LCM-MVSNet | | | 95.70 1 | 96.40 1 | 93.61 2 | 98.67 1 | 85.39 29 | 95.54 3 | 97.36 1 | 96.97 1 | 99.04 1 | 99.05 1 | 96.61 1 | 95.92 10 | 85.07 38 | 99.27 3 | 99.54 1 |
|
PMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 80.48 6 | 90.08 35 | 90.66 37 | 88.34 67 | 96.71 2 | 92.97 2 | 90.31 39 | 89.57 168 | 88.51 14 | 90.11 85 | 95.12 48 | 90.98 7 | 88.92 230 | 77.55 130 | 97.07 68 | 83.13 300 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
zzz-MVS | | | 91.27 19 | 91.26 26 | 91.29 25 | 96.59 3 | 86.29 14 | 88.94 65 | 91.81 95 | 84.07 33 | 92.00 60 | 94.40 71 | 86.63 40 | 95.28 38 | 88.59 4 | 98.31 25 | 92.30 157 |
|
MTAPA | | | 91.52 13 | 91.60 15 | 91.29 25 | 96.59 3 | 86.29 14 | 92.02 24 | 91.81 95 | 84.07 33 | 92.00 60 | 94.40 71 | 86.63 40 | 95.28 38 | 88.59 4 | 98.31 25 | 92.30 157 |
|
PEN-MVS | | | 90.03 37 | 91.88 12 | 84.48 134 | 96.57 5 | 58.88 258 | 88.95 64 | 93.19 50 | 91.62 3 | 96.01 6 | 96.16 22 | 87.02 37 | 95.60 23 | 78.69 120 | 98.72 10 | 98.97 3 |
|
PS-CasMVS | | | 90.06 36 | 91.92 9 | 84.47 135 | 96.56 6 | 58.83 261 | 89.04 63 | 92.74 69 | 91.40 4 | 96.12 4 | 96.06 24 | 87.23 35 | 95.57 24 | 79.42 114 | 98.74 7 | 99.00 2 |
|
DTE-MVSNet | | | 89.98 39 | 91.91 11 | 84.21 145 | 96.51 7 | 57.84 264 | 88.93 66 | 92.84 66 | 91.92 2 | 96.16 3 | 96.23 20 | 86.95 38 | 95.99 6 | 79.05 117 | 98.57 16 | 98.80 6 |
|
CP-MVSNet | | | 89.27 54 | 90.91 35 | 84.37 139 | 96.34 8 | 58.61 263 | 88.66 72 | 92.06 86 | 90.78 5 | 95.67 9 | 95.17 46 | 81.80 91 | 95.54 27 | 79.00 118 | 98.69 11 | 98.95 4 |
|
WR-MVS_H | | | 89.91 44 | 91.31 24 | 85.71 111 | 96.32 9 | 62.39 219 | 89.54 55 | 93.31 43 | 90.21 9 | 95.57 11 | 95.66 31 | 81.42 95 | 95.90 11 | 80.94 89 | 98.80 4 | 98.84 5 |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 91.14 23 | 90.91 35 | 91.83 18 | 96.18 10 | 86.88 11 | 92.20 22 | 93.03 58 | 82.59 47 | 88.52 128 | 94.37 74 | 86.74 39 | 95.41 33 | 86.32 28 | 98.21 31 | 93.19 129 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
mPP-MVS | | | 91.69 10 | 91.47 19 | 92.37 5 | 96.04 11 | 88.48 8 | 92.72 14 | 92.60 74 | 83.09 41 | 91.54 68 | 94.25 77 | 87.67 33 | 95.51 30 | 87.21 24 | 98.11 34 | 93.12 130 |
|
MP-MVS-pluss | | | 90.81 25 | 91.08 28 | 89.99 47 | 95.97 12 | 79.88 64 | 88.13 78 | 94.51 11 | 75.79 136 | 92.94 39 | 94.96 51 | 88.36 19 | 95.01 47 | 90.70 2 | 98.40 21 | 95.09 77 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
TDRefinement | | | 93.52 2 | 93.39 3 | 93.88 1 | 95.94 13 | 90.26 4 | 95.70 2 | 96.46 2 | 90.58 7 | 92.86 42 | 96.29 18 | 88.16 25 | 94.17 69 | 86.07 34 | 98.48 19 | 97.22 25 |
|
ACMMP_Plus | | | 90.65 27 | 91.07 30 | 89.42 54 | 95.93 14 | 79.54 69 | 89.95 44 | 93.68 33 | 77.65 106 | 91.97 62 | 94.89 53 | 88.38 18 | 95.45 31 | 89.27 3 | 97.87 45 | 93.27 125 |
|
HPM-MVS_fast | | | 92.50 5 | 92.54 5 | 92.37 5 | 95.93 14 | 85.81 27 | 92.99 11 | 94.23 16 | 85.21 25 | 92.51 52 | 95.13 47 | 90.65 10 | 95.34 35 | 88.06 10 | 98.15 33 | 95.95 51 |
|
HSP-MVS | | | 88.63 61 | 87.84 70 | 91.02 29 | 95.76 16 | 86.14 19 | 92.75 13 | 91.01 127 | 78.43 97 | 89.16 117 | 92.25 132 | 72.03 210 | 96.36 2 | 88.21 9 | 90.93 238 | 90.55 204 |
|
region2R | | | 91.44 17 | 91.30 25 | 91.87 16 | 95.75 17 | 85.90 23 | 92.63 17 | 93.30 44 | 81.91 56 | 90.88 80 | 94.21 78 | 87.75 30 | 95.87 12 | 87.60 17 | 97.71 52 | 93.83 108 |
|
ACMMPR | | | 91.49 14 | 91.35 23 | 91.92 14 | 95.74 18 | 85.88 24 | 92.58 18 | 93.25 48 | 81.99 54 | 91.40 71 | 94.17 80 | 87.51 34 | 95.87 12 | 87.74 12 | 97.76 48 | 93.99 102 |
|
TSAR-MVS + MP. | | | 88.14 67 | 87.82 71 | 89.09 59 | 95.72 19 | 76.74 95 | 92.49 20 | 91.19 123 | 67.85 235 | 86.63 156 | 94.84 55 | 79.58 110 | 95.96 9 | 87.62 15 | 94.50 156 | 94.56 84 |
|
PGM-MVS | | | 91.20 21 | 90.95 34 | 91.93 13 | 95.67 20 | 85.85 25 | 90.00 41 | 93.90 29 | 80.32 72 | 91.74 66 | 94.41 70 | 88.17 24 | 95.98 7 | 86.37 27 | 97.99 40 | 93.96 104 |
|
XVS | | | 91.54 12 | 91.36 21 | 92.08 8 | 95.64 21 | 86.25 16 | 92.64 15 | 93.33 41 | 85.07 26 | 89.99 89 | 94.03 85 | 86.57 42 | 95.80 15 | 87.35 20 | 97.62 55 | 94.20 95 |
|
X-MVStestdata | | | 85.04 116 | 82.70 165 | 92.08 8 | 95.64 21 | 86.25 16 | 92.64 15 | 93.33 41 | 85.07 26 | 89.99 89 | 16.05 366 | 86.57 42 | 95.80 15 | 87.35 20 | 97.62 55 | 94.20 95 |
|
HPM-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 92.13 6 | 92.20 7 | 91.91 15 | 95.58 23 | 84.67 38 | 93.51 6 | 94.85 9 | 82.88 44 | 91.77 65 | 93.94 93 | 90.55 12 | 95.73 20 | 88.50 8 | 98.23 30 | 95.33 70 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 91.91 9 | 91.87 13 | 92.03 11 | 95.53 24 | 85.91 22 | 93.35 9 | 94.16 20 | 82.52 48 | 92.39 56 | 94.14 81 | 89.15 16 | 95.62 22 | 87.35 20 | 98.24 29 | 94.56 84 |
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 |
GST-MVS | | | 90.96 24 | 91.01 31 | 90.82 34 | 95.45 25 | 82.73 51 | 91.75 28 | 93.74 32 | 80.98 66 | 91.38 72 | 93.80 97 | 87.20 36 | 95.80 15 | 87.10 25 | 97.69 53 | 93.93 105 |
|
HFP-MVS | | | 91.30 18 | 91.39 20 | 91.02 29 | 95.43 26 | 84.66 39 | 92.58 18 | 93.29 46 | 81.99 54 | 91.47 69 | 93.96 89 | 88.35 20 | 95.56 25 | 87.74 12 | 97.74 50 | 92.85 135 |
|
#test# | | | 90.49 32 | 90.31 43 | 91.02 29 | 95.43 26 | 84.66 39 | 90.65 37 | 93.29 46 | 77.00 121 | 91.47 69 | 93.96 89 | 88.35 20 | 95.56 25 | 84.88 41 | 97.74 50 | 92.85 135 |
|
SMA-MVS | | | 90.31 33 | 90.48 41 | 89.83 48 | 95.31 28 | 79.52 70 | 90.98 35 | 93.24 49 | 75.37 145 | 92.84 43 | 95.28 41 | 85.58 51 | 96.09 5 | 87.92 11 | 97.76 48 | 93.88 107 |
|
CP-MVS | | | 91.67 11 | 91.58 16 | 91.96 12 | 95.29 29 | 87.62 9 | 93.38 7 | 93.36 39 | 83.16 40 | 91.06 75 | 94.00 86 | 88.26 22 | 95.71 21 | 87.28 23 | 98.39 22 | 92.55 150 |
|
VDDNet | | | 84.35 136 | 85.39 113 | 81.25 205 | 95.13 30 | 59.32 256 | 85.42 124 | 81.11 253 | 86.41 22 | 87.41 144 | 96.21 21 | 73.61 181 | 90.61 196 | 66.33 218 | 96.85 73 | 93.81 113 |
|
CPTT-MVS | | | 89.39 52 | 88.98 58 | 90.63 37 | 95.09 31 | 86.95 10 | 92.09 23 | 92.30 80 | 79.74 77 | 87.50 143 | 92.38 126 | 81.42 95 | 93.28 121 | 83.07 65 | 97.24 64 | 91.67 176 |
|
ACMM | | 79.39 9 | 90.65 27 | 90.99 32 | 89.63 51 | 95.03 32 | 83.53 44 | 89.62 52 | 93.35 40 | 79.20 85 | 93.83 28 | 93.60 100 | 90.81 8 | 92.96 135 | 85.02 40 | 98.45 20 | 92.41 153 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
UA-Net | | | 91.49 14 | 91.53 17 | 91.39 22 | 94.98 33 | 82.95 50 | 93.52 5 | 92.79 67 | 88.22 15 | 88.53 127 | 97.64 2 | 83.45 65 | 94.55 60 | 86.02 35 | 98.60 14 | 96.67 36 |
|
HPM-MVS++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 88.93 58 | 88.45 65 | 90.38 41 | 94.92 34 | 85.85 25 | 89.70 48 | 91.27 120 | 78.20 100 | 86.69 155 | 92.28 131 | 80.36 104 | 95.06 46 | 86.17 33 | 96.49 86 | 90.22 210 |
|
XVG-ACMP-BASELINE | | | 89.98 39 | 89.84 46 | 90.41 40 | 94.91 35 | 84.50 41 | 89.49 57 | 93.98 25 | 79.68 78 | 92.09 58 | 93.89 94 | 83.80 61 | 93.10 129 | 82.67 72 | 98.04 35 | 93.64 117 |
|
OPM-MVS | | | 89.80 45 | 89.97 44 | 89.27 56 | 94.76 36 | 79.86 65 | 86.76 104 | 92.78 68 | 78.78 92 | 92.51 52 | 93.64 99 | 88.13 26 | 93.84 84 | 84.83 43 | 97.55 58 | 94.10 100 |
|
LPG-MVS_test | | | 91.47 16 | 91.68 14 | 90.82 34 | 94.75 37 | 81.69 52 | 90.00 41 | 94.27 13 | 82.35 49 | 93.67 30 | 94.82 56 | 91.18 5 | 95.52 28 | 85.36 36 | 98.73 8 | 95.23 74 |
|
LGP-MVS_train | | | | | 90.82 34 | 94.75 37 | 81.69 52 | | 94.27 13 | 82.35 49 | 93.67 30 | 94.82 56 | 91.18 5 | 95.52 28 | 85.36 36 | 98.73 8 | 95.23 74 |
|
abl_6 | | | 93.02 4 | 93.16 4 | 92.60 4 | 94.73 39 | 88.99 7 | 93.26 10 | 94.19 19 | 89.11 10 | 94.43 18 | 95.27 42 | 91.86 3 | 95.09 44 | 87.54 19 | 98.02 38 | 93.71 115 |
|
XVG-OURS-SEG-HR | | | 89.59 49 | 89.37 53 | 90.28 43 | 94.47 40 | 85.95 21 | 86.84 100 | 93.91 28 | 80.07 75 | 86.75 154 | 93.26 103 | 93.64 2 | 90.93 184 | 84.60 46 | 90.75 243 | 93.97 103 |
|
ACMP | | 79.16 10 | 90.54 30 | 90.60 38 | 90.35 42 | 94.36 41 | 80.98 58 | 89.16 61 | 94.05 23 | 79.03 89 | 92.87 41 | 93.74 98 | 90.60 11 | 95.21 42 | 82.87 68 | 98.76 5 | 94.87 79 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
XVG-OURS | | | 89.18 55 | 88.83 60 | 90.23 44 | 94.28 42 | 86.11 20 | 85.91 116 | 93.60 36 | 80.16 74 | 89.13 118 | 93.44 101 | 83.82 60 | 90.98 182 | 83.86 57 | 95.30 129 | 93.60 119 |
|
MIMVSNet1 | | | 83.63 156 | 84.59 133 | 80.74 211 | 94.06 43 | 62.77 210 | 82.72 190 | 84.53 235 | 77.57 108 | 90.34 83 | 95.92 26 | 76.88 141 | 85.83 278 | 61.88 248 | 97.42 60 | 93.62 118 |
|
TranMVSNet+NR-MVSNet | | | 87.86 70 | 88.76 63 | 85.18 118 | 94.02 44 | 64.13 188 | 84.38 139 | 91.29 119 | 84.88 28 | 92.06 59 | 93.84 95 | 86.45 44 | 93.73 85 | 73.22 165 | 98.66 12 | 97.69 11 |
|
新几何1 | | | | | 82.95 178 | 93.96 45 | 78.56 78 | | 80.24 257 | 55.45 309 | 83.93 202 | 91.08 165 | 71.19 215 | 88.33 242 | 65.84 223 | 93.07 190 | 81.95 315 |
|
1121 | | | 80.86 195 | 79.81 205 | 84.02 149 | 93.93 46 | 78.70 76 | 81.64 218 | 80.18 258 | 55.43 310 | 83.67 204 | 91.15 160 | 71.29 214 | 91.41 174 | 67.95 209 | 93.06 191 | 81.96 314 |
|
SteuartSystems-ACMMP | | | 91.16 22 | 91.36 21 | 90.55 38 | 93.91 47 | 80.97 59 | 91.49 30 | 93.48 38 | 82.82 45 | 92.60 51 | 93.97 87 | 88.19 23 | 96.29 4 | 87.61 16 | 98.20 32 | 94.39 93 |
Skip Steuart: Steuart Systems R&D Blog. |
test_part2 | | | | | | 93.86 48 | 77.77 82 | | | | 92.84 43 | | | | | | |
|
v1.0 | | | 38.20 343 | 50.94 342 | 0.00 359 | 93.86 48 | 0.00 374 | 0.00 365 | 93.93 26 | 84.39 30 | 92.84 43 | 93.43 102 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
LTVRE_ROB | | 86.10 1 | 93.04 3 | 93.44 2 | 91.82 19 | 93.73 50 | 85.72 28 | 96.79 1 | 95.51 4 | 88.86 12 | 95.63 10 | 96.99 8 | 84.81 54 | 93.16 126 | 91.10 1 | 97.53 59 | 96.58 39 |
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 |
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 83.01 3 | 91.97 8 | 91.95 8 | 92.04 10 | 93.68 51 | 86.15 18 | 93.37 8 | 95.10 7 | 90.28 8 | 92.11 57 | 95.03 49 | 89.75 14 | 94.93 49 | 79.95 106 | 98.27 28 | 95.04 78 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
DeepC-MVS | | 82.31 4 | 89.15 56 | 89.08 55 | 89.37 55 | 93.64 52 | 79.07 72 | 88.54 74 | 94.20 17 | 73.53 162 | 89.71 101 | 94.82 56 | 85.09 52 | 95.77 18 | 84.17 53 | 98.03 37 | 93.26 126 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
mvs_tets | | | 89.78 46 | 89.27 54 | 91.30 24 | 93.51 53 | 84.79 36 | 89.89 46 | 90.63 133 | 70.00 215 | 94.55 17 | 96.67 11 | 87.94 29 | 93.59 95 | 84.27 51 | 95.97 107 | 95.52 66 |
|
HQP_MVS | | | 87.75 73 | 87.43 78 | 88.70 63 | 93.45 54 | 76.42 99 | 89.45 58 | 93.61 34 | 79.44 82 | 86.55 157 | 92.95 112 | 74.84 159 | 95.22 40 | 80.78 92 | 95.83 114 | 94.46 89 |
|
plane_prior7 | | | | | | 93.45 54 | 77.31 89 | | | | | | | | | | |
|
WR-MVS | | | 83.56 158 | 84.40 139 | 81.06 208 | 93.43 56 | 54.88 287 | 78.67 268 | 85.02 231 | 81.24 62 | 90.74 81 | 91.56 149 | 72.85 196 | 91.08 180 | 68.00 207 | 98.04 35 | 97.23 24 |
|
ESAPD | | | 90.53 31 | 91.08 28 | 88.88 60 | 93.38 57 | 78.65 77 | 89.15 62 | 94.05 23 | 84.68 29 | 93.90 25 | 94.11 83 | 88.13 26 | 96.30 3 | 84.51 47 | 97.81 46 | 91.70 175 |
|
jajsoiax | | | 89.41 51 | 88.81 61 | 91.19 28 | 93.38 57 | 84.72 37 | 89.70 48 | 90.29 149 | 69.27 219 | 94.39 19 | 96.38 15 | 86.02 49 | 93.52 106 | 83.96 54 | 95.92 110 | 95.34 69 |
|
PS-MVSNAJss | | | 88.31 65 | 87.90 69 | 89.56 53 | 93.31 59 | 77.96 81 | 87.94 80 | 91.97 90 | 70.73 208 | 94.19 23 | 96.67 11 | 76.94 135 | 94.57 58 | 83.07 65 | 96.28 93 | 96.15 42 |
|
test222 | | | | | | 93.31 59 | 76.54 96 | 79.38 256 | 77.79 267 | 52.59 323 | 82.36 223 | 90.84 175 | 66.83 230 | | | 91.69 214 | 81.25 327 |
|
DU-MVS | | | 86.80 84 | 86.99 85 | 86.21 100 | 93.24 61 | 67.02 167 | 83.16 178 | 92.21 82 | 81.73 58 | 90.92 77 | 91.97 136 | 77.20 129 | 93.99 75 | 74.16 154 | 98.35 23 | 97.61 12 |
|
NR-MVSNet | | | 86.00 101 | 86.22 99 | 85.34 116 | 93.24 61 | 64.56 185 | 82.21 205 | 90.46 137 | 80.99 65 | 88.42 130 | 91.97 136 | 77.56 126 | 93.85 82 | 72.46 173 | 98.65 13 | 97.61 12 |
|
OurMVSNet-221017-0 | | | 90.01 38 | 89.74 48 | 90.83 33 | 93.16 63 | 80.37 60 | 91.91 27 | 93.11 52 | 81.10 64 | 95.32 12 | 97.24 5 | 72.94 195 | 94.85 51 | 85.07 38 | 97.78 47 | 97.26 22 |
|
pcd1.5k->3k | | | 38.83 342 | 41.11 344 | 32.01 353 | 93.13 64 | 0.00 374 | 0.00 365 | 91.38 117 | 0.00 369 | 0.00 371 | 0.00 371 | 89.24 15 | 0.00 371 | 0.00 368 | 96.24 96 | 96.02 48 |
|
UniMVSNet (Re) | | | 86.87 81 | 86.98 86 | 86.55 90 | 93.11 65 | 68.48 158 | 83.80 154 | 92.87 63 | 80.37 70 | 89.61 109 | 91.81 143 | 77.72 124 | 94.18 67 | 75.00 151 | 98.53 17 | 96.99 33 |
|
APD-MVS_3200maxsize | | | 92.05 7 | 92.24 6 | 91.48 20 | 93.02 66 | 85.17 31 | 92.47 21 | 95.05 8 | 87.65 18 | 93.21 36 | 94.39 73 | 90.09 13 | 95.08 45 | 86.67 26 | 97.60 57 | 94.18 97 |
|
ACMH+ | | 77.89 11 | 90.73 26 | 91.50 18 | 88.44 65 | 93.00 67 | 76.26 101 | 89.65 51 | 95.55 3 | 87.72 17 | 93.89 27 | 94.94 52 | 91.62 4 | 93.44 112 | 78.35 122 | 98.76 5 | 95.61 65 |
|
APDe-MVS | | | 91.22 20 | 91.92 9 | 89.14 58 | 92.97 68 | 78.04 80 | 92.84 12 | 94.14 21 | 83.33 38 | 93.90 25 | 95.73 29 | 88.77 17 | 96.41 1 | 87.60 17 | 97.98 42 | 92.98 133 |
|
114514_t | | | 83.10 166 | 82.54 170 | 84.77 125 | 92.90 69 | 69.10 156 | 86.65 109 | 90.62 134 | 54.66 313 | 81.46 236 | 90.81 176 | 76.98 134 | 94.38 61 | 72.62 172 | 96.18 97 | 90.82 196 |
|
testdata | | | | | 79.54 227 | 92.87 70 | 72.34 124 | | 80.14 259 | 59.91 290 | 85.47 176 | 91.75 145 | 67.96 226 | 85.24 282 | 68.57 205 | 92.18 211 | 81.06 332 |
|
CNVR-MVS | | | 87.81 72 | 87.68 74 | 88.21 68 | 92.87 70 | 77.30 90 | 85.25 125 | 91.23 121 | 77.31 116 | 87.07 150 | 91.47 151 | 82.94 70 | 94.71 54 | 84.67 45 | 96.27 95 | 92.62 149 |
|
UniMVSNet_NR-MVSNet | | | 86.84 83 | 87.06 83 | 86.17 103 | 92.86 72 | 67.02 167 | 82.55 194 | 91.56 100 | 83.08 42 | 90.92 77 | 91.82 142 | 78.25 120 | 93.99 75 | 74.16 154 | 98.35 23 | 97.49 15 |
|
plane_prior1 | | | | | | 92.83 73 | | | | | | | | | | | |
|
原ACMM1 | | | | | 84.60 131 | 92.81 74 | 74.01 112 | | 91.50 102 | 62.59 271 | 82.73 219 | 90.67 181 | 76.53 142 | 94.25 64 | 69.24 194 | 95.69 119 | 85.55 263 |
|
plane_prior6 | | | | | | 92.61 75 | 76.54 96 | | | | | | 74.84 159 | | | | |
|
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 89.54 50 | 89.63 50 | 89.26 57 | 92.57 76 | 81.34 57 | 90.19 40 | 93.08 54 | 80.87 67 | 91.13 74 | 93.19 104 | 86.22 47 | 95.97 8 | 82.23 76 | 97.18 66 | 90.45 206 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
test_0402 | | | 88.65 60 | 89.58 52 | 85.88 108 | 92.55 77 | 72.22 127 | 84.01 145 | 89.44 170 | 88.63 13 | 94.38 20 | 95.77 28 | 86.38 46 | 93.59 95 | 79.84 107 | 95.21 130 | 91.82 171 |
|
SixPastTwentyTwo | | | 87.20 77 | 87.45 77 | 86.45 92 | 92.52 78 | 69.19 155 | 87.84 84 | 88.05 188 | 81.66 59 | 94.64 16 | 96.53 14 | 65.94 234 | 94.75 53 | 83.02 67 | 96.83 75 | 95.41 68 |
|
ACMH | | 76.49 14 | 89.34 53 | 91.14 27 | 83.96 152 | 92.50 79 | 70.36 145 | 89.55 53 | 93.84 30 | 81.89 57 | 94.70 15 | 95.44 39 | 90.69 9 | 88.31 243 | 83.33 61 | 98.30 27 | 93.20 128 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
VPNet | | | 80.25 203 | 81.68 181 | 75.94 276 | 92.46 80 | 47.98 341 | 76.70 285 | 81.67 251 | 73.45 164 | 84.87 182 | 92.82 116 | 74.66 164 | 86.51 268 | 61.66 251 | 96.85 73 | 93.33 124 |
|
agg_prior3 | | | 85.76 107 | 84.95 120 | 88.16 69 | 92.43 81 | 79.92 63 | 83.98 146 | 90.03 157 | 65.11 257 | 83.66 205 | 90.64 184 | 81.00 98 | 93.67 87 | 81.21 84 | 96.54 83 | 90.88 193 |
|
F-COLMAP | | | 84.97 119 | 83.42 156 | 89.63 51 | 92.39 82 | 83.40 45 | 88.83 68 | 91.92 92 | 73.19 173 | 80.18 261 | 89.15 207 | 77.04 133 | 93.28 121 | 65.82 224 | 92.28 206 | 92.21 162 |
|
test_djsdf | | | 89.62 48 | 89.01 56 | 91.45 21 | 92.36 83 | 82.98 49 | 91.98 25 | 90.08 155 | 71.54 202 | 94.28 22 | 96.54 13 | 81.57 93 | 94.27 62 | 86.26 29 | 96.49 86 | 97.09 29 |
|
TEST9 | | | | | | 92.34 84 | 79.70 67 | 83.94 147 | 90.32 142 | 65.41 255 | 84.49 192 | 90.97 169 | 82.03 85 | 93.63 90 | | | |
|
train_agg | | | 85.98 104 | 85.28 114 | 88.07 71 | 92.34 84 | 79.70 67 | 83.94 147 | 90.32 142 | 65.79 248 | 84.49 192 | 90.97 169 | 81.93 87 | 93.63 90 | 81.21 84 | 96.54 83 | 90.88 193 |
|
NCCC | | | 87.36 74 | 86.87 89 | 88.83 61 | 92.32 86 | 78.84 75 | 86.58 111 | 91.09 125 | 78.77 93 | 84.85 183 | 90.89 173 | 80.85 99 | 95.29 36 | 81.14 86 | 95.32 126 | 92.34 156 |
|
FC-MVSNet-test | | | 85.93 105 | 87.05 84 | 82.58 184 | 92.25 87 | 56.44 276 | 85.75 119 | 93.09 53 | 77.33 115 | 91.94 63 | 94.65 61 | 74.78 161 | 93.41 115 | 75.11 150 | 98.58 15 | 97.88 9 |
|
CDPH-MVS | | | 86.17 100 | 85.54 110 | 88.05 74 | 92.25 87 | 75.45 104 | 83.85 151 | 92.01 88 | 65.91 247 | 86.19 164 | 91.75 145 | 83.77 62 | 94.98 48 | 77.43 133 | 96.71 78 | 93.73 114 |
|
pmmvs6 | | | 86.52 90 | 88.06 67 | 81.90 193 | 92.22 89 | 62.28 225 | 84.66 133 | 89.15 173 | 83.54 37 | 89.85 97 | 97.32 3 | 88.08 28 | 86.80 263 | 70.43 187 | 97.30 63 | 96.62 37 |
|
EG-PatchMatch MVS | | | 84.08 146 | 84.11 146 | 83.98 151 | 92.22 89 | 72.61 121 | 82.20 207 | 87.02 207 | 72.63 180 | 88.86 120 | 91.02 167 | 78.52 116 | 91.11 179 | 73.41 164 | 91.09 229 | 88.21 235 |
|
test_8 | | | | | | 92.09 91 | 78.87 74 | 83.82 152 | 90.31 144 | 65.79 248 | 84.36 195 | 90.96 171 | 81.93 87 | 93.44 112 | | | |
|
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 86.86 82 | 86.58 93 | 87.72 76 | 92.09 91 | 77.43 87 | 87.35 90 | 92.09 85 | 78.87 91 | 84.27 200 | 94.05 84 | 78.35 119 | 93.65 88 | 80.54 97 | 91.58 216 | 92.08 164 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
IS-MVSNet | | | 86.66 87 | 86.82 92 | 86.17 103 | 92.05 93 | 66.87 169 | 91.21 34 | 88.64 178 | 86.30 23 | 89.60 110 | 92.59 121 | 69.22 220 | 94.91 50 | 73.89 158 | 97.89 44 | 96.72 35 |
|
旧先验1 | | | | | | 91.97 94 | 71.77 133 | | 81.78 250 | | | 91.84 140 | 73.92 174 | | | 93.65 176 | 83.61 290 |
|
v7n | | | 90.13 34 | 90.96 33 | 87.65 78 | 91.95 95 | 71.06 141 | 89.99 43 | 93.05 55 | 86.53 21 | 94.29 21 | 96.27 19 | 82.69 72 | 94.08 73 | 86.25 31 | 97.63 54 | 97.82 10 |
|
NP-MVS | | | | | | 91.95 95 | 74.55 109 | | | | | 90.17 194 | | | | | |
|
OMC-MVS | | | 88.19 66 | 87.52 75 | 90.19 45 | 91.94 97 | 81.68 54 | 87.49 89 | 93.17 51 | 76.02 130 | 88.64 125 | 91.22 155 | 84.24 59 | 93.37 116 | 77.97 128 | 97.03 69 | 95.52 66 |
|
FIs | | | 85.35 112 | 86.27 98 | 82.60 183 | 91.86 98 | 57.31 268 | 85.10 127 | 93.05 55 | 75.83 135 | 91.02 76 | 93.97 87 | 73.57 182 | 92.91 139 | 73.97 157 | 98.02 38 | 97.58 14 |
|
MSLP-MVS++ | | | 85.00 118 | 86.03 103 | 81.90 193 | 91.84 99 | 71.56 139 | 86.75 105 | 93.02 59 | 75.95 133 | 87.12 147 | 89.39 204 | 77.98 121 | 89.40 221 | 77.46 131 | 94.78 148 | 84.75 276 |
|
DP-MVS Recon | | | 84.05 147 | 83.22 159 | 86.52 91 | 91.73 100 | 75.27 105 | 83.23 177 | 92.40 77 | 72.04 190 | 82.04 227 | 88.33 221 | 77.91 123 | 93.95 80 | 66.17 219 | 95.12 135 | 90.34 209 |
|
SD-MVS | | | 88.96 57 | 89.88 45 | 86.22 98 | 91.63 101 | 77.07 91 | 89.82 47 | 93.77 31 | 78.90 90 | 92.88 40 | 92.29 130 | 86.11 48 | 90.22 205 | 86.24 32 | 97.24 64 | 91.36 183 |
|
AllTest | | | 87.97 69 | 87.40 79 | 89.68 49 | 91.59 102 | 83.40 45 | 89.50 56 | 95.44 5 | 79.47 80 | 88.00 136 | 93.03 108 | 82.66 73 | 91.47 170 | 70.81 180 | 96.14 100 | 94.16 98 |
|
TestCases | | | | | 89.68 49 | 91.59 102 | 83.40 45 | | 95.44 5 | 79.47 80 | 88.00 136 | 93.03 108 | 82.66 73 | 91.47 170 | 70.81 180 | 96.14 100 | 94.16 98 |
|
MCST-MVS | | | 84.36 134 | 83.93 151 | 85.63 112 | 91.59 102 | 71.58 138 | 83.52 164 | 92.13 84 | 61.82 277 | 83.96 201 | 89.75 199 | 79.93 109 | 93.46 111 | 78.33 123 | 94.34 159 | 91.87 170 |
|
agg_prior1 | | | 85.72 108 | 85.20 115 | 87.28 84 | 91.58 105 | 77.69 83 | 83.69 158 | 90.30 146 | 66.29 243 | 84.32 196 | 91.07 166 | 82.13 81 | 93.18 124 | 81.02 87 | 96.36 90 | 90.98 188 |
|
agg_prior | | | | | | 91.58 105 | 77.69 83 | | 90.30 146 | | 84.32 196 | | | 93.18 124 | | | |
|
PVSNet_Blended_VisFu | | | 81.55 185 | 80.49 197 | 84.70 129 | 91.58 105 | 73.24 117 | 84.21 140 | 91.67 98 | 62.86 269 | 80.94 242 | 87.16 238 | 67.27 228 | 92.87 140 | 69.82 190 | 88.94 263 | 87.99 239 |
|
EPP-MVSNet | | | 85.47 111 | 85.04 117 | 86.77 87 | 91.52 108 | 69.37 150 | 91.63 29 | 87.98 191 | 81.51 61 | 87.05 151 | 91.83 141 | 66.18 233 | 95.29 36 | 70.75 182 | 96.89 72 | 95.64 60 |
|
DeepPCF-MVS | | 81.24 5 | 87.28 76 | 86.21 100 | 90.49 39 | 91.48 109 | 84.90 34 | 83.41 169 | 92.38 79 | 70.25 213 | 89.35 115 | 90.68 180 | 82.85 71 | 94.57 58 | 79.55 110 | 95.95 108 | 92.00 165 |
|
Baseline_NR-MVSNet | | | 84.00 149 | 85.90 104 | 78.29 243 | 91.47 110 | 53.44 296 | 82.29 201 | 87.00 208 | 79.06 88 | 89.55 111 | 95.72 30 | 77.20 129 | 86.14 274 | 72.30 174 | 98.51 18 | 95.28 72 |
|
HyFIR lowres test | | | 75.12 249 | 72.66 271 | 82.50 187 | 91.44 111 | 65.19 180 | 72.47 315 | 87.31 197 | 46.79 349 | 80.29 259 | 84.30 280 | 52.70 291 | 92.10 156 | 51.88 317 | 86.73 286 | 90.22 210 |
|
DP-MVS | | | 88.60 62 | 89.01 56 | 87.36 83 | 91.30 112 | 77.50 86 | 87.55 87 | 92.97 61 | 87.95 16 | 89.62 107 | 92.87 115 | 84.56 56 | 93.89 81 | 77.65 129 | 96.62 80 | 90.70 198 |
|
DeepC-MVS_fast | | 80.27 8 | 86.23 97 | 85.65 109 | 87.96 75 | 91.30 112 | 76.92 92 | 87.19 93 | 91.99 89 | 70.56 209 | 84.96 179 | 90.69 179 | 80.01 107 | 95.14 43 | 78.37 121 | 95.78 116 | 91.82 171 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
3Dnovator+ | | 83.92 2 | 89.97 41 | 89.66 49 | 90.92 32 | 91.27 114 | 81.66 55 | 91.25 33 | 94.13 22 | 88.89 11 | 88.83 122 | 94.26 76 | 77.55 127 | 95.86 14 | 84.88 41 | 95.87 112 | 95.24 73 |
|
HQP-NCC | | | | | | 91.19 115 | | 84.77 129 | | 73.30 169 | 80.55 256 | | | | | | |
|
ACMP_Plane | | | | | | 91.19 115 | | 84.77 129 | | 73.30 169 | 80.55 256 | | | | | | |
|
HQP-MVS | | | 84.61 125 | 84.06 147 | 86.27 96 | 91.19 115 | 70.66 143 | 84.77 129 | 92.68 70 | 73.30 169 | 80.55 256 | 90.17 194 | 72.10 206 | 94.61 56 | 77.30 134 | 94.47 157 | 93.56 121 |
|
VDD-MVS | | | 84.23 140 | 84.58 134 | 83.20 174 | 91.17 118 | 65.16 181 | 83.25 175 | 84.97 233 | 79.79 76 | 87.18 146 | 94.27 75 | 74.77 162 | 90.89 187 | 69.24 194 | 96.54 83 | 93.55 123 |
|
K. test v3 | | | 85.14 113 | 84.73 123 | 86.37 93 | 91.13 119 | 69.63 149 | 85.45 123 | 76.68 275 | 84.06 35 | 92.44 54 | 96.99 8 | 62.03 246 | 94.65 55 | 80.58 96 | 93.24 188 | 94.83 82 |
|
lessismore_v0 | | | | | 85.95 105 | 91.10 120 | 70.99 142 | | 70.91 322 | | 91.79 64 | 94.42 69 | 61.76 247 | 92.93 137 | 79.52 113 | 93.03 192 | 93.93 105 |
|
TransMVSNet (Re) | | | 84.02 148 | 85.74 106 | 78.85 233 | 91.00 121 | 55.20 286 | 82.29 201 | 87.26 198 | 79.65 79 | 88.38 132 | 95.52 37 | 83.00 69 | 86.88 255 | 67.97 208 | 96.60 81 | 94.45 91 |
|
wuykxyi23d | | | 88.46 63 | 88.80 62 | 87.44 82 | 90.96 122 | 93.03 1 | 85.85 118 | 81.96 247 | 74.58 151 | 98.58 2 | 97.29 4 | 87.73 31 | 87.31 250 | 82.84 70 | 99.41 1 | 81.99 313 |
|
PAPM_NR | | | 83.23 163 | 83.19 161 | 83.33 172 | 90.90 123 | 65.98 175 | 88.19 77 | 90.78 129 | 78.13 102 | 80.87 244 | 87.92 229 | 73.49 184 | 92.42 148 | 70.07 188 | 88.40 267 | 91.60 178 |
|
CSCG | | | 86.26 95 | 86.47 95 | 85.60 113 | 90.87 124 | 74.26 111 | 87.98 79 | 91.85 93 | 80.35 71 | 89.54 113 | 88.01 225 | 79.09 112 | 92.13 153 | 75.51 146 | 95.06 137 | 90.41 207 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 73.85 16 | 82.09 179 | 80.31 198 | 87.45 81 | 90.86 125 | 80.29 61 | 85.88 117 | 90.65 132 | 68.17 229 | 76.32 287 | 86.33 251 | 73.12 194 | 92.61 146 | 61.40 254 | 90.02 253 | 89.44 219 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
test12 | | | | | 86.57 89 | 90.74 126 | 72.63 120 | | 90.69 131 | | 82.76 218 | | 79.20 111 | 94.80 52 | | 95.32 126 | 92.27 159 |
|
ITE_SJBPF | | | | | 90.11 46 | 90.72 127 | 84.97 33 | | 90.30 146 | 81.56 60 | 90.02 88 | 91.20 158 | 82.40 77 | 90.81 189 | 73.58 161 | 94.66 153 | 94.56 84 |
|
TAMVS | | | 78.08 218 | 76.36 231 | 83.23 173 | 90.62 128 | 72.87 118 | 79.08 263 | 80.01 260 | 61.72 279 | 81.35 238 | 86.92 241 | 63.96 240 | 88.78 236 | 50.61 318 | 93.01 193 | 88.04 238 |
|
test_prior3 | | | 86.31 94 | 86.31 97 | 86.32 94 | 90.59 129 | 71.99 131 | 83.37 170 | 92.85 64 | 75.43 142 | 84.58 190 | 91.57 147 | 81.92 89 | 94.17 69 | 79.54 111 | 96.97 70 | 92.80 138 |
|
test_prior | | | | | 86.32 94 | 90.59 129 | 71.99 131 | | 92.85 64 | | | | | 94.17 69 | | | 92.80 138 |
|
ambc | | | | | 82.98 177 | 90.55 131 | 64.86 182 | 88.20 76 | 89.15 173 | | 89.40 114 | 93.96 89 | 71.67 213 | 91.38 176 | 78.83 119 | 96.55 82 | 92.71 141 |
|
Anonymous20231211 | | | 88.40 64 | 89.62 51 | 84.73 127 | 90.46 132 | 65.27 179 | 88.86 67 | 93.02 59 | 87.15 19 | 93.05 38 | 97.10 6 | 82.28 79 | 92.02 157 | 76.70 140 | 97.99 40 | 96.88 34 |
|
Test_1112_low_res | | | 73.90 262 | 73.08 267 | 76.35 270 | 90.35 133 | 55.95 277 | 73.40 313 | 86.17 214 | 50.70 339 | 73.14 310 | 85.94 256 | 58.31 266 | 85.90 277 | 56.51 289 | 83.22 315 | 87.20 248 |
|
VPA-MVSNet | | | 83.47 161 | 84.73 123 | 79.69 224 | 90.29 134 | 57.52 267 | 81.30 225 | 88.69 177 | 76.29 126 | 87.58 141 | 94.44 68 | 80.60 102 | 87.20 251 | 66.60 217 | 96.82 76 | 94.34 94 |
|
FMVSNet1 | | | 84.55 127 | 85.45 112 | 81.85 196 | 90.27 135 | 61.05 240 | 86.83 101 | 88.27 185 | 78.57 96 | 89.66 103 | 95.64 33 | 75.43 150 | 90.68 193 | 69.09 198 | 95.33 125 | 93.82 110 |
|
Anonymous20240529 | | | 86.20 99 | 87.13 81 | 83.42 171 | 90.19 136 | 64.55 186 | 84.55 136 | 90.71 130 | 85.85 24 | 89.94 92 | 95.24 44 | 82.13 81 | 90.40 200 | 69.19 197 | 96.40 89 | 95.31 71 |
|
MVS_111021_HR | | | 84.63 124 | 84.34 144 | 85.49 115 | 90.18 137 | 75.86 103 | 79.23 262 | 87.13 203 | 73.35 166 | 85.56 174 | 89.34 205 | 83.60 64 | 90.50 198 | 76.64 141 | 94.05 164 | 90.09 215 |
|
RPSCF | | | 88.00 68 | 86.93 88 | 91.22 27 | 90.08 138 | 89.30 6 | 89.68 50 | 91.11 124 | 79.26 84 | 89.68 102 | 94.81 59 | 82.44 76 | 87.74 247 | 76.54 142 | 88.74 266 | 96.61 38 |
|
nrg030 | | | 87.85 71 | 88.49 64 | 85.91 106 | 90.07 139 | 69.73 147 | 87.86 81 | 94.20 17 | 74.04 156 | 92.70 49 | 94.66 60 | 85.88 50 | 91.50 169 | 79.72 108 | 97.32 62 | 96.50 40 |
|
v748 | | | 88.91 59 | 89.82 47 | 86.19 102 | 90.06 140 | 68.53 157 | 88.81 69 | 91.48 104 | 84.36 31 | 94.19 23 | 95.98 25 | 82.52 75 | 92.67 144 | 84.30 50 | 96.67 79 | 97.37 19 |
|
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 83.66 155 | 83.69 153 | 83.57 165 | 90.05 141 | 72.26 126 | 86.29 115 | 90.00 158 | 78.19 101 | 81.65 234 | 87.16 238 | 83.40 66 | 94.24 65 | 61.69 250 | 94.76 151 | 84.21 283 |
|
pm-mvs1 | | | 83.69 154 | 84.95 120 | 79.91 220 | 90.04 142 | 59.66 253 | 82.43 196 | 87.44 195 | 75.52 141 | 87.85 138 | 95.26 43 | 81.25 97 | 85.65 280 | 68.74 202 | 96.04 105 | 94.42 92 |
|
CHOSEN 1792x2688 | | | 72.45 277 | 70.56 287 | 78.13 245 | 90.02 143 | 63.08 204 | 68.72 328 | 83.16 238 | 42.99 358 | 75.92 292 | 85.46 262 | 57.22 276 | 85.18 284 | 49.87 322 | 81.67 324 | 86.14 257 |
|
anonymousdsp | | | 89.73 47 | 88.88 59 | 92.27 7 | 89.82 144 | 86.67 12 | 90.51 38 | 90.20 154 | 69.87 216 | 95.06 13 | 96.14 23 | 84.28 58 | 93.07 134 | 87.68 14 | 96.34 91 | 97.09 29 |
|
1112_ss | | | 74.82 254 | 73.74 254 | 78.04 247 | 89.57 145 | 60.04 249 | 76.49 289 | 87.09 206 | 54.31 314 | 73.66 309 | 79.80 328 | 60.25 254 | 86.76 265 | 58.37 279 | 84.15 311 | 87.32 247 |
|
PCF-MVS | | 74.62 15 | 82.15 178 | 80.92 193 | 85.84 109 | 89.43 146 | 72.30 125 | 80.53 235 | 91.82 94 | 57.36 301 | 87.81 139 | 89.92 197 | 77.67 125 | 93.63 90 | 58.69 274 | 95.08 136 | 91.58 179 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVP-Stereo | | | 75.81 244 | 73.51 264 | 82.71 182 | 89.35 147 | 73.62 113 | 80.06 238 | 85.20 226 | 60.30 288 | 73.96 307 | 87.94 227 | 57.89 271 | 89.45 219 | 52.02 312 | 74.87 345 | 85.06 269 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
CNLPA | | | 83.55 159 | 83.10 162 | 84.90 121 | 89.34 148 | 83.87 43 | 84.54 137 | 88.77 175 | 79.09 87 | 83.54 208 | 88.66 214 | 74.87 158 | 81.73 306 | 66.84 215 | 92.29 205 | 89.11 225 |
|
TSAR-MVS + GP. | | | 83.95 150 | 82.69 166 | 87.72 76 | 89.27 149 | 81.45 56 | 83.72 157 | 81.58 252 | 74.73 149 | 85.66 171 | 86.06 255 | 72.56 204 | 92.69 143 | 75.44 148 | 95.21 130 | 89.01 231 |
|
MVS_111021_LR | | | 84.28 138 | 83.76 152 | 85.83 110 | 89.23 150 | 83.07 48 | 80.99 231 | 83.56 237 | 72.71 179 | 86.07 165 | 89.07 208 | 81.75 92 | 86.19 273 | 77.11 136 | 93.36 181 | 88.24 234 |
|
LFMVS | | | 80.15 206 | 80.56 195 | 78.89 232 | 89.19 151 | 55.93 278 | 85.22 126 | 73.78 294 | 82.96 43 | 84.28 199 | 92.72 120 | 57.38 274 | 90.07 212 | 63.80 235 | 95.75 117 | 90.68 199 |
|
CLD-MVS | | | 83.18 164 | 82.64 167 | 84.79 124 | 89.05 152 | 67.82 164 | 77.93 275 | 92.52 75 | 68.33 228 | 85.07 178 | 81.54 318 | 82.06 83 | 92.96 135 | 69.35 193 | 97.91 43 | 93.57 120 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
LS3D | | | 90.60 29 | 90.34 42 | 91.38 23 | 89.03 153 | 84.23 42 | 93.58 4 | 94.68 10 | 90.65 6 | 90.33 84 | 93.95 92 | 84.50 57 | 95.37 34 | 80.87 90 | 95.50 122 | 94.53 88 |
|
CDS-MVSNet | | | 77.32 224 | 75.40 240 | 83.06 176 | 89.00 154 | 72.48 123 | 77.90 276 | 82.17 246 | 60.81 285 | 78.94 270 | 83.49 288 | 59.30 261 | 88.76 237 | 54.64 304 | 92.37 204 | 87.93 241 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tttt0517 | | | 81.07 192 | 79.58 206 | 85.52 114 | 88.99 155 | 66.45 173 | 87.03 97 | 75.51 282 | 73.76 160 | 88.32 134 | 90.20 191 | 37.96 354 | 94.16 72 | 79.36 116 | 95.13 133 | 95.93 52 |
|
tfpnnormal | | | 81.79 183 | 82.95 163 | 78.31 242 | 88.93 156 | 55.40 282 | 80.83 234 | 82.85 241 | 76.81 122 | 85.90 169 | 94.14 81 | 74.58 166 | 86.51 268 | 66.82 216 | 95.68 120 | 93.01 132 |
|
v13 | | | 87.31 75 | 88.10 66 | 84.94 120 | 88.84 157 | 63.75 192 | 87.85 83 | 91.47 107 | 79.12 86 | 93.72 29 | 95.82 27 | 75.20 153 | 93.58 98 | 84.76 44 | 96.16 98 | 97.48 16 |
|
Vis-MVSNet (Re-imp) | | | 77.82 219 | 77.79 217 | 77.92 249 | 88.82 158 | 51.29 316 | 83.28 173 | 71.97 310 | 74.04 156 | 82.23 224 | 89.78 198 | 57.38 274 | 89.41 220 | 57.22 286 | 95.41 123 | 93.05 131 |
|
TAPA-MVS | | 77.73 12 | 85.71 109 | 84.83 122 | 88.37 66 | 88.78 159 | 79.72 66 | 87.15 95 | 93.50 37 | 69.17 221 | 85.80 170 | 89.56 202 | 80.76 100 | 92.13 153 | 73.21 169 | 95.51 121 | 93.25 127 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
v12 | | | 87.15 78 | 87.91 68 | 84.84 122 | 88.69 160 | 63.52 195 | 87.58 86 | 91.46 108 | 78.74 94 | 93.57 32 | 95.66 31 | 74.94 157 | 93.57 99 | 84.50 48 | 96.08 103 | 97.43 17 |
|
v52 | | | 89.97 41 | 90.60 38 | 88.07 71 | 88.69 160 | 72.01 129 | 91.35 31 | 92.64 72 | 82.22 51 | 95.97 8 | 96.31 16 | 84.82 53 | 93.98 77 | 88.59 4 | 94.83 146 | 98.23 7 |
|
V4 | | | 89.97 41 | 90.60 38 | 88.07 71 | 88.69 160 | 72.01 129 | 91.35 31 | 92.64 72 | 82.22 51 | 95.98 7 | 96.31 16 | 84.80 55 | 93.98 77 | 88.59 4 | 94.83 146 | 98.23 7 |
|
FPMVS | | | 72.29 280 | 72.00 278 | 73.14 296 | 88.63 163 | 85.00 32 | 74.65 304 | 67.39 340 | 71.94 198 | 77.80 278 | 87.66 232 | 50.48 296 | 75.83 323 | 49.95 320 | 79.51 332 | 58.58 360 |
|
BH-untuned | | | 80.96 194 | 80.99 191 | 80.84 210 | 88.55 164 | 68.23 159 | 80.33 237 | 88.46 179 | 72.79 178 | 86.55 157 | 86.76 242 | 74.72 163 | 91.77 165 | 61.79 249 | 88.99 262 | 82.52 306 |
|
V9 | | | 86.96 79 | 87.70 73 | 84.74 126 | 88.52 165 | 63.27 201 | 87.31 91 | 91.45 110 | 78.28 99 | 93.43 33 | 95.45 38 | 74.59 165 | 93.57 99 | 84.23 52 | 96.01 106 | 97.38 18 |
|
v11 | | | 86.96 79 | 87.78 72 | 84.51 132 | 88.50 166 | 62.60 215 | 87.21 92 | 91.63 99 | 78.08 103 | 93.40 34 | 95.56 36 | 75.07 154 | 93.57 99 | 84.46 49 | 96.08 103 | 97.36 20 |
|
Anonymous202405211 | | | 80.51 200 | 81.19 188 | 78.49 240 | 88.48 167 | 57.26 269 | 76.63 286 | 82.49 243 | 81.21 63 | 84.30 198 | 92.24 133 | 67.99 225 | 86.24 272 | 62.22 244 | 95.13 133 | 91.98 168 |
|
ab-mvs | | | 79.67 208 | 80.56 195 | 76.99 261 | 88.48 167 | 56.93 271 | 84.70 132 | 86.06 215 | 68.95 225 | 80.78 245 | 93.08 106 | 75.30 152 | 84.62 289 | 56.78 288 | 90.90 239 | 89.43 220 |
|
PHI-MVS | | | 86.38 92 | 85.81 105 | 88.08 70 | 88.44 169 | 77.34 88 | 89.35 60 | 93.05 55 | 73.15 174 | 84.76 184 | 87.70 231 | 78.87 114 | 94.18 67 | 80.67 95 | 96.29 92 | 92.73 140 |
|
V14 | | | 86.75 85 | 87.46 76 | 84.62 130 | 88.35 170 | 63.00 206 | 87.02 98 | 91.42 113 | 77.78 105 | 93.27 35 | 95.23 45 | 74.22 168 | 93.56 103 | 83.95 55 | 95.93 109 | 97.31 21 |
|
xiu_mvs_v1_base_debu | | | 80.84 196 | 80.14 201 | 82.93 179 | 88.31 171 | 71.73 134 | 79.53 246 | 87.17 200 | 65.43 252 | 79.59 263 | 82.73 301 | 76.94 135 | 90.14 208 | 73.22 165 | 88.33 268 | 86.90 251 |
|
xiu_mvs_v1_base | | | 80.84 196 | 80.14 201 | 82.93 179 | 88.31 171 | 71.73 134 | 79.53 246 | 87.17 200 | 65.43 252 | 79.59 263 | 82.73 301 | 76.94 135 | 90.14 208 | 73.22 165 | 88.33 268 | 86.90 251 |
|
xiu_mvs_v1_base_debi | | | 80.84 196 | 80.14 201 | 82.93 179 | 88.31 171 | 71.73 134 | 79.53 246 | 87.17 200 | 65.43 252 | 79.59 263 | 82.73 301 | 76.94 135 | 90.14 208 | 73.22 165 | 88.33 268 | 86.90 251 |
|
MG-MVS | | | 80.32 202 | 80.94 192 | 78.47 241 | 88.18 174 | 52.62 303 | 82.29 201 | 85.01 232 | 72.01 191 | 79.24 268 | 92.54 124 | 69.36 219 | 93.36 117 | 70.65 184 | 89.19 261 | 89.45 218 |
|
PM-MVS | | | 80.20 205 | 79.00 208 | 83.78 156 | 88.17 175 | 86.66 13 | 81.31 223 | 66.81 346 | 69.64 217 | 88.33 133 | 90.19 192 | 64.58 237 | 83.63 298 | 71.99 178 | 90.03 252 | 81.06 332 |
|
v10 | | | 86.54 89 | 87.10 82 | 84.84 122 | 88.16 176 | 63.28 200 | 86.64 110 | 92.20 83 | 75.42 144 | 92.81 46 | 94.50 66 | 74.05 171 | 94.06 74 | 83.88 56 | 96.28 93 | 97.17 27 |
|
v15 | | | 86.56 88 | 87.25 80 | 84.51 132 | 88.15 177 | 62.72 211 | 86.72 108 | 91.40 115 | 77.38 110 | 93.11 37 | 95.00 50 | 73.93 173 | 93.55 104 | 83.67 59 | 95.86 113 | 97.26 22 |
|
v7 | | | 84.81 121 | 85.00 118 | 84.23 144 | 88.15 177 | 63.27 201 | 83.79 155 | 91.39 116 | 71.10 206 | 90.07 86 | 91.28 153 | 74.04 172 | 93.63 90 | 81.48 83 | 93.67 175 | 95.79 53 |
|
casdiffmvs1 | | | 83.63 156 | 83.44 155 | 84.20 147 | 88.08 179 | 66.53 172 | 88.62 73 | 92.02 87 | 58.66 293 | 82.94 216 | 93.84 95 | 78.76 115 | 93.10 129 | 76.73 139 | 91.29 226 | 92.96 134 |
|
v17 | | | 86.32 93 | 86.95 87 | 84.44 136 | 88.00 180 | 62.62 214 | 86.74 106 | 91.48 104 | 77.17 118 | 92.74 47 | 94.56 62 | 73.74 177 | 93.53 105 | 83.27 62 | 94.87 145 | 97.18 26 |
|
canonicalmvs | | | 85.50 110 | 86.14 101 | 83.58 164 | 87.97 181 | 67.13 166 | 87.55 87 | 94.32 12 | 73.44 165 | 88.47 129 | 87.54 234 | 86.45 44 | 91.06 181 | 75.76 145 | 93.76 172 | 92.54 151 |
|
v16 | | | 86.24 96 | 86.85 90 | 84.43 137 | 87.96 182 | 62.59 216 | 86.73 107 | 91.48 104 | 77.17 118 | 92.67 50 | 94.55 63 | 73.63 178 | 93.52 106 | 83.26 63 | 94.16 161 | 97.17 27 |
|
VNet | | | 79.31 209 | 80.27 199 | 76.44 269 | 87.92 183 | 53.95 291 | 75.58 297 | 84.35 236 | 74.39 154 | 82.23 224 | 90.72 178 | 72.84 197 | 84.39 291 | 60.38 261 | 93.98 166 | 90.97 189 |
|
view600 | | | 76.79 229 | 76.54 226 | 77.56 253 | 87.91 184 | 50.77 322 | 81.92 211 | 71.35 318 | 77.38 110 | 84.62 186 | 88.40 217 | 45.18 325 | 89.26 223 | 58.58 275 | 93.49 177 | 92.66 143 |
|
view800 | | | 76.79 229 | 76.54 226 | 77.56 253 | 87.91 184 | 50.77 322 | 81.92 211 | 71.35 318 | 77.38 110 | 84.62 186 | 88.40 217 | 45.18 325 | 89.26 223 | 58.58 275 | 93.49 177 | 92.66 143 |
|
conf0.05thres1000 | | | 76.79 229 | 76.54 226 | 77.56 253 | 87.91 184 | 50.77 322 | 81.92 211 | 71.35 318 | 77.38 110 | 84.62 186 | 88.40 217 | 45.18 325 | 89.26 223 | 58.58 275 | 93.49 177 | 92.66 143 |
|
tfpn | | | 76.79 229 | 76.54 226 | 77.56 253 | 87.91 184 | 50.77 322 | 81.92 211 | 71.35 318 | 77.38 110 | 84.62 186 | 88.40 217 | 45.18 325 | 89.26 223 | 58.58 275 | 93.49 177 | 92.66 143 |
|
v8 | | | 86.22 98 | 86.83 91 | 84.36 140 | 87.82 188 | 62.35 220 | 86.42 113 | 91.33 118 | 76.78 123 | 92.73 48 | 94.48 67 | 73.41 185 | 93.72 86 | 83.10 64 | 95.41 123 | 97.01 32 |
|
v1neww | | | 84.43 131 | 84.66 129 | 83.75 157 | 87.81 189 | 62.34 221 | 83.59 160 | 90.27 150 | 72.33 185 | 89.93 94 | 91.22 155 | 73.28 189 | 93.29 118 | 80.25 102 | 93.25 186 | 95.62 61 |
|
v7new | | | 84.43 131 | 84.66 129 | 83.75 157 | 87.81 189 | 62.34 221 | 83.59 160 | 90.27 150 | 72.33 185 | 89.93 94 | 91.22 155 | 73.28 189 | 93.29 118 | 80.25 102 | 93.25 186 | 95.62 61 |
|
v6 | | | 84.43 131 | 84.66 129 | 83.75 157 | 87.81 189 | 62.34 221 | 83.59 160 | 90.26 152 | 72.33 185 | 89.94 92 | 91.19 159 | 73.30 188 | 93.29 118 | 80.26 101 | 93.26 185 | 95.62 61 |
|
alignmvs | | | 83.94 151 | 83.98 149 | 83.80 154 | 87.80 192 | 67.88 163 | 84.54 137 | 91.42 113 | 73.27 172 | 88.41 131 | 87.96 226 | 72.33 205 | 90.83 188 | 76.02 144 | 94.11 162 | 92.69 142 |
|
v1192 | | | 84.57 126 | 84.69 127 | 84.21 145 | 87.75 193 | 62.88 208 | 83.02 180 | 91.43 111 | 69.08 223 | 89.98 91 | 90.89 173 | 72.70 200 | 93.62 94 | 82.41 73 | 94.97 140 | 96.13 43 |
|
v18 | | | 85.99 103 | 86.55 94 | 84.30 142 | 87.73 194 | 62.29 224 | 86.40 114 | 91.49 103 | 76.64 124 | 92.40 55 | 94.20 79 | 73.28 189 | 93.52 106 | 82.87 68 | 93.99 165 | 97.09 29 |
|
PatchMatch-RL | | | 74.48 256 | 73.22 266 | 78.27 244 | 87.70 195 | 85.26 30 | 75.92 293 | 70.09 325 | 64.34 261 | 76.09 290 | 81.25 320 | 65.87 235 | 78.07 316 | 53.86 306 | 83.82 312 | 71.48 348 |
|
v1144 | | | 84.54 129 | 84.72 125 | 84.00 150 | 87.67 196 | 62.55 217 | 82.97 182 | 90.93 128 | 70.32 212 | 89.80 99 | 90.99 168 | 73.50 183 | 93.48 110 | 81.69 82 | 94.65 154 | 95.97 49 |
|
v1240 | | | 84.30 137 | 84.51 136 | 83.65 162 | 87.65 197 | 61.26 237 | 82.85 185 | 91.54 101 | 67.94 233 | 90.68 82 | 90.65 182 | 71.71 212 | 93.64 89 | 82.84 70 | 94.78 148 | 96.07 45 |
|
v1921920 | | | 84.23 140 | 84.37 143 | 83.79 155 | 87.64 198 | 61.71 228 | 82.91 184 | 91.20 122 | 67.94 233 | 90.06 87 | 90.34 187 | 72.04 209 | 93.59 95 | 82.32 75 | 94.91 141 | 96.07 45 |
|
v144192 | | | 84.24 139 | 84.41 138 | 83.71 161 | 87.59 199 | 61.57 233 | 82.95 183 | 91.03 126 | 67.82 236 | 89.80 99 | 90.49 185 | 73.28 189 | 93.51 109 | 81.88 80 | 94.89 142 | 96.04 47 |
|
Fast-Effi-MVS+ | | | 81.04 193 | 80.57 194 | 82.46 188 | 87.50 200 | 63.22 203 | 78.37 271 | 89.63 166 | 68.01 230 | 81.87 229 | 82.08 313 | 82.31 78 | 92.65 145 | 67.10 211 | 88.30 272 | 91.51 181 |
|
pmmvs-eth3d | | | 78.42 216 | 77.04 222 | 82.57 186 | 87.44 201 | 74.41 110 | 80.86 233 | 79.67 261 | 55.68 308 | 84.69 185 | 90.31 190 | 60.91 250 | 85.42 281 | 62.20 245 | 91.59 215 | 87.88 242 |
|
MVS_0304 | | | 84.88 120 | 83.96 150 | 87.64 79 | 87.43 202 | 74.83 107 | 84.18 141 | 93.30 44 | 77.48 109 | 77.39 281 | 88.46 216 | 74.53 167 | 95.74 19 | 78.09 127 | 94.75 152 | 92.36 155 |
|
IterMVS-LS | | | 84.73 123 | 84.98 119 | 83.96 152 | 87.35 203 | 63.66 193 | 83.25 175 | 89.88 161 | 76.06 128 | 89.62 107 | 92.37 129 | 73.40 187 | 92.52 147 | 78.16 125 | 94.77 150 | 95.69 58 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v1 | | | 84.16 142 | 84.38 140 | 83.52 167 | 87.33 204 | 61.71 228 | 82.79 187 | 89.73 164 | 71.89 201 | 89.64 104 | 91.11 164 | 72.72 198 | 93.10 129 | 80.40 98 | 93.79 171 | 95.75 55 |
|
v1141 | | | 84.16 142 | 84.38 140 | 83.52 167 | 87.32 205 | 61.70 230 | 82.79 187 | 89.74 162 | 71.90 199 | 89.64 104 | 91.12 162 | 72.68 201 | 93.10 129 | 80.39 100 | 93.80 170 | 95.75 55 |
|
divwei89l23v2f112 | | | 84.16 142 | 84.38 140 | 83.52 167 | 87.32 205 | 61.70 230 | 82.79 187 | 89.74 162 | 71.90 199 | 89.64 104 | 91.12 162 | 72.68 201 | 93.10 129 | 80.40 98 | 93.81 169 | 95.75 55 |
|
tfpn111 | | | 76.03 241 | 75.53 239 | 77.53 257 | 87.27 207 | 51.88 308 | 81.07 228 | 73.26 299 | 75.68 137 | 83.25 210 | 86.37 248 | 45.54 315 | 89.38 222 | 55.07 300 | 92.26 207 | 91.34 184 |
|
conf200view11 | | | 75.62 245 | 75.05 243 | 77.34 259 | 87.27 207 | 51.88 308 | 81.07 228 | 73.26 299 | 75.68 137 | 83.25 210 | 86.37 248 | 45.54 315 | 88.80 231 | 51.98 313 | 90.99 233 | 91.34 184 |
|
thres100view900 | | | 75.45 246 | 75.05 243 | 76.66 268 | 87.27 207 | 51.88 308 | 81.07 228 | 73.26 299 | 75.68 137 | 83.25 210 | 86.37 248 | 45.54 315 | 88.80 231 | 51.98 313 | 90.99 233 | 89.31 222 |
|
MIMVSNet | | | 71.09 288 | 71.59 281 | 69.57 312 | 87.23 210 | 50.07 330 | 78.91 264 | 71.83 312 | 60.20 289 | 71.26 320 | 91.76 144 | 55.08 286 | 76.09 321 | 41.06 346 | 87.02 285 | 82.54 305 |
|
Effi-MVS+ | | | 83.90 152 | 84.01 148 | 83.57 165 | 87.22 211 | 65.61 178 | 86.55 112 | 92.40 77 | 78.64 95 | 81.34 239 | 84.18 281 | 83.65 63 | 92.93 137 | 74.22 153 | 87.87 276 | 92.17 163 |
|
BH-RMVSNet | | | 80.53 199 | 80.22 200 | 81.49 201 | 87.19 212 | 66.21 174 | 77.79 277 | 86.23 213 | 74.21 155 | 83.69 203 | 88.50 215 | 73.25 193 | 90.75 190 | 63.18 240 | 87.90 275 | 87.52 244 |
|
thisisatest0530 | | | 79.07 210 | 77.33 220 | 84.26 143 | 87.13 213 | 64.58 184 | 83.66 159 | 75.95 277 | 68.86 226 | 85.22 177 | 87.36 237 | 38.10 353 | 93.57 99 | 75.47 147 | 94.28 160 | 94.62 83 |
|
Effi-MVS+-dtu | | | 85.82 106 | 83.38 157 | 93.14 3 | 87.13 213 | 91.15 3 | 87.70 85 | 88.42 180 | 74.57 152 | 83.56 207 | 85.65 257 | 78.49 117 | 94.21 66 | 72.04 176 | 92.88 195 | 94.05 101 |
|
mvs-test1 | | | 84.55 127 | 82.12 176 | 91.84 17 | 87.13 213 | 89.54 5 | 85.05 128 | 88.42 180 | 74.57 152 | 80.60 253 | 82.98 294 | 78.49 117 | 93.98 77 | 72.04 176 | 89.77 255 | 92.00 165 |
|
v2v482 | | | 84.09 145 | 84.24 145 | 83.62 163 | 87.13 213 | 61.40 234 | 82.71 191 | 89.71 165 | 72.19 189 | 89.55 111 | 91.41 152 | 70.70 217 | 93.20 123 | 81.02 87 | 93.76 172 | 96.25 41 |
|
jason | | | 77.42 223 | 75.75 237 | 82.43 189 | 87.10 217 | 69.27 151 | 77.99 274 | 81.94 249 | 51.47 333 | 77.84 276 | 85.07 270 | 60.32 253 | 89.00 228 | 70.74 183 | 89.27 260 | 89.03 229 |
jason: jason. |
PS-MVSNAJ | | | 77.04 227 | 76.53 230 | 78.56 238 | 87.09 218 | 61.40 234 | 75.26 299 | 87.13 203 | 61.25 282 | 74.38 306 | 77.22 339 | 76.94 135 | 90.94 183 | 64.63 231 | 84.83 307 | 83.35 295 |
|
xiu_mvs_v2_base | | | 77.19 225 | 76.75 224 | 78.52 239 | 87.01 219 | 61.30 236 | 75.55 298 | 87.12 205 | 61.24 283 | 74.45 304 | 78.79 332 | 77.20 129 | 90.93 184 | 64.62 232 | 84.80 308 | 83.32 296 |
|
thres600view7 | | | 75.97 242 | 75.35 242 | 77.85 251 | 87.01 219 | 51.84 312 | 80.45 236 | 73.26 299 | 75.20 146 | 83.10 214 | 86.31 253 | 45.54 315 | 89.05 227 | 55.03 301 | 92.24 208 | 92.66 143 |
|
BH-w/o | | | 76.57 235 | 76.07 234 | 78.10 246 | 86.88 221 | 65.92 176 | 77.63 278 | 86.33 212 | 65.69 250 | 80.89 243 | 79.95 327 | 68.97 223 | 90.74 191 | 53.01 309 | 85.25 301 | 77.62 337 |
|
MAR-MVS | | | 80.24 204 | 78.74 210 | 84.73 127 | 86.87 222 | 78.18 79 | 85.75 119 | 87.81 193 | 65.67 251 | 77.84 276 | 78.50 333 | 73.79 176 | 90.53 197 | 61.59 253 | 90.87 240 | 85.49 265 |
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 |
testing_2 | | | 84.36 134 | 84.64 132 | 83.50 170 | 86.74 223 | 63.97 191 | 84.56 135 | 90.31 144 | 66.22 244 | 91.62 67 | 94.55 63 | 75.88 147 | 91.95 158 | 77.02 138 | 94.89 142 | 94.56 84 |
|
QAPM | | | 82.59 172 | 82.59 169 | 82.58 184 | 86.44 224 | 66.69 170 | 89.94 45 | 90.36 141 | 67.97 232 | 84.94 181 | 92.58 123 | 72.71 199 | 92.18 152 | 70.63 185 | 87.73 278 | 88.85 232 |
|
PAPM | | | 71.77 283 | 70.06 294 | 76.92 262 | 86.39 225 | 53.97 290 | 76.62 287 | 86.62 210 | 53.44 319 | 63.97 349 | 84.73 276 | 57.79 272 | 92.34 149 | 39.65 348 | 81.33 327 | 84.45 278 |
|
GBi-Net | | | 82.02 180 | 82.07 177 | 81.85 196 | 86.38 226 | 61.05 240 | 86.83 101 | 88.27 185 | 72.43 181 | 86.00 166 | 95.64 33 | 63.78 241 | 90.68 193 | 65.95 220 | 93.34 182 | 93.82 110 |
|
test1 | | | 82.02 180 | 82.07 177 | 81.85 196 | 86.38 226 | 61.05 240 | 86.83 101 | 88.27 185 | 72.43 181 | 86.00 166 | 95.64 33 | 63.78 241 | 90.68 193 | 65.95 220 | 93.34 182 | 93.82 110 |
|
FMVSNet2 | | | 81.31 187 | 81.61 183 | 80.41 215 | 86.38 226 | 58.75 262 | 83.93 149 | 86.58 211 | 72.43 181 | 87.65 140 | 92.98 110 | 63.78 241 | 90.22 205 | 66.86 213 | 93.92 167 | 92.27 159 |
|
3Dnovator | | 80.37 7 | 84.80 122 | 84.71 126 | 85.06 119 | 86.36 229 | 74.71 108 | 88.77 70 | 90.00 158 | 75.65 140 | 84.96 179 | 93.17 105 | 74.06 170 | 91.19 177 | 78.28 124 | 91.09 229 | 89.29 224 |
|
casdiffmvs | | | 82.99 167 | 82.51 171 | 84.42 138 | 86.34 230 | 67.92 162 | 87.86 81 | 92.28 81 | 60.95 284 | 81.12 240 | 93.08 106 | 76.07 146 | 93.43 114 | 79.41 115 | 85.45 297 | 91.93 169 |
|
Anonymous20231206 | | | 71.38 287 | 71.88 279 | 69.88 308 | 86.31 231 | 54.37 288 | 70.39 323 | 74.62 287 | 52.57 324 | 76.73 283 | 88.76 211 | 59.94 256 | 72.06 330 | 44.35 341 | 93.23 189 | 83.23 298 |
|
API-MVS | | | 82.28 176 | 82.61 168 | 81.30 203 | 86.29 232 | 69.79 146 | 88.71 71 | 87.67 194 | 78.42 98 | 82.15 226 | 84.15 283 | 77.98 121 | 91.59 168 | 65.39 226 | 92.75 197 | 82.51 307 |
|
tfpn200view9 | | | 74.86 253 | 74.23 251 | 76.74 267 | 86.24 233 | 52.12 305 | 79.24 259 | 73.87 292 | 73.34 167 | 81.82 231 | 84.60 278 | 46.02 309 | 88.80 231 | 51.98 313 | 90.99 233 | 89.31 222 |
|
thres400 | | | 75.14 247 | 74.23 251 | 77.86 250 | 86.24 233 | 52.12 305 | 79.24 259 | 73.87 292 | 73.34 167 | 81.82 231 | 84.60 278 | 46.02 309 | 88.80 231 | 51.98 313 | 90.99 233 | 92.66 143 |
|
testmv | | | 70.47 292 | 70.70 286 | 69.77 310 | 86.22 235 | 53.89 292 | 67.32 334 | 71.91 311 | 63.32 264 | 78.16 274 | 89.47 203 | 56.12 280 | 73.10 328 | 36.43 354 | 87.33 281 | 82.33 309 |
|
UGNet | | | 82.78 169 | 81.64 182 | 86.21 100 | 86.20 236 | 76.24 102 | 86.86 99 | 85.68 219 | 77.07 120 | 73.76 308 | 92.82 116 | 69.64 218 | 91.82 164 | 69.04 199 | 93.69 174 | 90.56 203 |
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 |
CANet | | | 83.79 153 | 82.85 164 | 86.63 88 | 86.17 237 | 72.21 128 | 83.76 156 | 91.43 111 | 77.24 117 | 74.39 305 | 87.45 235 | 75.36 151 | 95.42 32 | 77.03 137 | 92.83 196 | 92.25 161 |
|
conf0.01 | | | 74.17 259 | 73.53 258 | 76.08 274 | 86.13 238 | 50.06 331 | 79.45 250 | 68.54 331 | 72.01 191 | 80.76 246 | 82.50 304 | 41.39 339 | 86.83 257 | 59.66 265 | 91.36 219 | 91.34 184 |
|
conf0.002 | | | 74.17 259 | 73.53 258 | 76.08 274 | 86.13 238 | 50.06 331 | 79.45 250 | 68.54 331 | 72.01 191 | 80.76 246 | 82.50 304 | 41.39 339 | 86.83 257 | 59.66 265 | 91.36 219 | 91.34 184 |
|
thresconf0.02 | | | 73.65 264 | 73.53 258 | 73.98 285 | 86.13 238 | 50.06 331 | 79.45 250 | 68.54 331 | 72.01 191 | 80.76 246 | 82.50 304 | 41.39 339 | 86.83 257 | 59.66 265 | 91.36 219 | 85.06 269 |
|
tfpn_n400 | | | 73.65 264 | 73.53 258 | 73.98 285 | 86.13 238 | 50.06 331 | 79.45 250 | 68.54 331 | 72.01 191 | 80.76 246 | 82.50 304 | 41.39 339 | 86.83 257 | 59.66 265 | 91.36 219 | 85.06 269 |
|
tfpnconf | | | 73.65 264 | 73.53 258 | 73.98 285 | 86.13 238 | 50.06 331 | 79.45 250 | 68.54 331 | 72.01 191 | 80.76 246 | 82.50 304 | 41.39 339 | 86.83 257 | 59.66 265 | 91.36 219 | 85.06 269 |
|
tfpnview11 | | | 73.65 264 | 73.53 258 | 73.98 285 | 86.13 238 | 50.06 331 | 79.45 250 | 68.54 331 | 72.01 191 | 80.76 246 | 82.50 304 | 41.39 339 | 86.83 257 | 59.66 265 | 91.36 219 | 85.06 269 |
|
TR-MVS | | | 76.77 233 | 75.79 235 | 79.72 223 | 86.10 244 | 65.79 177 | 77.14 282 | 83.02 239 | 65.20 256 | 81.40 237 | 82.10 312 | 66.30 231 | 90.73 192 | 55.57 295 | 85.27 300 | 82.65 302 |
|
LCM-MVSNet-Re | | | 83.48 160 | 85.06 116 | 78.75 235 | 85.94 245 | 55.75 281 | 80.05 239 | 94.27 13 | 76.47 125 | 96.09 5 | 94.54 65 | 83.31 67 | 89.75 216 | 59.95 262 | 94.89 142 | 90.75 197 |
|
Fast-Effi-MVS+-dtu | | | 82.54 173 | 81.41 185 | 85.90 107 | 85.60 246 | 76.53 98 | 83.07 179 | 89.62 167 | 73.02 176 | 79.11 269 | 83.51 287 | 80.74 101 | 90.24 204 | 68.76 201 | 89.29 258 | 90.94 190 |
|
v148 | | | 82.31 175 | 82.48 172 | 81.81 199 | 85.59 247 | 59.66 253 | 81.47 221 | 86.02 216 | 72.85 177 | 88.05 135 | 90.65 182 | 70.73 216 | 90.91 186 | 75.15 149 | 91.79 212 | 94.87 79 |
|
MVSFormer | | | 82.23 177 | 81.57 184 | 84.19 148 | 85.54 248 | 69.26 152 | 91.98 25 | 90.08 155 | 71.54 202 | 76.23 288 | 85.07 270 | 58.69 264 | 94.27 62 | 86.26 29 | 88.77 264 | 89.03 229 |
|
lupinMVS | | | 76.37 238 | 74.46 249 | 82.09 190 | 85.54 248 | 69.26 152 | 76.79 283 | 80.77 256 | 50.68 340 | 76.23 288 | 82.82 299 | 58.69 264 | 88.94 229 | 69.85 189 | 88.77 264 | 88.07 236 |
|
tfpn1000 | | | 73.63 268 | 73.58 256 | 73.79 291 | 85.46 250 | 50.31 328 | 79.99 241 | 68.18 337 | 72.33 185 | 80.66 252 | 83.05 292 | 39.80 350 | 86.74 266 | 60.96 257 | 91.78 213 | 84.32 281 |
|
TinyColmap | | | 81.25 190 | 82.34 175 | 77.99 248 | 85.33 251 | 60.68 245 | 82.32 200 | 88.33 183 | 71.26 204 | 86.97 152 | 92.22 134 | 77.10 132 | 86.98 254 | 62.37 243 | 95.17 132 | 86.31 256 |
|
PAPR | | | 78.84 211 | 78.10 215 | 81.07 207 | 85.17 252 | 60.22 248 | 82.21 205 | 90.57 135 | 62.51 272 | 75.32 298 | 84.61 277 | 74.99 156 | 92.30 150 | 59.48 272 | 88.04 274 | 90.68 199 |
|
DI_MVS_plusplus_test | | | 81.27 189 | 81.26 186 | 81.29 204 | 84.98 253 | 61.65 232 | 81.98 210 | 87.25 199 | 63.56 262 | 87.56 142 | 89.60 201 | 73.62 179 | 91.83 163 | 72.20 175 | 90.59 249 | 90.38 208 |
|
pmmvs4 | | | 74.92 252 | 72.98 269 | 80.73 212 | 84.95 254 | 71.71 137 | 76.23 292 | 77.59 268 | 52.83 322 | 77.73 279 | 86.38 247 | 56.35 278 | 84.97 285 | 57.72 285 | 87.05 284 | 85.51 264 |
|
Test4 | | | 81.31 187 | 81.13 190 | 81.88 195 | 84.89 255 | 63.05 205 | 82.37 198 | 90.50 136 | 62.75 270 | 89.00 119 | 88.29 222 | 67.55 227 | 91.68 166 | 73.55 162 | 91.24 228 | 90.89 192 |
|
Patchmatch-RL test | | | 74.48 256 | 73.68 255 | 76.89 264 | 84.83 256 | 66.54 171 | 72.29 316 | 69.16 330 | 57.70 299 | 86.76 153 | 86.33 251 | 45.79 314 | 82.59 302 | 69.63 191 | 90.65 247 | 81.54 322 |
|
tfpn_ndepth | | | 72.54 276 | 72.30 276 | 73.24 294 | 84.81 257 | 51.42 314 | 79.24 259 | 70.49 324 | 69.26 220 | 78.48 272 | 79.80 328 | 40.16 349 | 86.77 264 | 58.08 284 | 90.43 250 | 81.53 323 |
|
test_normal | | | 81.23 191 | 81.16 189 | 81.43 202 | 84.77 258 | 61.99 227 | 81.46 222 | 86.95 209 | 63.16 267 | 87.22 145 | 89.63 200 | 73.62 179 | 91.65 167 | 72.92 170 | 90.70 244 | 90.65 201 |
|
XXY-MVS | | | 74.44 258 | 76.19 233 | 69.21 313 | 84.61 259 | 52.43 304 | 71.70 318 | 77.18 270 | 60.73 287 | 80.60 253 | 90.96 171 | 75.44 149 | 69.35 337 | 56.13 291 | 88.33 268 | 85.86 261 |
|
cascas | | | 76.29 239 | 74.81 245 | 80.72 213 | 84.47 260 | 62.94 207 | 73.89 309 | 87.34 196 | 55.94 307 | 75.16 300 | 76.53 342 | 63.97 239 | 91.16 178 | 65.00 227 | 90.97 237 | 88.06 237 |
|
PVSNet_BlendedMVS | | | 78.80 212 | 77.84 216 | 81.65 200 | 84.43 261 | 63.41 196 | 79.49 249 | 90.44 138 | 61.70 280 | 75.43 296 | 87.07 240 | 69.11 221 | 91.44 172 | 60.68 259 | 92.24 208 | 90.11 214 |
|
PVSNet_Blended | | | 76.49 237 | 75.40 240 | 79.76 222 | 84.43 261 | 63.41 196 | 75.14 300 | 90.44 138 | 57.36 301 | 75.43 296 | 78.30 334 | 69.11 221 | 91.44 172 | 60.68 259 | 87.70 279 | 84.42 279 |
|
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 76.72 13 | 81.98 182 | 82.00 179 | 81.93 192 | 84.42 263 | 68.22 160 | 88.50 75 | 89.48 169 | 66.92 239 | 81.80 233 | 91.86 138 | 72.59 203 | 90.16 207 | 71.19 179 | 91.25 227 | 87.40 246 |
|
OpenMVS_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 70.19 17 | 77.77 221 | 77.46 218 | 78.71 236 | 84.39 264 | 61.15 238 | 81.18 227 | 82.52 242 | 62.45 274 | 83.34 209 | 87.37 236 | 66.20 232 | 88.66 239 | 64.69 230 | 85.02 304 | 86.32 255 |
|
0601test | | | 78.71 214 | 78.51 212 | 79.32 229 | 84.32 265 | 58.84 259 | 78.38 269 | 85.33 223 | 75.99 131 | 82.49 220 | 86.57 243 | 58.01 267 | 90.02 213 | 62.74 241 | 92.73 198 | 89.10 226 |
|
Anonymous20240521 | | | 78.71 214 | 78.51 212 | 79.32 229 | 84.32 265 | 58.84 259 | 78.38 269 | 85.33 223 | 75.99 131 | 82.49 220 | 86.57 243 | 58.01 267 | 90.02 213 | 62.74 241 | 92.73 198 | 89.10 226 |
|
Regformer-3 | | | 85.06 115 | 84.67 128 | 86.22 98 | 84.27 267 | 73.43 115 | 84.07 143 | 85.26 225 | 80.77 68 | 88.62 126 | 85.48 260 | 80.56 103 | 90.39 201 | 81.99 78 | 91.04 231 | 94.85 81 |
|
Regformer-4 | | | 86.41 91 | 85.71 107 | 88.52 64 | 84.27 267 | 77.57 85 | 84.07 143 | 88.00 190 | 82.82 45 | 89.84 98 | 85.48 260 | 82.06 83 | 92.77 141 | 83.83 58 | 91.04 231 | 95.22 76 |
|
DELS-MVS | | | 81.44 186 | 81.25 187 | 82.03 191 | 84.27 267 | 62.87 209 | 76.47 290 | 92.49 76 | 70.97 207 | 81.64 235 | 83.83 284 | 75.03 155 | 92.70 142 | 74.29 152 | 92.22 210 | 90.51 205 |
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 |
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 84.44 130 | 86.33 96 | 78.78 234 | 84.20 270 | 73.57 114 | 89.55 53 | 90.44 138 | 84.24 32 | 84.38 194 | 94.89 53 | 76.35 145 | 80.40 310 | 76.14 143 | 96.80 77 | 82.36 308 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
Regformer-1 | | | 86.00 101 | 85.50 111 | 87.49 80 | 84.18 271 | 76.90 93 | 83.52 164 | 87.94 192 | 82.18 53 | 89.19 116 | 85.07 270 | 82.28 79 | 91.89 161 | 82.40 74 | 92.72 200 | 93.69 116 |
|
Regformer-2 | | | 86.74 86 | 86.08 102 | 88.73 62 | 84.18 271 | 79.20 71 | 83.52 164 | 89.33 171 | 83.33 38 | 89.92 96 | 85.07 270 | 83.23 68 | 93.16 126 | 83.39 60 | 92.72 200 | 93.83 108 |
|
EI-MVSNet-Vis-set | | | 85.12 114 | 84.53 135 | 86.88 85 | 84.01 273 | 72.76 119 | 83.91 150 | 85.18 227 | 80.44 69 | 88.75 123 | 85.49 259 | 80.08 106 | 91.92 159 | 82.02 77 | 90.85 241 | 95.97 49 |
|
semantic-postprocess | | | | | 84.34 141 | 83.93 274 | 69.66 148 | | 81.09 254 | 72.43 181 | 86.47 163 | 90.19 192 | 57.56 273 | 93.15 128 | 77.45 132 | 86.39 290 | 90.22 210 |
|
MSDG | | | 80.06 207 | 79.99 204 | 80.25 216 | 83.91 275 | 68.04 161 | 77.51 280 | 89.19 172 | 77.65 106 | 81.94 228 | 83.45 289 | 76.37 144 | 86.31 271 | 63.31 239 | 86.59 287 | 86.41 254 |
|
EI-MVSNet-UG-set | | | 85.04 116 | 84.44 137 | 86.85 86 | 83.87 276 | 72.52 122 | 83.82 152 | 85.15 228 | 80.27 73 | 88.75 123 | 85.45 263 | 79.95 108 | 91.90 160 | 81.92 79 | 90.80 242 | 96.13 43 |
|
thres200 | | | 72.34 279 | 71.55 283 | 74.70 283 | 83.48 277 | 51.60 313 | 75.02 301 | 73.71 295 | 70.14 214 | 78.56 271 | 80.57 322 | 46.20 307 | 88.20 244 | 46.99 335 | 89.29 258 | 84.32 281 |
|
USDC | | | 76.63 234 | 76.73 225 | 76.34 271 | 83.46 278 | 57.20 270 | 80.02 240 | 88.04 189 | 52.14 328 | 83.65 206 | 91.25 154 | 63.24 244 | 86.65 267 | 54.66 303 | 94.11 162 | 85.17 267 |
|
HY-MVS | | 64.64 18 | 73.03 271 | 72.47 275 | 74.71 282 | 83.36 279 | 54.19 289 | 82.14 208 | 81.96 247 | 56.76 306 | 69.57 328 | 86.21 254 | 60.03 255 | 84.83 288 | 49.58 324 | 82.65 320 | 85.11 268 |
|
EI-MVSNet | | | 82.61 171 | 82.42 173 | 83.20 174 | 83.25 280 | 63.66 193 | 83.50 167 | 85.07 229 | 76.06 128 | 86.55 157 | 85.10 268 | 73.41 185 | 90.25 202 | 78.15 126 | 90.67 245 | 95.68 59 |
|
CVMVSNet | | | 72.62 275 | 71.41 284 | 76.28 272 | 83.25 280 | 60.34 247 | 83.50 167 | 79.02 263 | 37.77 362 | 76.33 286 | 85.10 268 | 49.60 298 | 87.41 249 | 70.54 186 | 77.54 341 | 81.08 330 |
|
V42 | | | 83.47 161 | 83.37 158 | 83.75 157 | 83.16 282 | 63.33 199 | 81.31 223 | 90.23 153 | 69.51 218 | 90.91 79 | 90.81 176 | 74.16 169 | 92.29 151 | 80.06 104 | 90.22 251 | 95.62 61 |
|
EU-MVSNet | | | 75.12 249 | 74.43 250 | 77.18 260 | 83.11 283 | 59.48 255 | 85.71 121 | 82.43 244 | 39.76 361 | 85.64 172 | 88.76 211 | 44.71 331 | 87.88 246 | 73.86 159 | 85.88 294 | 84.16 284 |
|
FMVSNet3 | | | 78.80 212 | 78.55 211 | 79.57 226 | 82.89 284 | 56.89 273 | 81.76 215 | 85.77 218 | 69.04 224 | 86.00 166 | 90.44 186 | 51.75 293 | 90.09 211 | 65.95 220 | 93.34 182 | 91.72 174 |
|
test1235678 | | | 65.57 317 | 65.73 314 | 65.06 329 | 82.84 285 | 50.90 320 | 62.90 342 | 69.26 328 | 57.17 304 | 72.36 314 | 83.04 293 | 46.02 309 | 70.10 334 | 32.79 359 | 85.24 302 | 74.19 344 |
|
MVS_Test | | | 82.47 174 | 83.22 159 | 80.22 217 | 82.62 286 | 57.75 266 | 82.54 195 | 91.96 91 | 71.16 205 | 82.89 217 | 92.52 125 | 77.41 128 | 90.50 198 | 80.04 105 | 87.84 277 | 92.40 154 |
|
LF4IMVS | | | 82.75 170 | 81.93 180 | 85.19 117 | 82.08 287 | 80.15 62 | 85.53 122 | 88.76 176 | 68.01 230 | 85.58 173 | 87.75 230 | 71.80 211 | 86.85 256 | 74.02 156 | 93.87 168 | 88.58 233 |
|
PVSNet | | 58.17 21 | 66.41 313 | 65.63 315 | 68.75 316 | 81.96 288 | 49.88 337 | 62.19 344 | 72.51 307 | 51.03 336 | 68.04 333 | 75.34 346 | 50.84 294 | 74.77 325 | 45.82 339 | 82.96 316 | 81.60 321 |
|
GA-MVS | | | 75.83 243 | 74.61 246 | 79.48 228 | 81.87 289 | 59.25 257 | 73.42 312 | 82.88 240 | 68.68 227 | 79.75 262 | 81.80 315 | 50.62 295 | 89.46 218 | 66.85 214 | 85.64 296 | 89.72 216 |
|
MS-PatchMatch | | | 70.93 289 | 70.22 292 | 73.06 297 | 81.85 290 | 62.50 218 | 73.82 310 | 77.90 266 | 52.44 325 | 75.92 292 | 81.27 319 | 55.67 282 | 81.75 305 | 55.37 297 | 77.70 339 | 74.94 342 |
|
Patchmatch-test1 | | | 72.75 274 | 72.61 272 | 73.19 295 | 81.62 291 | 55.86 279 | 78.89 265 | 71.37 317 | 61.73 278 | 74.93 301 | 82.15 311 | 60.46 252 | 81.80 304 | 59.68 264 | 82.63 322 | 81.92 316 |
|
FMVSNet5 | | | 72.10 281 | 71.69 280 | 73.32 292 | 81.57 292 | 53.02 299 | 76.77 284 | 78.37 265 | 63.31 265 | 76.37 285 | 91.85 139 | 36.68 356 | 78.98 314 | 47.87 331 | 92.45 203 | 87.95 240 |
|
thisisatest0515 | | | 73.00 272 | 70.52 288 | 80.46 214 | 81.45 293 | 59.90 251 | 73.16 314 | 74.31 291 | 57.86 298 | 76.08 291 | 77.78 335 | 37.60 355 | 92.12 155 | 65.00 227 | 91.45 218 | 89.35 221 |
|
CANet_DTU | | | 77.81 220 | 77.05 221 | 80.09 218 | 81.37 294 | 59.90 251 | 83.26 174 | 88.29 184 | 69.16 222 | 67.83 335 | 83.72 285 | 60.93 249 | 89.47 217 | 69.22 196 | 89.70 256 | 90.88 193 |
|
ANet_high | | | 83.17 165 | 85.68 108 | 75.65 278 | 81.24 295 | 45.26 346 | 79.94 242 | 92.91 62 | 83.83 36 | 91.33 73 | 96.88 10 | 80.25 105 | 85.92 276 | 68.89 200 | 95.89 111 | 95.76 54 |
|
new-patchmatchnet | | | 70.10 295 | 73.37 265 | 60.29 341 | 81.23 296 | 16.95 369 | 59.54 348 | 74.62 287 | 62.93 268 | 80.97 241 | 87.93 228 | 62.83 245 | 71.90 331 | 55.24 298 | 95.01 139 | 92.00 165 |
|
test20.03 | | | 73.75 263 | 74.59 248 | 71.22 307 | 81.11 297 | 51.12 318 | 70.15 324 | 72.10 309 | 70.42 210 | 80.28 260 | 91.50 150 | 64.21 238 | 74.72 327 | 46.96 336 | 94.58 155 | 87.82 243 |
|
MVS | | | 73.21 270 | 72.59 273 | 75.06 280 | 80.97 298 | 60.81 244 | 81.64 218 | 85.92 217 | 46.03 352 | 71.68 318 | 77.54 336 | 68.47 224 | 89.77 215 | 55.70 294 | 85.39 298 | 74.60 343 |
|
N_pmnet | | | 70.20 293 | 68.80 300 | 74.38 284 | 80.91 299 | 84.81 35 | 59.12 351 | 76.45 276 | 55.06 311 | 75.31 299 | 82.36 310 | 55.74 281 | 54.82 362 | 47.02 334 | 87.24 283 | 83.52 291 |
|
IterMVS | | | 76.91 228 | 76.34 232 | 78.64 237 | 80.91 299 | 64.03 189 | 76.30 291 | 79.03 262 | 64.88 259 | 83.11 213 | 89.16 206 | 59.90 257 | 84.46 290 | 68.61 204 | 85.15 303 | 87.42 245 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
WTY-MVS | | | 67.91 307 | 68.35 302 | 66.58 324 | 80.82 301 | 48.12 340 | 65.96 338 | 72.60 305 | 53.67 318 | 71.20 321 | 81.68 317 | 58.97 263 | 69.06 339 | 48.57 327 | 81.67 324 | 82.55 304 |
|
IB-MVS | | 62.13 19 | 71.64 284 | 68.97 298 | 79.66 225 | 80.80 302 | 62.26 226 | 73.94 308 | 76.90 272 | 63.27 266 | 68.63 331 | 76.79 340 | 33.83 359 | 91.84 162 | 59.28 273 | 87.26 282 | 84.88 274 |
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 |
our_test_3 | | | 71.85 282 | 71.59 281 | 72.62 301 | 80.71 303 | 53.78 293 | 69.72 326 | 71.71 316 | 58.80 292 | 78.03 275 | 80.51 323 | 56.61 277 | 78.84 315 | 62.20 245 | 86.04 293 | 85.23 266 |
|
ppachtmachnet_test | | | 74.73 255 | 74.00 253 | 76.90 263 | 80.71 303 | 56.89 273 | 71.53 319 | 78.42 264 | 58.24 296 | 79.32 267 | 82.92 298 | 57.91 270 | 84.26 292 | 65.60 225 | 91.36 219 | 89.56 217 |
|
testgi | | | 72.36 278 | 74.61 246 | 65.59 326 | 80.56 305 | 42.82 354 | 68.29 329 | 73.35 298 | 66.87 240 | 81.84 230 | 89.93 196 | 72.08 208 | 66.92 345 | 46.05 338 | 92.54 202 | 87.01 250 |
|
1314 | | | 73.22 269 | 72.56 274 | 75.20 279 | 80.41 306 | 57.84 264 | 81.64 218 | 85.36 222 | 51.68 331 | 73.10 311 | 76.65 341 | 61.45 248 | 85.19 283 | 63.54 236 | 79.21 336 | 82.59 303 |
|
diffmvs1 | | | 82.95 168 | 83.63 154 | 80.90 209 | 80.05 307 | 61.05 240 | 82.98 181 | 89.93 160 | 74.72 150 | 82.37 222 | 92.93 114 | 76.47 143 | 88.80 231 | 81.73 81 | 91.54 217 | 92.85 135 |
|
CR-MVSNet | | | 74.00 261 | 73.04 268 | 76.85 265 | 79.58 308 | 62.64 212 | 82.58 192 | 76.90 272 | 50.50 341 | 75.72 294 | 92.38 126 | 48.07 302 | 84.07 293 | 68.72 203 | 82.91 318 | 83.85 287 |
|
RPMNet | | | 76.06 240 | 75.79 235 | 76.85 265 | 79.58 308 | 62.64 212 | 82.58 192 | 71.75 314 | 74.80 148 | 75.72 294 | 92.59 121 | 48.69 300 | 84.07 293 | 73.48 163 | 82.91 318 | 83.85 287 |
|
UnsupCasMVSNet_bld | | | 69.21 303 | 69.68 295 | 67.82 320 | 79.42 310 | 51.15 317 | 67.82 333 | 75.79 278 | 54.15 315 | 77.47 280 | 85.36 267 | 59.26 262 | 70.64 333 | 48.46 328 | 79.35 334 | 81.66 320 |
|
PatchT | | | 70.52 291 | 72.76 270 | 63.79 332 | 79.38 311 | 33.53 362 | 77.63 278 | 65.37 348 | 73.61 161 | 71.77 317 | 92.79 119 | 44.38 332 | 75.65 324 | 64.53 233 | 85.37 299 | 82.18 311 |
|
Patchmtry | | | 76.56 236 | 77.46 218 | 73.83 290 | 79.37 312 | 46.60 343 | 82.41 197 | 76.90 272 | 73.81 159 | 85.56 174 | 92.38 126 | 48.07 302 | 83.98 295 | 63.36 238 | 95.31 128 | 90.92 191 |
|
mvs_anonymous | | | 78.13 217 | 78.76 209 | 76.23 273 | 79.24 313 | 50.31 328 | 78.69 267 | 84.82 234 | 61.60 281 | 83.09 215 | 92.82 116 | 73.89 175 | 87.01 252 | 68.33 206 | 86.41 289 | 91.37 182 |
|
MVS-HIRNet | | | 61.16 328 | 62.92 323 | 55.87 345 | 79.09 314 | 35.34 361 | 71.83 317 | 57.98 362 | 46.56 350 | 59.05 359 | 91.14 161 | 49.95 297 | 76.43 320 | 38.74 351 | 71.92 351 | 55.84 361 |
|
diffmvs | | | 81.78 184 | 82.36 174 | 80.02 219 | 79.06 315 | 59.93 250 | 83.30 172 | 88.41 182 | 73.47 163 | 78.38 273 | 92.05 135 | 75.85 148 | 88.38 241 | 80.73 94 | 89.98 254 | 91.76 173 |
|
MDA-MVSNet-bldmvs | | | 77.47 222 | 76.90 223 | 79.16 231 | 79.03 316 | 64.59 183 | 66.58 337 | 75.67 280 | 73.15 174 | 88.86 120 | 88.99 209 | 66.94 229 | 81.23 307 | 64.71 229 | 88.22 273 | 91.64 177 |
|
tpm2 | | | 68.45 305 | 66.83 309 | 73.30 293 | 78.93 317 | 48.50 338 | 79.76 243 | 71.76 313 | 47.50 348 | 69.92 327 | 83.60 286 | 42.07 338 | 88.40 240 | 48.44 329 | 79.51 332 | 83.01 301 |
|
testus | | | 62.33 322 | 63.03 322 | 60.20 342 | 78.78 318 | 40.74 355 | 59.14 349 | 69.80 327 | 49.26 345 | 71.41 319 | 74.72 348 | 52.33 292 | 63.52 354 | 29.84 361 | 82.01 323 | 76.36 339 |
|
tpm | | | 67.95 306 | 68.08 305 | 67.55 321 | 78.74 319 | 43.53 352 | 75.60 296 | 67.10 345 | 54.92 312 | 72.23 315 | 88.10 224 | 42.87 336 | 75.97 322 | 52.21 311 | 80.95 330 | 83.15 299 |
|
tpmp4_e23 | | | 69.43 300 | 67.33 307 | 75.72 277 | 78.53 320 | 52.75 300 | 82.13 209 | 74.91 284 | 49.23 346 | 66.37 338 | 84.17 282 | 41.28 345 | 88.67 238 | 49.73 323 | 79.63 331 | 85.75 262 |
|
no-one | | | 71.52 286 | 70.43 291 | 74.81 281 | 78.45 321 | 63.41 196 | 57.73 354 | 77.03 271 | 51.46 334 | 77.17 282 | 90.33 188 | 54.96 287 | 80.35 311 | 47.41 332 | 99.29 2 | 80.68 334 |
|
1111 | | | 61.71 324 | 63.77 320 | 55.55 347 | 78.05 322 | 25.74 366 | 60.62 345 | 67.52 338 | 66.09 245 | 74.68 302 | 86.50 245 | 16.00 371 | 59.22 360 | 38.79 349 | 85.65 295 | 81.70 318 |
|
.test1245 | | | 48.02 341 | 54.41 340 | 28.84 354 | 78.05 322 | 25.74 366 | 60.62 345 | 67.52 338 | 66.09 245 | 74.68 302 | 86.50 245 | 16.00 371 | 59.22 360 | 38.79 349 | 1.47 366 | 1.55 367 |
|
MDTV_nov1_ep13 | | | | 68.29 304 | | 78.03 324 | 43.87 351 | 74.12 307 | 72.22 308 | 52.17 326 | 67.02 337 | 85.54 258 | 45.36 321 | 80.85 308 | 55.73 292 | 84.42 310 | |
|
EPNet_dtu | | | 72.87 273 | 71.33 285 | 77.49 258 | 77.72 325 | 60.55 246 | 82.35 199 | 75.79 278 | 66.49 242 | 58.39 362 | 81.06 321 | 53.68 289 | 85.98 275 | 53.55 307 | 92.97 194 | 85.95 259 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 69.71 299 | 68.83 299 | 72.33 303 | 77.66 326 | 53.60 294 | 79.29 257 | 69.99 326 | 57.66 300 | 72.53 313 | 82.93 297 | 46.45 306 | 80.08 313 | 60.91 258 | 72.09 350 | 83.31 297 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
sss | | | 66.92 310 | 67.26 308 | 65.90 325 | 77.23 327 | 51.10 319 | 64.79 339 | 71.72 315 | 52.12 329 | 70.13 326 | 80.18 325 | 57.96 269 | 65.36 352 | 50.21 319 | 81.01 329 | 81.25 327 |
|
CostFormer | | | 69.98 298 | 68.68 301 | 73.87 289 | 77.14 328 | 50.72 326 | 79.26 258 | 74.51 289 | 51.94 330 | 70.97 323 | 84.75 275 | 45.16 329 | 87.49 248 | 55.16 299 | 79.23 335 | 83.40 294 |
|
tpm cat1 | | | 66.76 311 | 65.21 316 | 71.42 306 | 77.09 329 | 50.62 327 | 78.01 273 | 73.68 296 | 44.89 354 | 68.64 329 | 79.00 331 | 45.51 319 | 82.42 303 | 49.91 321 | 70.15 354 | 81.23 329 |
|
pmmvs5 | | | 70.73 290 | 70.07 293 | 72.72 299 | 77.03 330 | 52.73 301 | 74.14 306 | 75.65 281 | 50.36 342 | 72.17 316 | 85.37 266 | 55.42 284 | 80.67 309 | 52.86 310 | 87.59 280 | 84.77 275 |
|
EPNet | | | 80.37 201 | 78.41 214 | 86.23 97 | 76.75 331 | 73.28 116 | 87.18 94 | 77.45 269 | 76.24 127 | 68.14 332 | 88.93 210 | 65.41 236 | 93.85 82 | 69.47 192 | 96.12 102 | 91.55 180 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CHOSEN 280x420 | | | 59.08 332 | 56.52 337 | 66.76 323 | 76.51 332 | 64.39 187 | 49.62 361 | 59.00 358 | 43.86 356 | 55.66 365 | 68.41 356 | 35.55 358 | 68.21 341 | 43.25 342 | 76.78 343 | 67.69 353 |
|
UnsupCasMVSNet_eth | | | 71.63 285 | 72.30 276 | 69.62 311 | 76.47 333 | 52.70 302 | 70.03 325 | 80.97 255 | 59.18 291 | 79.36 266 | 88.21 223 | 60.50 251 | 69.12 338 | 58.33 281 | 77.62 340 | 87.04 249 |
|
test-LLR | | | 67.21 309 | 66.74 310 | 68.63 317 | 76.45 334 | 55.21 284 | 67.89 330 | 67.14 343 | 62.43 275 | 65.08 345 | 72.39 350 | 43.41 333 | 69.37 335 | 61.00 255 | 84.89 305 | 81.31 325 |
|
test-mter | | | 65.00 318 | 63.79 319 | 68.63 317 | 76.45 334 | 55.21 284 | 67.89 330 | 67.14 343 | 50.98 337 | 65.08 345 | 72.39 350 | 28.27 366 | 69.37 335 | 61.00 255 | 84.89 305 | 81.31 325 |
|
gg-mvs-nofinetune | | | 68.96 304 | 69.11 297 | 68.52 319 | 76.12 336 | 45.32 345 | 83.59 160 | 55.88 363 | 86.68 20 | 64.62 348 | 97.01 7 | 30.36 363 | 83.97 296 | 44.78 340 | 82.94 317 | 76.26 340 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 59.41 20 | 75.12 249 | 73.57 257 | 79.77 221 | 75.84 337 | 67.22 165 | 81.21 226 | 82.18 245 | 50.78 338 | 76.50 284 | 87.66 232 | 55.20 285 | 82.99 300 | 62.17 247 | 90.64 248 | 89.09 228 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test12356 | | | 54.91 338 | 57.14 336 | 48.22 351 | 75.83 338 | 17.47 368 | 52.31 360 | 69.20 329 | 51.66 332 | 60.11 355 | 75.40 345 | 29.77 365 | 62.62 358 | 27.64 362 | 72.37 349 | 64.59 354 |
|
PNet_i23d | | | 52.13 339 | 51.24 341 | 54.79 348 | 75.56 339 | 45.26 346 | 54.54 357 | 52.55 366 | 66.95 238 | 57.19 363 | 65.82 359 | 13.15 373 | 63.40 355 | 36.39 355 | 39.04 364 | 55.71 362 |
|
wuyk23d | | | 75.13 248 | 79.30 207 | 62.63 333 | 75.56 339 | 75.18 106 | 80.89 232 | 73.10 303 | 75.06 147 | 94.76 14 | 95.32 40 | 87.73 31 | 52.85 363 | 34.16 357 | 97.11 67 | 59.85 358 |
|
Patchmatch-test | | | 65.91 315 | 67.38 306 | 61.48 338 | 75.51 341 | 43.21 353 | 68.84 327 | 63.79 350 | 62.48 273 | 72.80 312 | 83.42 290 | 44.89 330 | 59.52 359 | 48.27 330 | 86.45 288 | 81.70 318 |
|
new_pmnet | | | 55.69 336 | 57.66 335 | 49.76 349 | 75.47 342 | 30.59 363 | 59.56 347 | 51.45 367 | 43.62 357 | 62.49 350 | 75.48 344 | 40.96 347 | 49.15 365 | 37.39 353 | 72.52 348 | 69.55 351 |
|
gm-plane-assit | | | | | | 75.42 343 | 44.97 349 | | | 52.17 326 | | 72.36 352 | | 87.90 245 | 54.10 305 | | |
|
PatchFormer-LS_test | | | 67.91 307 | 66.49 313 | 72.17 304 | 75.29 344 | 51.85 311 | 75.68 294 | 73.62 297 | 57.23 303 | 68.64 329 | 68.13 358 | 42.19 337 | 82.76 301 | 64.06 234 | 73.51 347 | 81.89 317 |
|
MVSTER | | | 77.09 226 | 75.70 238 | 81.25 205 | 75.27 345 | 61.08 239 | 77.49 281 | 85.07 229 | 60.78 286 | 86.55 157 | 88.68 213 | 43.14 335 | 90.25 202 | 73.69 160 | 90.67 245 | 92.42 152 |
|
PVSNet_0 | | 51.08 22 | 56.10 335 | 54.97 339 | 59.48 343 | 75.12 346 | 53.28 298 | 55.16 355 | 61.89 353 | 44.30 355 | 59.16 358 | 62.48 362 | 54.22 288 | 65.91 350 | 35.40 356 | 47.01 362 | 59.25 359 |
|
test0.0.03 1 | | | 64.66 319 | 64.36 318 | 65.57 327 | 75.03 347 | 46.89 342 | 64.69 340 | 61.58 356 | 62.43 275 | 71.18 322 | 77.54 336 | 43.41 333 | 68.47 340 | 40.75 347 | 82.65 320 | 81.35 324 |
|
DWT-MVSNet_test | | | 66.43 312 | 64.37 317 | 72.63 300 | 74.86 348 | 50.86 321 | 76.52 288 | 72.74 304 | 54.06 316 | 65.50 342 | 68.30 357 | 32.13 361 | 84.84 287 | 61.63 252 | 73.59 346 | 82.19 310 |
|
tpmvs | | | 70.16 294 | 69.56 296 | 71.96 305 | 74.71 349 | 48.13 339 | 79.63 244 | 75.45 283 | 65.02 258 | 70.26 325 | 81.88 314 | 45.34 322 | 85.68 279 | 58.34 280 | 75.39 344 | 82.08 312 |
|
MDA-MVSNet_test_wron | | | 70.05 297 | 70.44 289 | 68.88 315 | 73.84 350 | 53.47 295 | 58.93 353 | 67.28 341 | 58.43 294 | 87.09 149 | 85.40 264 | 59.80 259 | 67.25 343 | 59.66 265 | 83.54 313 | 85.92 260 |
|
YYNet1 | | | 70.06 296 | 70.44 289 | 68.90 314 | 73.76 351 | 53.42 297 | 58.99 352 | 67.20 342 | 58.42 295 | 87.10 148 | 85.39 265 | 59.82 258 | 67.32 342 | 59.79 263 | 83.50 314 | 85.96 258 |
|
GG-mvs-BLEND | | | | | 67.16 322 | 73.36 352 | 46.54 344 | 84.15 142 | 55.04 364 | | 58.64 361 | 61.95 363 | 29.93 364 | 83.87 297 | 38.71 352 | 76.92 342 | 71.07 349 |
|
test2356 | | | 56.69 334 | 55.15 338 | 61.32 339 | 73.20 353 | 44.11 350 | 54.95 356 | 62.52 351 | 48.75 347 | 62.45 351 | 68.42 355 | 21.10 370 | 65.67 351 | 26.86 363 | 78.08 338 | 74.19 344 |
|
JIA-IIPM | | | 69.41 302 | 66.64 312 | 77.70 252 | 73.19 354 | 71.24 140 | 75.67 295 | 65.56 347 | 70.42 210 | 65.18 344 | 92.97 111 | 33.64 360 | 83.06 299 | 53.52 308 | 69.61 357 | 78.79 336 |
|
ADS-MVSNet2 | | | 65.87 316 | 63.64 321 | 72.55 302 | 73.16 355 | 56.92 272 | 67.10 335 | 74.81 286 | 49.74 343 | 66.04 340 | 82.97 295 | 46.71 304 | 77.26 317 | 42.29 343 | 69.96 355 | 83.46 292 |
|
ADS-MVSNet | | | 61.90 323 | 62.19 325 | 61.03 340 | 73.16 355 | 36.42 360 | 67.10 335 | 61.75 354 | 49.74 343 | 66.04 340 | 82.97 295 | 46.71 304 | 63.21 356 | 42.29 343 | 69.96 355 | 83.46 292 |
|
DSMNet-mixed | | | 60.98 330 | 61.61 327 | 59.09 344 | 72.88 357 | 45.05 348 | 74.70 303 | 46.61 369 | 26.20 364 | 65.34 343 | 90.32 189 | 55.46 283 | 63.12 357 | 41.72 345 | 81.30 328 | 69.09 352 |
|
tpmrst | | | 66.28 314 | 66.69 311 | 65.05 330 | 72.82 358 | 39.33 357 | 78.20 272 | 70.69 323 | 53.16 321 | 67.88 334 | 80.36 324 | 48.18 301 | 74.75 326 | 58.13 282 | 70.79 352 | 81.08 330 |
|
TESTMET0.1,1 | | | 61.29 327 | 60.32 331 | 64.19 331 | 72.06 359 | 51.30 315 | 67.89 330 | 62.09 352 | 45.27 353 | 60.65 354 | 69.01 353 | 27.93 367 | 64.74 353 | 56.31 290 | 81.65 326 | 76.53 338 |
|
dp | | | 60.70 331 | 60.29 332 | 61.92 336 | 72.04 360 | 38.67 359 | 70.83 320 | 64.08 349 | 51.28 335 | 60.75 353 | 77.28 338 | 36.59 357 | 71.58 332 | 47.41 332 | 62.34 361 | 75.52 341 |
|
pmmvs3 | | | 62.47 320 | 60.02 333 | 69.80 309 | 71.58 361 | 64.00 190 | 70.52 322 | 58.44 360 | 39.77 360 | 66.05 339 | 75.84 343 | 27.10 368 | 72.28 329 | 46.15 337 | 84.77 309 | 73.11 346 |
|
LP | | | 69.42 301 | 68.30 303 | 72.77 298 | 71.48 362 | 56.84 275 | 73.66 311 | 74.84 285 | 63.52 263 | 70.95 324 | 83.35 291 | 49.55 299 | 77.15 319 | 57.13 287 | 70.21 353 | 84.33 280 |
|
EPMVS | | | 62.47 320 | 62.63 324 | 62.01 334 | 70.63 363 | 38.74 358 | 74.76 302 | 52.86 365 | 53.91 317 | 67.71 336 | 80.01 326 | 39.40 351 | 66.60 347 | 55.54 296 | 68.81 359 | 80.68 334 |
|
E-PMN | | | 61.59 326 | 61.62 326 | 61.49 337 | 66.81 364 | 55.40 282 | 53.77 358 | 60.34 357 | 66.80 241 | 58.90 360 | 65.50 360 | 40.48 348 | 66.12 349 | 55.72 293 | 86.25 291 | 62.95 356 |
|
testpf | | | 58.55 333 | 61.58 328 | 49.48 350 | 66.03 365 | 40.05 356 | 74.40 305 | 58.07 361 | 64.72 260 | 59.36 357 | 72.67 349 | 22.76 369 | 66.92 345 | 67.07 212 | 69.15 358 | 41.46 363 |
|
EMVS | | | 61.10 329 | 60.81 329 | 61.99 335 | 65.96 366 | 55.86 279 | 53.10 359 | 58.97 359 | 67.06 237 | 56.89 364 | 63.33 361 | 40.98 346 | 67.03 344 | 54.79 302 | 86.18 292 | 63.08 355 |
|
PMMVS | | | 61.65 325 | 60.38 330 | 65.47 328 | 65.40 367 | 69.26 152 | 63.97 341 | 61.73 355 | 36.80 363 | 60.11 355 | 68.43 354 | 59.42 260 | 66.35 348 | 48.97 326 | 78.57 337 | 60.81 357 |
|
PMMVS2 | | | 55.64 337 | 59.27 334 | 44.74 352 | 64.30 368 | 12.32 370 | 40.60 362 | 49.79 368 | 53.19 320 | 65.06 347 | 84.81 274 | 53.60 290 | 49.76 364 | 32.68 360 | 89.41 257 | 72.15 347 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 40.22 23 | 51.82 340 | 50.47 343 | 55.87 345 | 62.66 369 | 51.91 307 | 31.61 364 | 39.28 370 | 40.65 359 | 50.76 366 | 74.98 347 | 56.24 279 | 44.67 366 | 33.94 358 | 64.11 360 | 71.04 350 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | 24.13 355 | 32.95 370 | 29.49 364 | | 21.63 373 | 12.07 365 | 37.95 367 | 45.07 364 | 30.84 362 | 19.21 367 | 17.94 365 | 33.06 365 | 23.69 364 |
|
tmp_tt | | | 20.25 345 | 24.50 346 | 7.49 356 | 4.47 371 | 8.70 371 | 34.17 363 | 25.16 372 | 1.00 366 | 32.43 368 | 18.49 365 | 39.37 352 | 9.21 368 | 21.64 364 | 43.75 363 | 4.57 365 |
|
testmvs | | | 5.91 349 | 7.65 350 | 0.72 358 | 1.20 372 | 0.37 373 | 59.14 349 | 0.67 375 | 0.49 368 | 1.11 369 | 2.76 369 | 0.94 375 | 0.24 370 | 1.02 367 | 1.47 366 | 1.55 367 |
|
test123 | | | 6.27 348 | 8.08 349 | 0.84 357 | 1.11 373 | 0.57 372 | 62.90 342 | 0.82 374 | 0.54 367 | 1.07 370 | 2.75 370 | 1.26 374 | 0.30 369 | 1.04 366 | 1.26 368 | 1.66 366 |
|
cdsmvs_eth3d_5k | | | 20.81 344 | 27.75 345 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 85.44 221 | 0.00 369 | 0.00 371 | 82.82 299 | 81.46 94 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
pcd_1.5k_mvsjas | | | 6.41 347 | 8.55 348 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 76.94 135 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
sosnet-low-res | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
sosnet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
uncertanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
Regformer | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
ab-mvs-re | | | 6.65 346 | 8.87 347 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 79.80 328 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
uanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
GSMVS | | | | | | | | | | | | | | | | | 83.88 285 |
|
test_part1 | | | | | 0.00 359 | | 0.00 374 | 0.00 365 | 93.93 26 | | | | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
sam_mvs1 | | | | | | | | | | | | | 46.11 308 | | | | 83.88 285 |
|
sam_mvs | | | | | | | | | | | | | 45.92 313 | | | | |
|
MTGPA | ![Method available as binary. binary](img/icon_binary.png) | | | | | | | | 91.81 95 | | | | | | | | |
|
test_post1 | | | | | | | | 78.85 266 | | | | 3.13 367 | 45.19 324 | 80.13 312 | 58.11 283 | | |
|
test_post | | | | | | | | | | | | 3.10 368 | 45.43 320 | 77.22 318 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 81.71 316 | 45.93 312 | 87.01 252 | | | |
|
MTMP | | | | | | | | 90.66 36 | 33.14 371 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 80.83 91 | 96.45 88 | 90.57 202 |
|
agg_prior2 | | | | | | | | | | | | | | | 79.68 109 | 96.16 98 | 90.22 210 |
|
test_prior4 | | | | | | | 78.97 73 | 84.59 134 | | | | | | | | | |
|
test_prior2 | | | | | | | | 83.37 170 | | 75.43 142 | 84.58 190 | 91.57 147 | 81.92 89 | | 79.54 111 | 96.97 70 | |
|
旧先验2 | | | | | | | | 81.73 216 | | 56.88 305 | 86.54 162 | | | 84.90 286 | 72.81 171 | | |
|
新几何2 | | | | | | | | 81.72 217 | | | | | | | | | |
|
无先验 | | | | | | | | 82.81 186 | 85.62 220 | 58.09 297 | | | | 91.41 174 | 67.95 209 | | 84.48 277 |
|
原ACMM2 | | | | | | | | 82.26 204 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 86.43 270 | 63.52 237 | | |
|
segment_acmp | | | | | | | | | | | | | 81.94 86 | | | | |
|
testdata1 | | | | | | | | 79.62 245 | | 73.95 158 | | | | | | | |
|
plane_prior5 | | | | | | | | | 93.61 34 | | | | | 95.22 40 | 80.78 92 | 95.83 114 | 94.46 89 |
|
plane_prior4 | | | | | | | | | | | | 92.95 112 | | | | | |
|
plane_prior3 | | | | | | | 76.85 94 | | | 77.79 104 | 86.55 157 | | | | | | |
|
plane_prior2 | | | | | | | | 89.45 58 | | 79.44 82 | | | | | | | |
|
plane_prior | | | | | | | 76.42 99 | 87.15 95 | | 75.94 134 | | | | | | 95.03 138 | |
|
n2 | | | | | | | | | 0.00 376 | | | | | | | | |
|
nn | | | | | | | | | 0.00 376 | | | | | | | | |
|
door-mid | | | | | | | | | 74.45 290 | | | | | | | | |
|
test11 | | | | | | | | | 91.46 108 | | | | | | | | |
|
door | | | | | | | | | 72.57 306 | | | | | | | | |
|
HQP5-MVS | | | | | | | 70.66 143 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.30 134 | | |
|
HQP4-MVS | | | | | | | | | | | 80.56 255 | | | 94.61 56 | | | 93.56 121 |
|
HQP3-MVS | | | | | | | | | 92.68 70 | | | | | | | 94.47 157 | |
|
HQP2-MVS | | | | | | | | | | | | | 72.10 206 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 27.60 365 | 70.76 321 | | 46.47 351 | 61.27 352 | | 45.20 323 | | 49.18 325 | | 83.75 289 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 95.74 118 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 97.35 61 | |
|
Test By Simon | | | | | | | | | | | | | 79.09 112 | | | | |
|