271 | 92.38 203 | 88.73 229 | 93.39 301 |
|
alignmvs | | | 97.81 77 | 97.33 84 | 99.25 49 | 98.77 131 | 98.66 46 | 99.99 4 | 98.44 104 | 94.40 99 | 98.41 102 | 99.47 108 | 93.65 99 | 99.42 146 | 98.57 79 | 94.26 199 | 99.67 114 |
|
nrg030 | | | 93.51 194 | 92.53 202 | 96.45 181 | 94.36 273 | 97.20 104 | 99.81 114 | 97.16 261 | 91.60 197 | 89.86 226 | 97.46 201 | 86.37 198 | 97.68 229 | 95.88 143 | 80.31 298 | 94.46 227 |
|
v144192 | | | 90.79 247 | 89.52 254 | 94.59 229 | 93.11 296 | 92.77 215 | 99.56 168 | 96.99 275 | 86.38 283 | 89.82 229 | 94.95 290 | 80.50 246 | 97.10 259 | 83.98 290 | 80.41 296 | 93.90 280 |
|
FIs | | | 94.10 181 | 93.43 183 | 96.11 190 | 94.70 269 | 96.82 117 | 99.58 164 | 98.93 39 | 92.54 172 | 89.34 240 | 97.31 206 | 87.62 185 | 97.10 259 | 94.22 175 | 86.58 250 | 94.40 234 |
|
v1921920 | | | 90.46 254 | 89.12 261 | 94.50 235 | 92.96 300 | 92.46 226 | 99.49 179 | 96.98 277 | 86.10 286 | 89.61 235 | 95.30 275 | 78.55 261 | 97.03 266 | 82.17 301 | 80.89 294 | 94.01 270 |
|
UA-Net | | | 96.54 122 | 95.96 127 | 98.27 124 | 98.23 152 | 95.71 156 | 98.00 291 | 98.45 103 | 93.72 131 | 98.41 102 | 99.27 123 | 88.71 178 | 99.66 132 | 91.19 217 | 97.69 143 | 99.44 153 |
|
v1192 | | | 90.62 252 | 89.25 259 | 94.72 225 | 93.13 293 | 93.07 210 | 99.50 177 | 97.02 271 | 86.33 284 | 89.56 236 | 95.01 285 | 79.22 254 | 97.09 261 | 82.34 300 | 81.16 288 | 94.01 270 |
|
FC-MVSNet-test | | | 93.81 186 | 93.15 192 | 95.80 199 | 94.30 275 | 96.20 138 | 99.42 187 | 98.89 41 | 92.33 178 | 89.03 249 | 97.27 208 | 87.39 188 | 96.83 276 | 93.20 194 | 86.48 251 | 94.36 236 |
|
v1144 | | | 91.09 240 | 89.83 247 | 94.87 219 | 93.25 292 | 93.69 200 | 99.62 160 | 96.98 277 | 86.83 279 | 89.64 234 | 94.99 288 | 80.94 238 | 97.05 262 | 85.08 284 | 81.16 288 | 93.87 283 |
|
sosnet-low-res | | | 0.00 326 | 0.00 328 | 0.00 337 | 0.00 356 | 0.00 357 | 0.00 348 | 0.00 358 | 0.00 352 | 0.00 353 | 0.00 353 | 0.00 358 | 0.00 354 | 0.00 351 | 0.00 351 | 0.00 350 |
|
HFP-MVS | | | 98.56 37 | 98.37 40 | 99.14 63 | 99.96 8 | 97.43 97 | 99.95 40 | 98.61 68 | 94.77 85 | 99.31 61 | 99.85 33 | 94.22 82 | 100.00 1 | 98.70 71 | 99.98 33 | 99.98 51 |
|
v148 | | | 90.70 248 | 89.63 250 | 93.92 258 | 92.97 299 | 90.97 256 | 99.75 131 | 96.89 287 | 87.51 266 | 88.27 263 | 95.01 285 | 81.67 229 | 97.04 264 | 87.40 264 | 77.17 318 | 93.75 290 |
|
sosnet | | | 0.00 326 | 0.00 328 | 0.00 337 | 0.00 356 | 0.00 357 | 0.00 348 | 0.00 358 | 0.00 352 | 0.00 353 | 0.00 353 | 0.00 358 | 0.00 354 | 0.00 351 | 0.00 351 | 0.00 350 |
|
uncertanet | | | 0.00 326 | 0.00 328 | 0.00 337 | 0.00 356 | 0.00 357 | 0.00 348 | 0.00 358 | 0.00 352 | 0.00 353 | 0.00 353 | 0.00 358 | 0.00 354 | 0.00 351 | 0.00 351 | 0.00 350 |
|
AllTest | | | 92.48 214 | 91.64 216 | 95.00 215 | 99.01 111 | 88.43 292 | 98.94 239 | 96.82 293 | 86.50 281 | 88.71 252 | 98.47 183 | 74.73 286 | 99.88 85 | 85.39 281 | 96.18 171 | 96.71 214 |
|
TestCases | | | | | 95.00 215 | 99.01 111 | 88.43 292 | | 96.82 293 | 86.50 281 | 88.71 252 | 98.47 183 | 74.73 286 | 99.88 85 | 85.39 281 | 96.18 171 | 96.71 214 |
|
v7n | | | 89.65 271 | 88.29 276 | 93.72 263 | 92.22 308 | 90.56 265 | 99.07 224 | 97.10 264 | 85.42 298 | 86.73 281 | 94.72 293 | 80.06 249 | 97.13 256 | 81.14 306 | 78.12 310 | 93.49 298 |
|
region2R | | | 98.54 39 | 98.37 40 | 99.05 73 | 99.96 8 | 97.18 105 | 99.96 23 | 98.55 80 | 94.87 83 | 99.45 50 | 99.85 33 | 94.07 88 | 100.00 1 | 98.67 73 | 100.00 1 | 99.98 51 |
|
testing_2 | | | 85.10 296 | 81.72 303 | 95.22 209 | 82.25 340 | 94.16 190 | 97.54 298 | 97.01 274 | 88.15 259 | 62.23 337 | 86.43 334 | 44.43 343 | 97.18 252 | 92.28 205 | 85.20 261 | 94.31 241 |
|
RRT_MVS | | | 95.23 154 | 94.77 157 | 96.61 178 | 98.28 147 | 98.32 62 | 99.81 114 | 97.41 241 | 92.59 168 | 91.28 210 | 97.76 197 | 95.02 53 | 97.23 250 | 93.65 189 | 87.14 247 | 94.28 244 |
|
PS-MVSNAJss | | | 93.64 193 | 93.31 189 | 94.61 228 | 92.11 309 | 92.19 231 | 99.12 216 | 97.38 244 | 92.51 174 | 88.45 256 | 96.99 219 | 91.20 144 | 97.29 247 | 94.36 169 | 87.71 242 | 94.36 236 |
|
PS-MVSNAJ | | | 98.44 47 | 98.20 49 | 99.16 59 | 98.80 129 | 98.92 23 | 99.54 172 | 98.17 166 | 97.34 16 | 99.85 6 | 99.85 33 | 91.20 144 | 99.89 79 | 99.41 39 | 99.67 91 | 98.69 196 |
|
jajsoiax | | | 91.92 224 | 91.18 226 | 94.15 247 | 91.35 318 | 90.95 257 | 99.00 233 | 97.42 239 | 92.61 166 | 87.38 275 | 97.08 213 | 72.46 295 | 97.36 239 | 94.53 167 | 88.77 228 | 94.13 262 |
|
mvs_tets | | | 91.81 226 | 91.08 227 | 94.00 255 | 91.63 316 | 90.58 264 | 98.67 261 | 97.43 237 | 92.43 176 | 87.37 276 | 97.05 216 | 71.76 297 | 97.32 243 | 94.75 160 | 88.68 230 | 94.11 263 |
|
#test# | | | 98.59 35 | 98.41 33 | 99.14 63 | 99.96 8 | 97.43 97 | 99.95 40 | 98.61 68 | 95.00 79 | 99.31 61 | 99.85 33 | 94.22 82 | 100.00 1 | 98.78 68 | 99.98 33 | 99.98 51 |
|
EI-MVSNet-UG-set | | | 98.14 63 | 97.99 60 | 98.60 100 | 99.80 67 | 96.27 131 | 99.36 197 | 98.50 97 | 95.21 76 | 98.30 108 | 99.75 75 | 93.29 108 | 99.73 123 | 98.37 85 | 99.30 111 | 99.81 95 |
|
EI-MVSNet-Vis-set | | | 98.27 57 | 98.11 54 | 98.75 91 | 99.83 61 | 96.59 124 | 99.40 188 | 98.51 93 | 95.29 73 | 98.51 98 | 99.76 70 | 93.60 101 | 99.71 124 | 98.53 81 | 99.52 102 | 99.95 75 |
|
Regformer-3 | | | 98.58 36 | 98.41 33 | 99.10 69 | 99.84 58 | 97.57 87 | 99.66 150 | 98.52 87 | 95.79 57 | 99.01 77 | 99.77 66 | 94.40 70 | 99.75 116 | 98.82 64 | 99.83 77 | 99.98 51 |
|
Regformer-4 | | | 98.56 37 | 98.39 37 | 99.08 71 | 99.84 58 | 97.52 90 | 99.66 150 | 98.52 87 | 95.76 60 | 99.01 77 | 99.77 66 | 94.33 78 | 99.75 116 | 98.80 67 | 99.83 77 | 99.98 51 |
|
Regformer-1 | | | 98.79 24 | 98.60 23 | 99.36 45 | 99.85 53 | 98.34 61 | 99.87 87 | 98.52 87 | 96.05 53 | 99.41 54 | 99.79 60 | 94.93 59 | 99.76 113 | 99.07 47 | 99.90 68 | 99.99 20 |
|
Regformer-2 | | | 98.78 25 | 98.59 24 | 99.36 45 | 99.85 53 | 98.32 62 | 99.87 87 | 98.52 87 | 96.04 54 | 99.41 54 | 99.79 60 | 94.92 60 | 99.76 113 | 99.05 48 | 99.90 68 | 99.98 51 |
|
HPM-MVS++ | | | 99.07 9 | 98.88 13 | 99.63 12 | 99.90 41 | 99.02 19 | 99.95 40 | 98.56 76 | 97.56 13 | 99.44 51 | 99.85 33 | 95.38 45 | 100.00 1 | 99.31 41 | 99.99 20 | 99.87 90 |
|
test_prior4 | | | | | | | 98.05 70 | 99.94 55 | | | | | | | | | |
|
XVS | | | 98.70 28 | 98.55 25 | 99.15 61 | 99.94 14 | 97.50 93 | 99.94 55 | 98.42 123 | 96.22 49 | 99.41 54 | 99.78 64 | 94.34 75 | 99.96 53 | 98.92 59 | 99.95 50 | 99.99 20 |
|
v1240 | | | 90.20 262 | 88.79 268 | 94.44 239 | 93.05 298 | 92.27 230 | 99.38 193 | 96.92 285 | 85.89 288 | 89.36 239 | 94.87 292 | 77.89 264 | 97.03 266 | 80.66 308 | 81.08 290 | 94.01 270 |
|
test_prior3 | | | 98.99 13 | 98.84 14 | 99.43 35 | 99.94 14 | 98.49 56 | 99.95 40 | 98.65 59 | 95.78 58 | 99.73 26 | 99.76 70 | 96.00 29 | 99.80 106 | 99.78 20 | 100.00 1 | 99.99 20 |
|
pm-mvs1 | | | 89.36 275 | 87.81 281 | 94.01 254 | 93.40 291 | 91.93 237 | 98.62 264 | 96.48 306 | 86.25 285 | 83.86 304 | 96.14 243 | 73.68 292 | 97.04 264 | 86.16 277 | 75.73 325 | 93.04 309 |
|
test_prior2 | | | | | | | | 99.95 40 | | 95.78 58 | 99.73 26 | 99.76 70 | 96.00 29 | | 99.78 20 | 100.00 1 | |
|
X-MVStestdata | | | 93.83 184 | 92.06 210 | 99.15 61 | 99.94 14 | 97.50 93 | 99.94 55 | 98.42 123 | 96.22 49 | 99.41 54 | 41.37 351 | 94.34 75 | 99.96 53 | 98.92 59 | 99.95 50 | 99.99 20 |
|
test_prior | | | | | 99.43 35 | 99.94 14 | 98.49 56 | | 98.65 59 | | | | | 99.80 106 | | | 99.99 20 |
|
旧先验2 | | | | | | | | 99.46 184 | | 94.21 107 | 99.85 6 | | | 99.95 60 | 96.96 129 | | |
|
新几何2 | | | | | | | | 99.40 188 | | | | | | | | | |
|
新几何1 | | | | | 99.42 38 | 99.75 72 | 98.27 64 | | 98.63 65 | 92.69 161 | 99.55 42 | 99.82 52 | 94.40 70 | 100.00 1 | 91.21 216 | 99.94 56 | 99.99 20 |
|
旧先验1 | | | | | | 99.76 70 | 97.52 90 | | 98.64 62 | | | 99.85 33 | 95.63 39 | | | 99.94 56 | 99.99 20 |
|
无先验 | | | | | | | | 99.49 179 | 98.71 52 | 93.46 137 | | | | 100.00 1 | 94.36 169 | | 99.99 20 |
|
原ACMM2 | | | | | | | | 99.90 73 | | | | | | | | | |
|
原ACMM1 | | | | | 98.96 82 | 99.73 77 | 96.99 112 | | 98.51 93 | 94.06 114 | 99.62 37 | 99.85 33 | 94.97 58 | 99.96 53 | 95.11 149 | 99.95 50 | 99.92 84 |
|
test222 | | | | | | 99.55 90 | 97.41 100 | 99.34 198 | 98.55 80 | 91.86 190 | 99.27 65 | 99.83 49 | 93.84 95 | | | 99.95 50 | 99.99 20 |
|
testdata2 | | | | | | | | | | | | | | 99.99 36 | 90.54 231 | | |
|
segment_acmp | | | | | | | | | | | | | 96.68 22 | | | | |
|
testdata | | | | | 98.42 117 | 99.47 96 | 95.33 165 | | 98.56 76 | 93.78 128 | 99.79 21 | 99.85 33 | 93.64 100 | 99.94 68 | 94.97 151 | 99.94 56 | 100.00 1 |
|
testdata1 | | | | | | | | 99.28 206 | | 96.35 48 | | | | | | | |
|
v8 | | | 90.54 253 | 89.17 260 | 94.66 226 | 93.43 289 | 93.40 207 | 99.20 211 | 96.94 283 | 85.76 290 | 87.56 271 | 94.51 300 | 81.96 228 | 97.19 251 | 84.94 285 | 78.25 308 | 93.38 302 |
|
1314 | | | 96.84 109 | 95.96 127 | 99.48 33 | 96.74 227 | 98.52 54 | 98.31 277 | 98.86 44 | 95.82 56 | 89.91 224 | 98.98 145 | 87.49 186 | 99.96 53 | 97.80 107 | 99.73 87 | 99.96 67 |
|
1121 | | | 98.03 67 | 97.57 76 | 99.40 41 | 99.74 73 | 98.21 65 | 98.31 277 | 98.62 66 | 92.78 156 | 99.53 44 | 99.83 49 | 95.08 50 | 100.00 1 | 94.36 169 | 99.92 66 | 99.99 20 |
|
LFMVS | | | 94.75 165 | 93.56 181 | 98.30 123 | 99.03 110 | 95.70 157 | 98.74 254 | 97.98 184 | 87.81 265 | 98.47 100 | 99.39 116 | 67.43 314 | 99.53 136 | 98.01 99 | 95.20 192 | 99.67 114 |
|
VDD-MVS | | | 93.77 188 | 92.94 193 | 96.27 187 | 98.55 136 | 90.22 271 | 98.77 253 | 97.79 202 | 90.85 217 | 96.82 141 | 99.42 112 | 61.18 331 | 99.77 111 | 98.95 56 | 94.13 200 | 98.82 192 |
|
VDDNet | | | 93.12 200 | 91.91 213 | 96.76 172 | 96.67 230 | 92.65 223 | 98.69 259 | 98.21 160 | 82.81 311 | 97.75 123 | 99.28 120 | 61.57 329 | 99.48 144 | 98.09 96 | 94.09 201 | 98.15 201 |
|
v10 | | | 90.25 261 | 88.82 267 | 94.57 231 | 93.53 287 | 93.43 205 | 99.08 220 | 96.87 289 | 85.00 299 | 87.34 277 | 94.51 300 | 80.93 239 | 97.02 268 | 82.85 297 | 79.23 303 | 93.26 304 |
|
VPNet | | | 91.81 226 | 90.46 234 | 95.85 198 | 94.74 268 | 95.54 160 | 98.98 234 | 98.59 71 | 92.14 182 | 90.77 215 | 97.44 202 | 68.73 309 | 97.54 234 | 94.89 155 | 77.89 311 | 94.46 227 |
|
MVS | | | 96.60 121 | 95.56 139 | 99.72 9 | 96.85 220 | 99.22 15 | 98.31 277 | 98.94 36 | 91.57 198 | 90.90 213 | 99.61 98 | 86.66 195 | 99.96 53 | 97.36 118 | 99.88 72 | 99.99 20 |
|
v2v482 | | | 91.30 235 | 90.07 246 | 95.01 214 | 93.13 293 | 93.79 197 | 99.77 125 | 97.02 271 | 88.05 261 | 89.25 242 | 95.37 272 | 80.73 241 | 97.15 254 | 87.28 266 | 80.04 301 | 94.09 264 |
|
V42 | | | 91.28 237 | 90.12 245 | 94.74 223 | 93.42 290 | 93.46 204 | 99.68 147 | 97.02 271 | 87.36 269 | 89.85 228 | 95.05 283 | 81.31 235 | 97.34 241 | 87.34 265 | 80.07 300 | 93.40 300 |
|
SD-MVS | | | 98.92 16 | 98.70 17 | 99.56 21 | 99.70 81 | 98.73 41 | 99.94 55 | 98.34 144 | 96.38 44 | 99.81 12 | 99.76 70 | 94.59 66 | 99.98 42 | 99.84 13 | 99.96 47 | 99.97 62 |
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 | | | 93.83 184 | 92.84 194 | 96.80 170 | 95.73 249 | 93.57 201 | 99.88 84 | 97.24 254 | 92.57 171 | 92.92 197 | 96.66 229 | 78.73 259 | 97.67 230 | 87.75 260 | 94.06 202 | 99.17 175 |
|
MSLP-MVS++ | | | 99.13 7 | 99.01 9 | 99.49 31 | 99.94 14 | 98.46 58 | 99.98 8 | 98.86 44 | 97.10 25 | 99.80 16 | 99.94 4 | 95.92 33 | 100.00 1 | 99.51 33 | 100.00 1 | 100.00 1 |
|
APDe-MVS | | | 99.06 10 | 98.91 12 | 99.51 28 | 99.94 14 | 98.76 40 | 99.91 69 | 98.39 132 | 97.20 24 | 99.46 49 | 99.85 33 | 95.53 42 | 99.79 108 | 99.86 12 | 100.00 1 | 99.99 20 |
|
APD-MVS_3200maxsize | | | 98.25 60 | 98.08 55 | 98.78 89 | 99.81 66 | 96.60 123 | 99.82 112 | 98.30 149 | 93.95 120 | 99.37 58 | 99.77 66 | 92.84 117 | 99.76 113 | 98.95 56 | 99.92 66 | 99.97 62 |
|
ADS-MVSNet2 | | | 93.80 187 | 93.88 173 | 93.55 268 | 97.87 171 | 85.94 307 | 94.24 323 | 96.84 290 | 90.07 229 | 96.43 151 | 94.48 302 | 90.29 159 | 95.37 315 | 87.44 262 | 97.23 154 | 99.36 160 |
|
EI-MVSNet | | | 93.73 190 | 93.40 187 | 94.74 223 | 96.80 223 | 92.69 220 | 99.06 225 | 97.67 207 | 88.96 245 | 91.39 208 | 99.02 138 | 88.75 177 | 97.30 244 | 91.07 219 | 87.85 240 | 94.22 248 |
|
Regformer | | | 0.00 326 | 0.00 328 | 0.00 337 | 0.00 356 | 0.00 357 | 0.00 348 | 0.00 358 | 0.00 352 | 0.00 353 | 0.00 353 | 0.00 358 | 0.00 354 | 0.00 351 | 0.00 351 | 0.00 350 |
|
CVMVSNet | | | 94.68 168 | 94.94 153 | 93.89 260 | 96.80 223 | 86.92 303 | 99.06 225 | 98.98 34 | 94.45 95 | 94.23 184 | 99.02 138 | 85.60 203 | 95.31 317 | 90.91 225 | 95.39 189 | 99.43 154 |
|
pmmvs4 | | | 92.10 222 | 91.07 228 | 95.18 210 | 92.82 302 | 94.96 175 | 99.48 181 | 96.83 291 | 87.45 268 | 88.66 255 | 96.56 234 | 83.78 218 | 96.83 276 | 89.29 244 | 84.77 264 | 93.75 290 |
|
EU-MVSNet | | | 90.14 265 | 90.34 238 | 89.54 309 | 92.55 305 | 81.06 330 | 98.69 259 | 98.04 180 | 91.41 206 | 86.59 284 | 96.84 226 | 80.83 240 | 93.31 334 | 86.20 276 | 81.91 282 | 94.26 245 |
|
VNet | | | 97.21 98 | 96.57 107 | 99.13 68 | 98.97 115 | 97.82 80 | 99.03 231 | 99.21 27 | 94.31 103 | 99.18 71 | 98.88 156 | 86.26 199 | 99.89 79 | 98.93 58 | 94.32 198 | 99.69 111 |
|
test-LLR | | | 96.47 124 | 96.04 118 | 97.78 140 | 97.02 213 | 95.44 161 | 99.96 23 | 98.21 160 | 94.07 112 | 95.55 166 | 96.38 236 | 93.90 93 | 98.27 206 | 90.42 233 | 98.83 120 | 99.64 120 |
|
TESTMET0.1,1 | | | 96.74 115 | 96.26 114 | 98.16 127 | 97.36 199 | 96.48 125 | 99.96 23 | 98.29 150 | 91.93 188 | 95.77 164 | 98.07 191 | 95.54 40 | 98.29 203 | 90.55 230 | 98.89 118 | 99.70 109 |
|
test-mter | | | 96.39 128 | 95.93 129 | 97.78 140 | 97.02 213 | 95.44 161 | 99.96 23 | 98.21 160 | 91.81 193 | 95.55 166 | 96.38 236 | 95.17 47 | 98.27 206 | 90.42 233 | 98.83 120 | 99.64 120 |
|
VPA-MVSNet | | | 92.70 209 | 91.55 220 | 96.16 189 | 95.09 262 | 96.20 138 | 98.88 244 | 99.00 33 | 91.02 214 | 91.82 205 | 95.29 278 | 76.05 278 | 97.96 220 | 95.62 146 | 81.19 287 | 94.30 242 |
|
ACMMPR | | | 98.50 42 | 98.32 44 | 99.05 73 | 99.96 8 | 97.18 105 | 99.95 40 | 98.60 70 | 94.77 85 | 99.31 61 | 99.84 46 | 93.73 97 | 100.00 1 | 98.70 71 | 99.98 33 | 99.98 51 |
|
testgi | | | 89.01 279 | 88.04 279 | 91.90 291 | 93.49 288 | 84.89 314 | 99.73 139 | 95.66 319 | 93.89 125 | 85.14 298 | 98.17 188 | 59.68 332 | 94.66 325 | 77.73 317 | 88.88 225 | 96.16 219 |
|
test20.03 | | | 84.72 299 | 83.99 292 | 86.91 317 | 88.19 333 | 80.62 332 | 98.88 244 | 95.94 314 | 88.36 258 | 78.87 318 | 94.62 298 | 68.75 308 | 89.11 340 | 66.52 335 | 75.82 323 | 91.00 324 |
|
thres600view7 | | | 96.69 118 | 95.87 133 | 99.14 63 | 98.90 124 | 98.78 35 | 99.74 134 | 99.71 5 | 92.59 168 | 95.84 161 | 98.86 160 | 89.25 170 | 99.50 140 | 93.44 192 | 94.50 197 | 99.16 176 |
|
ADS-MVSNet | | | 94.79 162 | 94.02 169 | 97.11 165 | 97.87 171 | 93.79 197 | 94.24 323 | 98.16 169 | 90.07 229 | 96.43 151 | 94.48 302 | 90.29 159 | 98.19 210 | 87.44 262 | 97.23 154 | 99.36 160 |
|
MP-MVS | | | 98.23 61 | 97.97 61 | 99.03 75 | 99.94 14 | 97.17 108 | 99.95 40 | 98.39 132 | 94.70 88 | 98.26 111 | 99.81 56 | 91.84 138 | 100.00 1 | 98.85 63 | 99.97 44 | 99.93 78 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
testmvs | | | 40.60 320 | 44.45 322 | 29.05 335 | 19.49 355 | 14.11 356 | 99.68 147 | 18.47 355 | 20.74 349 | 64.59 336 | 98.48 182 | 10.95 354 | 17.09 353 | 56.66 342 | 11.01 348 | 55.94 346 |
|
thres400 | | | 96.78 112 | 95.99 120 | 99.16 59 | 98.94 117 | 98.82 31 | 99.78 122 | 99.71 5 | 92.86 150 | 96.02 158 | 98.87 158 | 89.33 168 | 99.50 140 | 93.84 179 | 94.57 194 | 99.16 176 |
|
test123 | | | 37.68 321 | 39.14 323 | 33.31 334 | 19.94 354 | 24.83 355 | 98.36 276 | 9.75 356 | 15.53 350 | 51.31 345 | 87.14 331 | 19.62 352 | 17.74 352 | 47.10 344 | 3.47 350 | 57.36 345 |
|
thres200 | | | 96.96 104 | 96.21 115 | 99.22 50 | 98.97 115 | 98.84 30 | 99.85 101 | 99.71 5 | 93.17 145 | 96.26 155 | 98.88 156 | 89.87 163 | 99.51 138 | 94.26 173 | 94.91 193 | 99.31 166 |
|
test0.0.03 1 | | | 93.86 183 | 93.61 176 | 94.64 227 | 95.02 265 | 92.18 232 | 99.93 61 | 98.58 72 | 94.07 112 | 87.96 266 | 98.50 178 | 93.90 93 | 94.96 321 | 81.33 305 | 93.17 209 | 96.78 213 |
|
pmmvs3 | | | 80.27 307 | 77.77 310 | 87.76 316 | 80.32 341 | 82.43 322 | 98.23 282 | 91.97 341 | 72.74 336 | 78.75 319 | 87.97 329 | 57.30 336 | 90.99 337 | 70.31 329 | 62.37 339 | 89.87 331 |
|
EMVS | | | 51.44 319 | 51.22 320 | 52.11 333 | 70.71 345 | 44.97 351 | 94.04 325 | 75.66 353 | 35.34 348 | 42.40 348 | 61.56 349 | 28.93 347 | 65.87 350 | 27.64 349 | 24.73 345 | 45.49 347 |
|
E-PMN | | | 52.30 317 | 52.18 318 | 52.67 332 | 71.51 344 | 45.40 349 | 93.62 329 | 76.60 352 | 36.01 346 | 43.50 347 | 64.13 346 | 27.11 348 | 67.31 349 | 31.06 348 | 26.06 344 | 45.30 348 |
|
PGM-MVS | | | 98.34 53 | 98.13 53 | 98.99 79 | 99.92 35 | 97.00 111 | 99.75 131 | 99.50 16 | 93.90 123 | 99.37 58 | 99.76 70 | 93.24 110 | 100.00 1 | 97.75 112 | 99.96 47 | 99.98 51 |
|
LCM-MVSNet-Re | | | 92.31 218 | 92.60 199 | 91.43 294 | 97.53 192 | 79.27 335 | 99.02 232 | 91.83 342 | 92.07 184 | 80.31 314 | 94.38 305 | 83.50 220 | 95.48 313 | 97.22 122 | 97.58 146 | 99.54 140 |
|
LCM-MVSNet | | | 67.77 311 | 64.73 314 | 76.87 324 | 62.95 349 | 56.25 346 | 89.37 340 | 93.74 339 | 44.53 343 | 61.99 338 | 80.74 338 | 20.42 351 | 86.53 342 | 69.37 331 | 59.50 341 | 87.84 337 |
|
MCST-MVS | | | 99.32 3 | 99.14 4 | 99.86 3 | 99.97 3 | 99.59 3 | 99.97 16 | 98.64 62 | 98.47 2 | 99.13 72 | 99.92 11 | 96.38 26 | 100.00 1 | 99.74 24 | 100.00 1 | 100.00 1 |
|
mvs_anonymous | | | 95.65 148 | 95.03 152 | 97.53 150 | 98.19 154 | 95.74 154 | 99.33 199 | 97.49 232 | 90.87 216 | 90.47 217 | 97.10 212 | 88.23 181 | 97.16 253 | 95.92 142 | 97.66 145 | 99.68 112 |
|
MVS_Test | | | 96.46 125 | 95.74 135 | 98.61 99 | 98.18 155 | 97.23 103 | 99.31 202 | 97.15 262 | 91.07 212 | 98.84 83 | 97.05 216 | 88.17 182 | 98.97 156 | 94.39 168 | 97.50 147 | 99.61 124 |
|
MDA-MVSNet-bldmvs | | | 84.09 301 | 81.52 305 | 91.81 292 | 91.32 319 | 88.00 299 | 98.67 261 | 95.92 315 | 80.22 320 | 55.60 343 | 93.32 313 | 68.29 312 | 93.60 332 | 73.76 326 | 76.61 322 | 93.82 287 |
|
CDPH-MVS | | | 98.65 31 | 98.36 42 | 99.49 31 | 99.94 14 | 98.73 41 | 99.87 87 | 98.33 145 | 93.97 118 | 99.76 24 | 99.87 26 | 94.99 57 | 99.75 116 | 98.55 80 | 100.00 1 | 99.98 51 |
|
test12 | | | | | 99.43 35 | 99.74 73 | 98.56 52 | | 98.40 129 | | 99.65 33 | | 94.76 62 | 99.75 116 | | 99.98 33 | 99.99 20 |
|
casdiffmvs | | | 96.42 127 | 95.97 125 | 97.77 142 | 97.30 204 | 94.98 174 | 99.84 105 | 97.09 265 | 93.75 130 | 96.58 146 | 99.26 126 | 85.07 209 | 98.78 164 | 97.77 110 | 97.04 159 | 99.54 140 |
|
diffmvs | | | 97.00 103 | 96.64 104 | 98.09 131 | 97.64 188 | 96.17 140 | 99.81 114 | 97.19 256 | 94.67 90 | 98.95 80 | 99.28 120 | 86.43 197 | 98.76 167 | 98.37 85 | 97.42 150 | 99.33 164 |
|
baseline2 | | | 96.71 117 | 96.49 109 | 97.37 158 | 95.63 256 | 95.96 147 | 99.74 134 | 98.88 42 | 92.94 149 | 91.61 206 | 98.97 147 | 97.72 5 | 98.62 176 | 94.83 156 | 98.08 139 | 97.53 211 |
|
baseline1 | | | 95.78 143 | 94.86 154 | 98.54 107 | 98.47 141 | 98.07 69 | 99.06 225 | 97.99 182 | 92.68 162 | 94.13 185 | 98.62 172 | 93.28 109 | 98.69 173 | 93.79 184 | 85.76 254 | 98.84 191 |
|
YYNet1 | | | 85.50 294 | 83.33 297 | 92.00 289 | 90.89 322 | 88.38 295 | 99.22 210 | 96.55 303 | 79.60 322 | 57.26 341 | 92.72 317 | 79.09 257 | 93.78 330 | 77.25 319 | 77.37 317 | 93.84 285 |
|
PMMVS2 | | | 67.15 312 | 64.15 315 | 76.14 325 | 70.56 346 | 62.07 343 | 93.89 326 | 87.52 349 | 58.09 340 | 60.02 339 | 78.32 339 | 22.38 350 | 84.54 343 | 59.56 341 | 47.03 342 | 81.80 339 |
|
MDA-MVSNet_test_wron | | | 85.51 293 | 83.32 298 | 92.10 288 | 90.96 321 | 88.58 291 | 99.20 211 | 96.52 304 | 79.70 321 | 57.12 342 | 92.69 319 | 79.11 256 | 93.86 329 | 77.10 320 | 77.46 316 | 93.86 284 |
|
tpmvs | | | 94.28 180 | 93.57 180 | 96.40 183 | 98.55 136 | 91.50 252 | 95.70 322 | 98.55 80 | 87.47 267 | 92.15 203 | 94.26 306 | 91.42 140 | 98.95 157 | 88.15 256 | 95.85 179 | 98.76 195 |
|
PM-MVS | | | 80.47 306 | 78.88 309 | 85.26 319 | 83.79 338 | 72.22 338 | 95.89 320 | 91.08 343 | 85.71 293 | 76.56 326 | 88.30 328 | 36.64 344 | 93.90 328 | 82.39 299 | 69.57 331 | 89.66 333 |
|
HQP_MVS | | | 94.49 175 | 94.36 163 | 94.87 219 | 95.71 252 | 91.74 243 | 99.84 105 | 97.87 195 | 96.38 44 | 93.01 195 | 98.59 173 | 80.47 247 | 98.37 198 | 97.79 108 | 89.55 218 | 94.52 224 |
|
plane_prior7 | | | | | | 95.71 252 | 91.59 251 | | | | | | | | | | |
|
plane_prior6 | | | | | | 95.76 247 | 91.72 246 | | | | | | 80.47 247 | | | | |
|
plane_prior5 | | | | | | | | | 97.87 195 | | | | | 98.37 198 | 97.79 108 | 89.55 218 | 94.52 224 |
|
plane_prior4 | | | | | | | | | | | | 98.59 173 | | | | | |
|
plane_prior3 | | | | | | | 91.64 249 | | | 96.63 38 | 93.01 195 | | | | | | |
|
plane_prior2 | | | | | | | | 99.84 105 | | 96.38 44 | | | | | | | |
|
plane_prior1 | | | | | | 95.73 249 | | | | | | | | | | | |
|
plane_prior | | | | | | | 91.74 243 | 99.86 98 | | 96.76 34 | | | | | | 89.59 217 | |
|
PS-CasMVS | | | 90.63 251 | 89.51 255 | 93.99 256 | 93.83 282 | 91.70 247 | 98.98 234 | 98.52 87 | 88.48 256 | 86.15 292 | 96.53 235 | 75.46 280 | 96.31 295 | 88.83 248 | 78.86 306 | 93.95 276 |
|
UniMVSNet_NR-MVSNet | | | 92.95 204 | 92.11 208 | 95.49 201 | 94.61 271 | 95.28 167 | 99.83 111 | 99.08 30 | 91.49 200 | 89.21 244 | 96.86 223 | 87.14 190 | 96.73 280 | 93.20 194 | 77.52 314 | 94.46 227 |
|
PEN-MVS | | | 90.19 263 | 89.06 263 | 93.57 267 | 93.06 297 | 90.90 258 | 99.06 225 | 98.47 99 | 88.11 260 | 85.91 294 | 96.30 239 | 76.67 270 | 95.94 310 | 87.07 268 | 76.91 320 | 93.89 281 |
|
TransMVSNet (Re) | | | 87.25 286 | 85.28 290 | 93.16 274 | 93.56 286 | 91.03 255 | 98.54 267 | 94.05 337 | 83.69 308 | 81.09 312 | 96.16 242 | 75.32 281 | 96.40 291 | 76.69 322 | 68.41 333 | 92.06 317 |
|
DTE-MVSNet | | | 89.40 273 | 88.24 277 | 92.88 280 | 92.66 304 | 89.95 275 | 99.10 217 | 98.22 159 | 87.29 270 | 85.12 299 | 96.22 241 | 76.27 275 | 95.30 318 | 83.56 294 | 75.74 324 | 93.41 299 |
|
DU-MVS | | | 92.46 215 | 91.45 223 | 95.49 201 | 94.05 278 | 95.28 167 | 99.81 114 | 98.74 51 | 92.25 180 | 89.21 244 | 96.64 231 | 81.66 230 | 96.73 280 | 93.20 194 | 77.52 314 | 94.46 227 |
|
UniMVSNet (Re) | | | 93.07 202 | 92.13 207 | 95.88 196 | 94.84 266 | 96.24 137 | 99.88 84 | 98.98 34 | 92.49 175 | 89.25 242 | 95.40 268 | 87.09 191 | 97.14 255 | 93.13 198 | 78.16 309 | 94.26 245 |
|
CP-MVSNet | | | 91.23 238 | 90.22 241 | 94.26 244 | 93.96 280 | 92.39 228 | 99.09 218 | 98.57 74 | 88.95 246 | 86.42 288 | 96.57 233 | 79.19 255 | 96.37 292 | 90.29 236 | 78.95 304 | 94.02 268 |
|
WR-MVS_H | | | 91.30 235 | 90.35 237 | 94.15 247 | 94.17 277 | 92.62 224 | 99.17 214 | 98.94 36 | 88.87 248 | 86.48 287 | 94.46 304 | 84.36 214 | 96.61 285 | 88.19 255 | 78.51 307 | 93.21 306 |
|
WR-MVS | | | 92.31 218 | 91.25 225 | 95.48 204 | 94.45 272 | 95.29 166 | 99.60 162 | 98.68 55 | 90.10 228 | 88.07 265 | 96.89 221 | 80.68 242 | 96.80 278 | 93.14 197 | 79.67 302 | 94.36 236 |
|
NR-MVSNet | | | 91.56 234 | 90.22 241 | 95.60 200 | 94.05 278 | 95.76 153 | 98.25 280 | 98.70 53 | 91.16 210 | 80.78 313 | 96.64 231 | 83.23 223 | 96.57 286 | 91.41 214 | 77.73 313 | 94.46 227 |
|
Baseline_NR-MVSNet | | | 90.33 258 | 89.51 255 | 92.81 281 | 92.84 301 | 89.95 275 | 99.77 125 | 93.94 338 | 84.69 302 | 89.04 248 | 95.66 255 | 81.66 230 | 96.52 287 | 90.99 222 | 76.98 319 | 91.97 319 |
|
TranMVSNet+NR-MVSNet | | | 91.68 233 | 90.61 233 | 94.87 219 | 93.69 285 | 93.98 194 | 99.69 145 | 98.65 59 | 91.03 213 | 88.44 257 | 96.83 227 | 80.05 250 | 96.18 300 | 90.26 237 | 76.89 321 | 94.45 232 |
|
TSAR-MVS + GP. | | | 98.60 33 | 98.51 28 | 98.86 87 | 99.73 77 | 96.63 121 | 99.97 16 | 97.92 191 | 98.07 5 | 98.76 87 | 99.55 102 | 95.00 56 | 99.94 68 | 99.91 11 | 97.68 144 | 99.99 20 |
|
abl_6 | | | 97.67 83 | 97.34 83 | 98.66 95 | 99.68 82 | 96.11 144 | 99.68 147 | 98.14 172 | 93.80 127 | 99.27 65 | 99.70 84 | 88.65 179 | 99.98 42 | 97.46 116 | 99.72 88 | 99.89 87 |
|
n2 | | | | | | | | | 0.00 358 | | | | | | | | |
|
nn | | | | | | | | | 0.00 358 | | | | | | | | |
|
mPP-MVS | | | 98.39 52 | 98.20 49 | 98.97 81 | 99.97 3 | 96.92 115 | 99.95 40 | 98.38 136 | 95.04 78 | 98.61 95 | 99.80 57 | 93.39 103 | 100.00 1 | 98.64 77 | 100.00 1 | 99.98 51 |
|
door-mid | | | | | | | | | 89.69 346 | | | | | | | | |
|
XVG-OURS-SEG-HR | | | 94.79 162 | 94.70 159 | 95.08 212 | 98.05 161 | 89.19 282 | 99.08 220 | 97.54 223 | 93.66 132 | 94.87 175 | 99.58 100 | 78.78 258 | 99.79 108 | 97.31 119 | 93.40 207 | 96.25 216 |
|
DWT-MVSNet_test | | | 97.31 93 | 97.19 87 | 97.66 146 | 98.24 151 | 94.67 183 | 98.86 248 | 98.20 164 | 93.60 134 | 98.09 114 | 98.89 154 | 97.51 7 | 98.78 164 | 94.04 176 | 97.28 153 | 99.55 136 |
|
MVSFormer | | | 96.94 105 | 96.60 105 | 97.95 135 | 97.28 205 | 97.70 84 | 99.55 170 | 97.27 252 | 91.17 208 | 99.43 52 | 99.54 104 | 90.92 152 | 96.89 272 | 94.67 164 | 99.62 94 | 99.25 171 |
|
jason | | | 97.24 96 | 96.86 97 | 98.38 120 | 95.73 249 | 97.32 101 | 99.97 16 | 97.40 243 | 95.34 72 | 98.60 96 | 99.54 104 | 87.70 184 | 98.56 178 | 97.94 104 | 99.47 105 | 99.25 171 |
jason: jason. |
lupinMVS | | | 97.85 73 | 97.60 74 | 98.62 98 | 97.28 205 | 97.70 84 | 99.99 4 | 97.55 221 | 95.50 69 | 99.43 52 | 99.67 91 | 90.92 152 | 98.71 171 | 98.40 84 | 99.62 94 | 99.45 151 |
|
test_djsdf | | | 92.83 206 | 92.29 206 | 94.47 237 | 91.90 312 | 92.46 226 | 99.55 170 | 97.27 252 | 91.17 208 | 89.96 222 | 96.07 246 | 81.10 236 | 96.89 272 | 94.67 164 | 88.91 224 | 94.05 267 |
|
HPM-MVS_fast | | | 97.80 78 | 97.50 77 | 98.68 93 | 99.79 68 | 96.42 127 | 99.88 84 | 98.16 169 | 91.75 194 | 98.94 81 | 99.54 104 | 91.82 139 | 99.65 133 | 97.62 114 | 99.99 20 | 99.99 20 |
|
RRT_test8_iter05 | | | 94.58 171 | 94.11 167 | 95.98 193 | 97.88 169 | 96.11 144 | 99.89 81 | 97.45 234 | 91.66 196 | 88.28 262 | 96.71 228 | 96.53 24 | 97.40 237 | 94.73 162 | 83.85 273 | 94.45 232 |
|
K. test v3 | | | 88.05 284 | 87.24 284 | 90.47 302 | 91.82 314 | 82.23 324 | 98.96 237 | 97.42 239 | 89.05 240 | 76.93 324 | 95.60 257 | 68.49 310 | 95.42 314 | 85.87 280 | 81.01 292 | 93.75 290 |
|
lessismore_v0 | | | | | 90.53 300 | 90.58 324 | 80.90 331 | | 95.80 316 | | 77.01 323 | 95.84 248 | 66.15 318 | 96.95 269 | 83.03 296 | 75.05 326 | 93.74 293 |
|
SixPastTwentyTwo | | | 88.73 280 | 88.01 280 | 90.88 297 | 91.85 313 | 82.24 323 | 98.22 283 | 95.18 329 | 88.97 244 | 82.26 308 | 96.89 221 | 71.75 298 | 96.67 283 | 84.00 289 | 82.98 275 | 93.72 294 |
|
OurMVSNet-221017-0 | | | 89.81 269 | 89.48 257 | 90.83 299 | 91.64 315 | 81.21 328 | 98.17 285 | 95.38 324 | 91.48 201 | 85.65 296 | 97.31 206 | 72.66 294 | 97.29 247 | 88.15 256 | 84.83 263 | 93.97 275 |
|
HPM-MVS | | | 97.96 68 | 97.72 69 | 98.68 93 | 99.84 58 | 96.39 130 | 99.90 73 | 98.17 166 | 92.61 166 | 98.62 94 | 99.57 101 | 91.87 137 | 99.67 131 | 98.87 62 | 99.99 20 | 99.99 20 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
XVG-OURS | | | 94.82 161 | 94.74 158 | 95.06 213 | 98.00 163 | 89.19 282 | 99.08 220 | 97.55 221 | 94.10 110 | 94.71 176 | 99.62 97 | 80.51 245 | 99.74 120 | 96.04 140 | 93.06 211 | 96.25 216 |
|
XVG-ACMP-BASELINE | | | 91.22 239 | 90.75 230 | 92.63 283 | 93.73 284 | 85.61 308 | 98.52 269 | 97.44 236 | 92.77 157 | 89.90 225 | 96.85 224 | 66.64 316 | 98.39 193 | 92.29 204 | 88.61 231 | 93.89 281 |
|
LPG-MVS_test | | | 92.96 203 | 92.71 197 | 93.71 264 | 95.43 258 | 88.67 288 | 99.75 131 | 97.62 212 | 92.81 153 | 90.05 219 | 98.49 179 | 75.24 282 | 98.40 191 | 95.84 144 | 89.12 222 | 94.07 265 |
|
LGP-MVS_train | | | | | 93.71 264 | 95.43 258 | 88.67 288 | | 97.62 212 | 92.81 153 | 90.05 219 | 98.49 179 | 75.24 282 | 98.40 191 | 95.84 144 | 89.12 222 | 94.07 265 |
|
baseline | | | 96.43 126 | 95.98 122 | 97.76 143 | 97.34 200 | 95.17 172 | 99.51 176 | 97.17 259 | 93.92 122 | 96.90 139 | 99.28 120 | 85.37 207 | 98.64 175 | 97.50 115 | 96.86 164 | 99.46 149 |
|
test11 | | | | | | | | | 98.44 104 | | | | | | | | |
|
door | | | | | | | | | 90.31 344 | | | | | | | | |
|
EPNet_dtu | | | 95.71 146 | 95.39 142 | 96.66 176 | 98.92 121 | 93.41 206 | 99.57 166 | 98.90 40 | 96.19 51 | 97.52 126 | 98.56 177 | 92.65 122 | 97.36 239 | 77.89 316 | 98.33 130 | 99.20 174 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CHOSEN 1792x2688 | | | 96.81 110 | 96.53 108 | 97.64 147 | 98.91 123 | 93.07 210 | 99.65 153 | 99.80 3 | 95.64 65 | 95.39 169 | 98.86 160 | 84.35 215 | 99.90 75 | 96.98 128 | 99.16 114 | 99.95 75 |
|
EPNet | | | 98.49 43 | 98.40 35 | 98.77 90 | 99.62 85 | 96.80 118 | 99.90 73 | 99.51 15 | 97.60 12 | 99.20 68 | 99.36 119 | 93.71 98 | 99.91 74 | 97.99 101 | 98.71 123 | 99.61 124 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
HQP5-MVS | | | | | | | 91.85 239 | | | | | | | | | | |
|
HQP-NCC | | | | | | 95.78 243 | | 99.87 87 | | 96.82 30 | 93.37 191 | | | | | | |
|
ACMP_Plane | | | | | | 95.78 243 | | 99.87 87 | | 96.82 30 | 93.37 191 | | | | | | |
|
APD-MVS | | | 98.62 32 | 98.35 43 | 99.41 39 | 99.90 41 | 98.51 55 | 99.87 87 | 98.36 141 | 94.08 111 | 99.74 25 | 99.73 79 | 94.08 87 | 99.74 120 | 99.42 38 | 99.99 20 | 99.99 20 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
BP-MVS | | | | | | | | | | | | | | | 97.92 105 | | |
|
HQP4-MVS | | | | | | | | | | | 93.37 191 | | | 98.39 193 | | | 94.53 222 |
|
HQP3-MVS | | | | | | | | | 97.89 193 | | | | | | | 89.60 215 | |
|
HQP2-MVS | | | | | | | | | | | | | 80.65 243 | | | | |
|
CNVR-MVS | | | 99.40 1 | 99.26 1 | 99.84 4 | 99.98 2 | 99.51 4 | 99.98 8 | 98.69 54 | 98.20 3 | 99.93 1 | 99.98 2 | 96.82 19 | 100.00 1 | 99.75 22 | 100.00 1 | 99.99 20 |
|
NCCC | | | 99.37 2 | 99.25 2 | 99.71 10 | 99.96 8 | 99.15 16 | 99.97 16 | 98.62 66 | 98.02 6 | 99.90 2 | 99.95 3 | 97.33 13 | 100.00 1 | 99.54 32 | 100.00 1 | 100.00 1 |
|
114514_t | | | 97.41 91 | 96.83 98 | 99.14 63 | 99.51 94 | 97.83 79 | 99.89 81 | 98.27 154 | 88.48 256 | 99.06 74 | 99.66 93 | 90.30 158 | 99.64 134 | 96.32 137 | 99.97 44 | 99.96 67 |
|
CP-MVS | | | 98.45 46 | 98.32 44 | 98.87 86 | 99.96 8 | 96.62 122 | 99.97 16 | 98.39 132 | 94.43 97 | 98.90 82 | 99.87 26 | 94.30 80 | 100.00 1 | 99.04 52 | 99.99 20 | 99.99 20 |
|
DSMNet-mixed | | | 88.28 283 | 88.24 277 | 88.42 315 | 89.64 329 | 75.38 337 | 98.06 289 | 89.86 345 | 85.59 294 | 88.20 264 | 92.14 322 | 76.15 277 | 91.95 335 | 78.46 314 | 96.05 174 | 97.92 204 |
|
tpm2 | | | 95.47 151 | 95.18 149 | 96.35 186 | 96.91 217 | 91.70 247 | 96.96 307 | 97.93 189 | 88.04 262 | 98.44 101 | 95.40 268 | 93.32 106 | 97.97 218 | 94.00 177 | 95.61 185 | 99.38 158 |
|
NP-MVS | | | | | | 95.77 246 | 91.79 241 | | | | | 98.65 169 | | | | | |
|
EG-PatchMatch MVS | | | 85.35 295 | 83.81 296 | 89.99 307 | 90.39 325 | 81.89 326 | 98.21 284 | 96.09 312 | 81.78 316 | 74.73 330 | 93.72 311 | 51.56 340 | 97.12 258 | 79.16 313 | 88.61 231 | 90.96 325 |
|
tpm cat1 | | | 93.51 194 | 92.52 203 | 96.47 179 | 97.77 178 | 91.47 253 | 96.13 315 | 98.06 178 | 80.98 318 | 92.91 198 | 93.78 310 | 89.66 164 | 98.87 159 | 87.03 270 | 96.39 169 | 99.09 182 |
|
SteuartSystems-ACMMP | | | 99.02 11 | 98.97 11 | 99.18 54 | 98.72 132 | 97.71 82 | 99.98 8 | 98.44 104 | 96.85 29 | 99.80 16 | 99.91 13 | 97.57 6 | 99.85 94 | 99.44 37 | 99.99 20 | 99.99 20 |
Skip Steuart: Steuart Systems R&D Blog. |
CostFormer | | | 96.10 136 | 95.88 132 | 96.78 171 | 97.03 212 | 92.55 225 | 97.08 304 | 97.83 200 | 90.04 231 | 98.72 89 | 94.89 291 | 95.01 55 | 98.29 203 | 96.54 135 | 95.77 181 | 99.50 146 |
|
CR-MVSNet | | | 93.45 197 | 92.62 198 | 95.94 194 | 96.29 232 | 92.66 221 | 92.01 334 | 96.23 308 | 92.62 165 | 96.94 137 | 93.31 314 | 91.04 149 | 96.03 306 | 79.23 312 | 95.96 176 | 99.13 180 |
|
JIA-IIPM | | | 91.76 232 | 90.70 231 | 94.94 217 | 96.11 235 | 87.51 300 | 93.16 330 | 98.13 174 | 75.79 330 | 97.58 125 | 77.68 340 | 92.84 117 | 97.97 218 | 88.47 253 | 96.54 166 | 99.33 164 |
|
Patchmtry | | | 89.70 270 | 88.49 272 | 93.33 270 | 96.24 234 | 89.94 277 | 91.37 337 | 96.23 308 | 78.22 324 | 87.69 268 | 93.31 314 | 91.04 149 | 96.03 306 | 80.18 311 | 82.10 280 | 94.02 268 |
|
PatchT | | | 90.38 256 | 88.75 269 | 95.25 208 | 95.99 239 | 90.16 272 | 91.22 338 | 97.54 223 | 76.80 326 | 97.26 131 | 86.01 335 | 91.88 136 | 96.07 305 | 66.16 336 | 95.91 178 | 99.51 144 |
|
tpmrst | | | 96.27 135 | 95.98 122 | 97.13 163 | 97.96 165 | 93.15 209 | 96.34 313 | 98.17 166 | 92.07 184 | 98.71 90 | 95.12 282 | 93.91 92 | 98.73 169 | 94.91 154 | 96.62 165 | 99.50 146 |
|
BH-w/o | | | 95.71 146 | 95.38 143 | 96.68 175 | 98.49 140 | 92.28 229 | 99.84 105 | 97.50 231 | 92.12 183 | 92.06 204 | 98.79 164 | 84.69 211 | 98.67 174 | 95.29 148 | 99.66 92 | 99.09 182 |
|
tpm | | | 93.70 192 | 93.41 186 | 94.58 230 | 95.36 260 | 87.41 301 | 97.01 305 | 96.90 286 | 90.85 217 | 96.72 144 | 94.14 307 | 90.40 157 | 96.84 275 | 90.75 229 | 88.54 234 | 99.51 144 |
|
DELS-MVS | | | 98.54 39 | 98.22 47 | 99.50 29 | 99.15 107 | 98.65 48 | 100.00 1 | 98.58 72 | 97.70 9 | 98.21 113 | 99.24 128 | 92.58 124 | 99.94 68 | 98.63 78 | 99.94 56 | 99.92 84 |
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 | | | 95.18 155 | 94.83 155 | 96.22 188 | 98.36 144 | 91.22 254 | 99.80 119 | 97.32 249 | 90.91 215 | 91.08 211 | 98.67 168 | 83.51 219 | 98.54 180 | 94.23 174 | 99.61 97 | 98.92 186 |
|
RPMNet | | | 89.39 274 | 87.20 285 | 95.94 194 | 96.29 232 | 92.66 221 | 92.01 334 | 97.63 209 | 70.19 338 | 96.94 137 | 85.87 336 | 87.25 189 | 96.03 306 | 62.69 339 | 95.96 176 | 99.13 180 |
|
MVSTER | | | 95.53 149 | 95.22 147 | 96.45 181 | 98.56 135 | 97.72 81 | 99.91 69 | 97.67 207 | 92.38 177 | 91.39 208 | 97.14 210 | 97.24 14 | 97.30 244 | 94.80 157 | 87.85 240 | 94.34 240 |
|
CPTT-MVS | | | 97.64 84 | 97.32 85 | 98.58 103 | 99.97 3 | 95.77 152 | 99.96 23 | 98.35 142 | 89.90 232 | 98.36 105 | 99.79 60 | 91.18 147 | 99.99 36 | 98.37 85 | 99.99 20 | 99.99 20 |
|
GBi-Net | | | 90.88 244 | 89.82 248 | 94.08 250 | 97.53 192 | 91.97 234 | 98.43 272 | 96.95 280 | 87.05 273 | 89.68 230 | 94.72 293 | 71.34 299 | 96.11 301 | 87.01 271 | 85.65 255 | 94.17 252 |
|
PVSNet_Blended_VisFu | | | 97.27 95 | 96.81 99 | 98.66 95 | 98.81 128 | 96.67 120 | 99.92 65 | 98.64 62 | 94.51 94 | 96.38 154 | 98.49 179 | 89.05 173 | 99.88 85 | 97.10 125 | 98.34 129 | 99.43 154 |
|
PVSNet_BlendedMVS | | | 96.05 137 | 95.82 134 | 96.72 174 | 99.59 86 | 96.99 112 | 99.95 40 | 99.10 28 | 94.06 114 | 98.27 109 | 95.80 249 | 89.00 174 | 99.95 60 | 99.12 45 | 87.53 245 | 93.24 305 |
|
UnsupCasMVSNet_eth | | | 85.52 292 | 83.99 292 | 90.10 305 | 89.36 330 | 83.51 318 | 96.65 309 | 97.99 182 | 89.14 238 | 75.89 328 | 93.83 309 | 63.25 326 | 93.92 327 | 81.92 303 | 67.90 335 | 92.88 310 |
|
UnsupCasMVSNet_bld | | | 79.97 309 | 77.03 311 | 88.78 313 | 85.62 335 | 81.98 325 | 93.66 328 | 97.35 246 | 75.51 332 | 70.79 334 | 83.05 337 | 48.70 342 | 94.91 322 | 78.31 315 | 60.29 340 | 89.46 335 |
|
PVSNet_Blended | | | 97.94 69 | 97.64 72 | 98.83 88 | 99.59 86 | 96.99 112 | 100.00 1 | 99.10 28 | 95.38 70 | 98.27 109 | 99.08 135 | 89.00 174 | 99.95 60 | 99.12 45 | 99.25 112 | 99.57 134 |
|
FMVSNet5 | | | 88.32 282 | 87.47 283 | 90.88 297 | 96.90 218 | 88.39 294 | 97.28 302 | 95.68 318 | 82.60 313 | 84.67 300 | 92.40 321 | 79.83 251 | 91.16 336 | 76.39 323 | 81.51 285 | 93.09 307 |
|
test1 | | | 90.88 244 | 89.82 248 | 94.08 250 | 97.53 192 | 91.97 234 | 98.43 272 | 96.95 280 | 87.05 273 | 89.68 230 | 94.72 293 | 71.34 299 | 96.11 301 | 87.01 271 | 85.65 255 | 94.17 252 |
|
new_pmnet | | | 84.49 300 | 82.92 300 | 89.21 310 | 90.03 327 | 82.60 320 | 96.89 308 | 95.62 320 | 80.59 319 | 75.77 329 | 89.17 327 | 65.04 322 | 94.79 324 | 72.12 327 | 81.02 291 | 90.23 329 |
|
FMVSNet3 | | | 92.69 210 | 91.58 218 | 95.99 192 | 98.29 146 | 97.42 99 | 99.26 208 | 97.62 212 | 89.80 234 | 89.68 230 | 95.32 274 | 81.62 232 | 96.27 297 | 87.01 271 | 85.65 255 | 94.29 243 |
|
dp | | | 95.05 158 | 94.43 162 | 96.91 167 | 97.99 164 | 92.73 219 | 96.29 314 | 97.98 184 | 89.70 235 | 95.93 160 | 94.67 297 | 93.83 96 | 98.45 186 | 86.91 274 | 96.53 167 | 99.54 140 |
|
FMVSNet2 | | | 91.02 241 | 89.56 252 | 95.41 205 | 97.53 192 | 95.74 154 | 98.98 234 | 97.41 241 | 87.05 273 | 88.43 259 | 95.00 287 | 71.34 299 | 96.24 299 | 85.12 283 | 85.21 260 | 94.25 247 |
|
FMVSNet1 | | | 88.50 281 | 86.64 286 | 94.08 250 | 95.62 257 | 91.97 234 | 98.43 272 | 96.95 280 | 83.00 310 | 86.08 293 | 94.72 293 | 59.09 333 | 96.11 301 | 81.82 304 | 84.07 270 | 94.17 252 |
|
N_pmnet | | | 80.06 308 | 80.78 306 | 77.89 323 | 91.94 311 | 45.28 350 | 98.80 252 | 56.82 354 | 78.10 325 | 80.08 316 | 93.33 312 | 77.03 266 | 95.76 312 | 68.14 333 | 82.81 276 | 92.64 312 |
|
cascas | | | 94.64 169 | 93.61 176 | 97.74 145 | 97.82 175 | 96.26 133 | 99.96 23 | 97.78 203 | 85.76 290 | 94.00 186 | 97.54 200 | 76.95 268 | 99.21 149 | 97.23 121 | 95.43 188 | 97.76 209 |
|
BH-RMVSNet | | | 95.18 155 | 94.31 164 | 97.80 139 | 98.17 156 | 95.23 170 | 99.76 130 | 97.53 225 | 92.52 173 | 94.27 183 | 99.25 127 | 76.84 269 | 98.80 162 | 90.89 226 | 99.54 101 | 99.35 162 |
|
UGNet | | | 95.33 153 | 94.57 160 | 97.62 149 | 98.55 136 | 94.85 177 | 98.67 261 | 99.32 24 | 95.75 63 | 96.80 142 | 96.27 240 | 72.18 296 | 99.96 53 | 94.58 166 | 99.05 116 | 98.04 203 |
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 | | | 98.10 65 | 97.60 74 | 99.60 17 | 98.92 121 | 99.28 12 | 99.89 81 | 99.52 13 | 95.58 67 | 98.24 112 | 99.39 116 | 93.33 105 | 99.74 120 | 97.98 103 | 95.58 186 | 99.78 100 |
|
XXY-MVS | | | 91.82 225 | 90.46 234 | 95.88 196 | 93.91 281 | 95.40 164 | 98.87 247 | 97.69 206 | 88.63 254 | 87.87 267 | 97.08 213 | 74.38 289 | 97.89 224 | 91.66 212 | 84.07 270 | 94.35 239 |
|
sss | | | 97.57 85 | 97.03 94 | 99.18 54 | 98.37 143 | 98.04 71 | 99.73 139 | 99.38 21 | 93.46 137 | 98.76 87 | 99.06 136 | 91.21 143 | 99.89 79 | 96.33 136 | 97.01 160 | 99.62 122 |
|
Test_1112_low_res | | | 95.72 144 | 94.83 155 | 98.42 117 | 97.79 177 | 96.41 128 | 99.65 153 | 96.65 301 | 92.70 160 | 92.86 200 | 96.13 244 | 92.15 132 | 99.30 147 | 91.88 210 | 93.64 205 | 99.55 136 |
|
1112_ss | | | 96.01 139 | 95.20 148 | 98.42 117 | 97.80 176 | 96.41 128 | 99.65 153 | 96.66 300 | 92.71 159 | 92.88 199 | 99.40 114 | 92.16 131 | 99.30 147 | 91.92 209 | 93.66 204 | 99.55 136 |
|
ab-mvs-re | | | 8.28 324 | 11.04 326 | 0.00 337 | 0.00 356 | 0.00 357 | 0.00 348 | 0.00 358 | 0.00 352 | 0.00 353 | 99.40 114 | 0.00 358 | 0.00 354 | 0.00 351 | 0.00 351 | 0.00 350 |
|
ab-mvs | | | 94.69 166 | 93.42 184 | 98.51 110 | 98.07 160 | 96.26 133 | 96.49 311 | 98.68 55 | 90.31 226 | 94.54 177 | 97.00 218 | 76.30 274 | 99.71 124 | 95.98 141 | 93.38 208 | 99.56 135 |
|
TR-MVS | | | 94.54 172 | 93.56 181 | 97.49 152 | 97.96 165 | 94.34 188 | 98.71 257 | 97.51 230 | 90.30 227 | 94.51 179 | 98.69 167 | 75.56 279 | 98.77 166 | 92.82 201 | 95.99 175 | 99.35 162 |
|
MDTV_nov1_ep13_2view | | | | | | | 96.26 133 | 96.11 316 | | 91.89 189 | 98.06 115 | | 94.40 70 | | 94.30 172 | | 99.67 114 |
|
MDTV_nov1_ep13 | | | | 95.69 136 | | 97.90 168 | 94.15 191 | 95.98 318 | 98.44 104 | 93.12 146 | 97.98 117 | 95.74 251 | 95.10 49 | 98.58 177 | 90.02 239 | 96.92 162 | |
|
MIMVSNet1 | | | 82.58 304 | 80.51 307 | 88.78 313 | 86.68 334 | 84.20 317 | 96.65 309 | 95.41 323 | 78.75 323 | 78.59 320 | 92.44 320 | 51.88 339 | 89.76 339 | 65.26 338 | 78.95 304 | 92.38 315 |
|
MIMVSNet | | | 90.30 259 | 88.67 270 | 95.17 211 | 96.45 231 | 91.64 249 | 92.39 332 | 97.15 262 | 85.99 287 | 90.50 216 | 93.19 316 | 66.95 315 | 94.86 323 | 82.01 302 | 93.43 206 | 99.01 185 |
|
IterMVS-LS | | | 92.69 210 | 92.11 208 | 94.43 241 | 96.80 223 | 92.74 217 | 99.45 185 | 96.89 287 | 88.98 243 | 89.65 233 | 95.38 271 | 88.77 176 | 96.34 294 | 90.98 223 | 82.04 281 | 94.22 248 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CDS-MVSNet | | | 96.34 129 | 96.07 117 | 97.13 163 | 97.37 198 | 94.96 175 | 99.53 173 | 97.91 192 | 91.55 199 | 95.37 170 | 98.32 186 | 95.05 52 | 97.13 256 | 93.80 183 | 95.75 183 | 99.30 167 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
ACMMP++_ref | | | | | | | | | | | | | | | | 87.04 248 | |
|
IterMVS | | | 90.91 243 | 90.17 243 | 93.12 275 | 96.78 226 | 90.42 269 | 98.89 242 | 97.05 270 | 89.03 241 | 86.49 286 | 95.42 267 | 76.59 271 | 95.02 319 | 87.22 267 | 84.09 269 | 93.93 278 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DP-MVS Recon | | | 98.41 49 | 98.02 58 | 99.56 21 | 99.97 3 | 98.70 43 | 99.92 65 | 98.44 104 | 92.06 186 | 98.40 104 | 99.84 46 | 95.68 38 | 100.00 1 | 98.19 89 | 99.71 89 | 99.97 62 |
|
MVS_111021_LR | | | 98.42 48 | 98.38 38 | 98.53 109 | 99.39 99 | 95.79 151 | 99.87 87 | 99.86 2 | 96.70 36 | 98.78 86 | 99.79 60 | 92.03 134 | 99.90 75 | 99.17 44 | 99.86 75 | 99.88 89 |
|
DP-MVS | | | 94.54 172 | 93.42 184 | 97.91 138 | 99.46 98 | 94.04 193 | 98.93 240 | 97.48 233 | 81.15 317 | 90.04 221 | 99.55 102 | 87.02 192 | 99.95 60 | 88.97 247 | 98.11 135 | 99.73 105 |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.23 237 | |
|
HQP-MVS | | | 94.61 170 | 94.50 161 | 94.92 218 | 95.78 243 | 91.85 239 | 99.87 87 | 97.89 193 | 96.82 30 | 93.37 191 | 98.65 169 | 80.65 243 | 98.39 193 | 97.92 105 | 89.60 215 | 94.53 222 |
|
QAPM | | | 95.40 152 | 94.17 166 | 99.10 69 | 96.92 216 | 97.71 82 | 99.40 188 | 98.68 55 | 89.31 237 | 88.94 250 | 98.89 154 | 82.48 225 | 99.96 53 | 93.12 199 | 99.83 77 | 99.62 122 |
|
Vis-MVSNet | | | 95.72 144 | 95.15 150 | 97.45 153 | 97.62 189 | 94.28 189 | 99.28 206 | 98.24 156 | 94.27 106 | 96.84 140 | 98.94 152 | 79.39 253 | 98.76 167 | 93.25 193 | 98.49 126 | 99.30 167 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MVS-HIRNet | | | 86.22 289 | 83.19 299 | 95.31 207 | 96.71 229 | 90.29 270 | 92.12 333 | 97.33 248 | 62.85 339 | 86.82 280 | 70.37 342 | 69.37 306 | 97.49 235 | 75.12 325 | 97.99 141 | 98.15 201 |
|
IS-MVSNet | | | 96.29 133 | 95.90 131 | 97.45 153 | 98.13 159 | 94.80 180 | 99.08 220 | 97.61 215 | 92.02 187 | 95.54 168 | 98.96 149 | 90.64 155 | 98.08 213 | 93.73 187 | 97.41 151 | 99.47 148 |
|
HyFIR lowres test | | | 96.66 120 | 96.43 111 | 97.36 159 | 99.05 109 | 93.91 196 | 99.70 144 | 99.80 3 | 90.54 221 | 96.26 155 | 98.08 190 | 92.15 132 | 98.23 208 | 96.84 133 | 95.46 187 | 99.93 78 |
|
EPMVS | | | 96.53 123 | 96.01 119 | 98.09 131 | 98.43 142 | 96.12 143 | 96.36 312 | 99.43 19 | 93.53 135 | 97.64 124 | 95.04 284 | 94.41 69 | 98.38 197 | 91.13 218 | 98.11 135 | 99.75 103 |
|
PAPM_NR | | | 98.12 64 | 97.93 65 | 98.70 92 | 99.94 14 | 96.13 141 | 99.82 112 | 98.43 112 | 94.56 92 | 97.52 126 | 99.70 84 | 94.40 70 | 99.98 42 | 97.00 127 | 99.98 33 | 99.99 20 |
|
TAMVS | | | 95.85 141 | 95.58 138 | 96.65 177 | 97.07 209 | 93.50 203 | 99.17 214 | 97.82 201 | 91.39 207 | 95.02 174 | 98.01 192 | 92.20 130 | 97.30 244 | 93.75 186 | 95.83 180 | 99.14 179 |
|
PAPR | | | 98.52 41 | 98.16 51 | 99.58 20 | 99.97 3 | 98.77 36 | 99.95 40 | 98.43 112 | 95.35 71 | 98.03 116 | 99.75 75 | 94.03 89 | 99.98 42 | 98.11 94 | 99.83 77 | 99.99 20 |
|
RPSCF | | | 91.80 229 | 92.79 196 | 88.83 312 | 98.15 157 | 69.87 339 | 98.11 287 | 96.60 302 | 83.93 305 | 94.33 182 | 99.27 123 | 79.60 252 | 99.46 145 | 91.99 207 | 93.16 210 | 97.18 212 |
|
Vis-MVSNet (Re-imp) | | | 96.32 130 | 95.98 122 | 97.35 160 | 97.93 167 | 94.82 179 | 99.47 182 | 98.15 171 | 91.83 191 | 95.09 173 | 99.11 133 | 91.37 142 | 97.47 236 | 93.47 191 | 97.43 148 | 99.74 104 |
|
test_0402 | | | 85.58 291 | 83.94 294 | 90.50 301 | 93.81 283 | 85.04 313 | 98.55 265 | 95.20 328 | 76.01 328 | 79.72 317 | 95.13 281 | 64.15 324 | 96.26 298 | 66.04 337 | 86.88 249 | 90.21 330 |
|
MVS_111021_HR | | | 98.72 27 | 98.62 22 | 99.01 78 | 99.36 101 | 97.18 105 | 99.93 61 | 99.90 1 | 96.81 33 | 98.67 91 | 99.77 66 | 93.92 91 | 99.89 79 | 99.27 43 | 99.94 56 | 99.96 67 |
|
CSCG | | | 97.10 100 | 97.04 93 | 97.27 162 | 99.89 44 | 91.92 238 | 99.90 73 | 99.07 31 | 88.67 252 | 95.26 172 | 99.82 52 | 93.17 112 | 99.98 42 | 98.15 92 | 99.47 105 | 99.90 86 |
|
PatchMatch-RL | | | 96.04 138 | 95.40 141 | 97.95 135 | 99.59 86 | 95.22 171 | 99.52 174 | 99.07 31 | 93.96 119 | 96.49 149 | 98.35 185 | 82.28 226 | 99.82 105 | 90.15 238 | 99.22 113 | 98.81 193 |
|
API-MVS | | | 97.86 72 | 97.66 71 | 98.47 112 | 99.52 92 | 95.41 163 | 99.47 182 | 98.87 43 | 91.68 195 | 98.84 83 | 99.85 33 | 92.34 128 | 99.99 36 | 98.44 83 | 99.96 47 | 100.00 1 |
|
Test By Simon | | | | | | | | | | | | | 92.82 119 | | | | |
|
TDRefinement | | | 84.76 297 | 82.56 301 | 91.38 295 | 74.58 343 | 84.80 315 | 97.36 301 | 94.56 334 | 84.73 301 | 80.21 315 | 96.12 245 | 63.56 325 | 98.39 193 | 87.92 258 | 63.97 337 | 90.95 326 |
|
USDC | | | 90.00 267 | 88.96 265 | 93.10 277 | 94.81 267 | 88.16 296 | 98.71 257 | 95.54 322 | 93.66 132 | 83.75 305 | 97.20 209 | 65.58 319 | 98.31 202 | 83.96 291 | 87.49 246 | 92.85 311 |
|
EPP-MVSNet | | | 96.69 118 | 96.60 105 | 96.96 166 | 97.74 181 | 93.05 212 | 99.37 195 | 98.56 76 | 88.75 250 | 95.83 163 | 99.01 140 | 96.01 28 | 98.56 178 | 96.92 131 | 97.20 156 | 99.25 171 |
|
PMMVS | | | 96.76 113 | 96.76 101 | 96.76 172 | 98.28 147 | 92.10 233 | 99.91 69 | 97.98 184 | 94.12 109 | 99.53 44 | 99.39 116 | 86.93 193 | 98.73 169 | 96.95 130 | 97.73 142 | 99.45 151 |
|
PAPM | | | 98.60 33 | 98.42 31 | 99.14 63 | 96.05 237 | 98.96 20 | 99.90 73 | 99.35 23 | 96.68 37 | 98.35 106 | 99.66 93 | 96.45 25 | 98.51 181 | 99.45 36 | 99.89 70 | 99.96 67 |
|
ACMMP | | | 97.74 81 | 97.44 79 | 98.66 95 | 99.92 35 | 96.13 141 | 99.18 213 | 99.45 17 | 94.84 84 | 96.41 153 | 99.71 82 | 91.40 141 | 99.99 36 | 97.99 101 | 98.03 140 | 99.87 90 |
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 | | | 97.76 80 | 97.38 80 | 98.92 85 | 99.53 91 | 96.84 116 | 99.87 87 | 98.14 172 | 93.78 128 | 96.55 148 | 99.69 87 | 92.28 129 | 99.98 42 | 97.13 123 | 99.44 107 | 99.93 78 |
|
PatchmatchNet | | | 95.94 140 | 95.45 140 | 97.39 157 | 97.83 174 | 94.41 187 | 96.05 317 | 98.40 129 | 92.86 150 | 97.09 135 | 95.28 279 | 94.21 85 | 98.07 215 | 89.26 245 | 98.11 135 | 99.70 109 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
PHI-MVS | | | 98.41 49 | 98.21 48 | 99.03 75 | 99.86 52 | 97.10 109 | 99.98 8 | 98.80 49 | 90.78 219 | 99.62 37 | 99.78 64 | 95.30 46 | 100.00 1 | 99.80 18 | 99.93 62 | 99.99 20 |
|
F-COLMAP | | | 96.93 106 | 96.95 96 | 96.87 169 | 99.71 80 | 91.74 243 | 99.85 101 | 97.95 187 | 93.11 147 | 95.72 165 | 99.16 132 | 92.35 127 | 99.94 68 | 95.32 147 | 99.35 110 | 98.92 186 |
|
ANet_high | | | 56.10 315 | 52.24 317 | 67.66 329 | 49.27 351 | 56.82 345 | 83.94 342 | 82.02 350 | 70.47 337 | 33.28 350 | 64.54 345 | 17.23 353 | 69.16 348 | 45.59 345 | 23.85 346 | 77.02 341 |
|
wuyk23d | | | 20.37 323 | 20.84 325 | 18.99 336 | 65.34 348 | 27.73 354 | 50.43 347 | 7.67 357 | 9.50 351 | 8.01 352 | 6.34 352 | 6.13 356 | 26.24 351 | 23.40 350 | 10.69 349 | 2.99 349 |
|
OMC-MVS | | | 97.28 94 | 97.23 86 | 97.41 155 | 99.76 70 | 93.36 208 | 99.65 153 | 97.95 187 | 96.03 55 | 97.41 129 | 99.70 84 | 89.61 165 | 99.51 138 | 96.73 134 | 98.25 134 | 99.38 158 |
|
MG-MVS | | | 98.91 17 | 98.65 20 | 99.68 11 | 99.94 14 | 99.07 18 | 99.64 157 | 99.44 18 | 97.33 17 | 99.00 79 | 99.72 80 | 94.03 89 | 99.98 42 | 98.73 70 | 100.00 1 | 100.00 1 |
|
AdaColmap | | | 97.23 97 | 96.80 100 | 98.51 110 | 99.99 1 | 95.60 159 | 99.09 218 | 98.84 46 | 93.32 140 | 96.74 143 | 99.72 80 | 86.04 200 | 100.00 1 | 98.01 99 | 99.43 108 | 99.94 77 |
|
uanet | | | 0.00 326 | 0.00 328 | 0.00 337 | 0.00 356 | 0.00 357 | 0.00 348 | 0.00 358 | 0.00 352 | 0.00 353 | 0.00 353 | 0.00 358 | 0.00 354 | 0.00 351 | 0.00 351 | 0.00 350 |
|
ITE_SJBPF | | | | | 92.38 284 | 95.69 254 | 85.14 312 | | 95.71 317 | 92.81 153 | 89.33 241 | 98.11 189 | 70.23 304 | 98.42 188 | 85.91 279 | 88.16 238 | 93.59 297 |
|
DeepMVS_CX | | | | | 82.92 322 | 95.98 241 | 58.66 344 | | 96.01 313 | 92.72 158 | 78.34 321 | 95.51 263 | 58.29 334 | 98.08 213 | 82.57 298 | 85.29 258 | 92.03 318 |
|
TinyColmap | | | 87.87 285 | 86.51 287 | 91.94 290 | 95.05 264 | 85.57 309 | 97.65 297 | 94.08 336 | 84.40 303 | 81.82 309 | 96.85 224 | 62.14 328 | 98.33 200 | 80.25 310 | 86.37 252 | 91.91 320 |
|
MAR-MVS | | | 97.43 87 | 97.19 87 | 98.15 130 | 99.47 96 | 94.79 181 | 99.05 229 | 98.76 50 | 92.65 164 | 98.66 92 | 99.82 52 | 88.52 180 | 99.98 42 | 98.12 93 | 99.63 93 | 99.67 114 |
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 | | | 89.25 278 | 88.85 266 | 90.45 303 | 92.81 303 | 81.19 329 | 98.12 286 | 94.79 331 | 91.44 203 | 86.29 290 | 97.11 211 | 65.30 321 | 98.11 212 | 88.53 252 | 85.25 259 | 92.07 316 |
|
MSDG | | | 94.37 177 | 93.36 188 | 97.40 156 | 98.88 126 | 93.95 195 | 99.37 195 | 97.38 244 | 85.75 292 | 90.80 214 | 99.17 131 | 84.11 217 | 99.88 85 | 86.35 275 | 98.43 128 | 98.36 198 |
|
LS3D | | | 95.84 142 | 95.11 151 | 98.02 134 | 99.85 53 | 95.10 173 | 98.74 254 | 98.50 97 | 87.22 272 | 93.66 190 | 99.86 29 | 87.45 187 | 99.95 60 | 90.94 224 | 99.81 83 | 99.02 184 |
|
CLD-MVS | | | 94.06 182 | 93.90 172 | 94.55 232 | 96.02 238 | 90.69 260 | 99.98 8 | 97.72 204 | 96.62 39 | 91.05 212 | 98.85 163 | 77.21 265 | 98.47 182 | 98.11 94 | 89.51 220 | 94.48 226 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
FPMVS | | | 68.72 310 | 68.72 312 | 68.71 328 | 65.95 347 | 44.27 352 | 95.97 319 | 94.74 332 | 51.13 341 | 53.26 344 | 90.50 326 | 25.11 349 | 83.00 344 | 60.80 340 | 80.97 293 | 78.87 340 |
|
Gipuma | | | 66.95 313 | 65.00 313 | 72.79 326 | 91.52 317 | 67.96 340 | 66.16 346 | 95.15 330 | 47.89 342 | 58.54 340 | 67.99 344 | 29.74 346 | 87.54 341 | 50.20 343 | 77.83 312 | 62.87 344 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |