region2R | | | 94.43 16 | 94.27 17 | 94.92 12 | 98.65 1 | 86.67 24 | 96.92 14 | 97.23 23 | 88.60 59 | 93.58 29 | 97.27 14 | 85.22 39 | 99.54 10 | 92.21 31 | 98.74 18 | 98.56 10 |
|
ACMMPR | | | 94.43 16 | 94.28 16 | 94.91 13 | 98.63 2 | 86.69 22 | 96.94 10 | 97.32 17 | 88.63 57 | 93.53 32 | 97.26 16 | 85.04 42 | 99.54 10 | 92.35 29 | 98.78 13 | 98.50 11 |
|
HFP-MVS | | | 94.52 12 | 94.40 13 | 94.86 15 | 98.61 3 | 86.81 17 | 96.94 10 | 97.34 12 | 88.63 57 | 93.65 25 | 97.21 19 | 86.10 29 | 99.49 16 | 92.35 29 | 98.77 14 | 98.30 26 |
|
#test# | | | 94.32 21 | 94.14 22 | 94.86 15 | 98.61 3 | 86.81 17 | 96.43 23 | 97.34 12 | 87.51 85 | 93.65 25 | 97.21 19 | 86.10 29 | 99.49 16 | 91.68 48 | 98.77 14 | 98.30 26 |
|
test_part2 | | | | | | 98.55 5 | 87.22 12 | | | | 96.40 4 | | | | | | |
|
v1.0 | | | 39.85 337 | 53.14 332 | 0.00 355 | 98.55 5 | 0.00 370 | 0.00 361 | 97.45 7 | 88.25 67 | 96.40 4 | 97.60 6 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
XVS | | | 94.45 14 | 94.32 14 | 94.85 17 | 98.54 7 | 86.60 27 | 96.93 12 | 97.19 24 | 90.66 22 | 92.85 39 | 97.16 24 | 85.02 43 | 99.49 16 | 91.99 39 | 98.56 35 | 98.47 14 |
|
X-MVStestdata | | | 88.31 141 | 86.13 189 | 94.85 17 | 98.54 7 | 86.60 27 | 96.93 12 | 97.19 24 | 90.66 22 | 92.85 39 | 23.41 361 | 85.02 43 | 99.49 16 | 91.99 39 | 98.56 35 | 98.47 14 |
|
mPP-MVS | | | 93.99 29 | 93.78 31 | 94.63 30 | 98.50 9 | 85.90 48 | 96.87 16 | 96.91 43 | 88.70 55 | 91.83 67 | 97.17 23 | 83.96 52 | 99.55 7 | 91.44 53 | 98.64 31 | 98.43 20 |
|
HSP-MVS | | | 95.30 4 | 95.48 3 | 94.76 25 | 98.49 10 | 86.52 29 | 96.91 15 | 96.73 56 | 91.73 9 | 96.10 7 | 96.69 39 | 89.90 2 | 99.30 29 | 94.70 3 | 98.04 49 | 98.45 18 |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 94.25 22 | 94.07 25 | 94.77 24 | 98.47 11 | 86.31 37 | 96.71 20 | 96.98 35 | 89.04 47 | 91.98 63 | 97.19 21 | 85.43 37 | 99.56 2 | 92.06 38 | 98.79 11 | 98.44 19 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
MCST-MVS | | | 94.45 14 | 94.20 21 | 95.19 7 | 98.46 12 | 87.50 9 | 95.00 91 | 97.12 28 | 87.13 92 | 92.51 53 | 96.30 55 | 89.24 7 | 99.34 23 | 93.46 14 | 98.62 32 | 98.73 4 |
|
PGM-MVS | | | 93.96 30 | 93.72 33 | 94.68 28 | 98.43 13 | 86.22 40 | 95.30 65 | 97.78 1 | 87.45 86 | 93.26 33 | 97.33 12 | 84.62 47 | 99.51 14 | 90.75 62 | 98.57 34 | 98.32 25 |
|
zzz-MVS | | | 94.47 13 | 94.30 15 | 95.00 10 | 98.42 14 | 86.95 13 | 95.06 87 | 96.97 36 | 91.07 14 | 93.14 37 | 97.56 7 | 84.30 49 | 99.56 2 | 93.43 15 | 98.75 16 | 98.47 14 |
|
MTAPA | | | 94.42 18 | 94.22 18 | 95.00 10 | 98.42 14 | 86.95 13 | 94.36 144 | 96.97 36 | 91.07 14 | 93.14 37 | 97.56 7 | 84.30 49 | 99.56 2 | 93.43 15 | 98.75 16 | 98.47 14 |
|
HPM-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 94.02 28 | 93.88 28 | 94.43 38 | 98.39 16 | 85.78 50 | 97.25 5 | 97.07 32 | 86.90 103 | 92.62 50 | 96.80 36 | 84.85 46 | 99.17 35 | 92.43 26 | 98.65 30 | 98.33 24 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
CP-MVS | | | 94.34 19 | 94.21 20 | 94.74 27 | 98.39 16 | 86.64 26 | 97.60 1 | 97.24 21 | 88.53 61 | 92.73 46 | 97.23 17 | 85.20 40 | 99.32 27 | 92.15 35 | 98.83 10 | 98.25 34 |
|
ESAPD | | | 95.57 1 | 95.67 1 | 95.25 6 | 98.36 18 | 87.28 11 | 95.56 59 | 97.51 4 | 89.13 45 | 97.14 2 | 97.91 3 | 91.64 1 | 99.62 1 | 94.61 5 | 99.17 2 | 98.86 2 |
|
HPM-MVS_fast | | | 93.40 42 | 93.22 40 | 93.94 49 | 98.36 18 | 84.83 58 | 97.15 7 | 96.80 51 | 85.77 121 | 92.47 54 | 97.13 25 | 82.38 62 | 99.07 44 | 90.51 64 | 98.40 39 | 97.92 59 |
|
DP-MVS Recon | | | 91.95 61 | 91.28 64 | 93.96 48 | 98.33 20 | 85.92 45 | 94.66 118 | 96.66 64 | 82.69 204 | 90.03 91 | 95.82 75 | 82.30 64 | 99.03 50 | 84.57 125 | 96.48 77 | 96.91 97 |
|
APDe-MVS | | | 95.46 2 | 95.64 2 | 94.91 13 | 98.26 21 | 86.29 39 | 97.46 2 | 97.40 10 | 89.03 48 | 96.20 6 | 98.10 1 | 89.39 6 | 99.34 23 | 95.88 1 | 99.03 3 | 99.10 1 |
|
TSAR-MVS + MP. | | | 94.85 9 | 94.94 8 | 94.58 32 | 98.25 22 | 86.33 35 | 96.11 34 | 96.62 67 | 88.14 70 | 96.10 7 | 96.96 29 | 89.09 8 | 98.94 65 | 94.48 6 | 98.68 24 | 98.48 13 |
|
HPM-MVS++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 95.14 7 | 94.91 9 | 95.83 1 | 98.25 22 | 89.65 1 | 95.92 42 | 96.96 39 | 91.75 8 | 94.02 21 | 96.83 33 | 88.12 11 | 99.55 7 | 93.41 17 | 98.94 5 | 98.28 28 |
|
CPTT-MVS | | | 91.99 60 | 91.80 59 | 92.55 90 | 98.24 24 | 81.98 130 | 96.76 19 | 96.49 73 | 81.89 220 | 90.24 87 | 96.44 52 | 78.59 105 | 98.61 86 | 89.68 69 | 97.85 53 | 97.06 92 |
|
MP-MVS-pluss | | | 94.21 25 | 94.00 27 | 94.85 17 | 98.17 25 | 86.65 25 | 94.82 103 | 97.17 26 | 86.26 113 | 92.83 41 | 97.87 4 | 85.57 36 | 99.56 2 | 94.37 8 | 98.92 6 | 98.34 23 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
SMA-MVS | | | 95.20 5 | 95.07 7 | 95.59 2 | 98.14 26 | 88.48 4 | 96.26 28 | 97.28 20 | 85.90 118 | 97.67 1 | 98.10 1 | 88.41 10 | 99.56 2 | 94.66 4 | 99.19 1 | 98.71 5 |
|
CNVR-MVS | | | 95.40 3 | 95.37 4 | 95.50 4 | 98.11 27 | 88.51 3 | 95.29 67 | 96.96 39 | 92.09 3 | 95.32 11 | 97.08 26 | 89.49 5 | 99.33 26 | 95.10 2 | 98.85 8 | 98.66 6 |
|
114514_t | | | 89.51 110 | 88.50 118 | 92.54 91 | 98.11 27 | 81.99 129 | 95.16 80 | 96.36 82 | 70.19 330 | 85.81 156 | 95.25 89 | 76.70 121 | 98.63 84 | 82.07 161 | 96.86 68 | 97.00 94 |
|
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 93.24 48 | 92.88 49 | 94.30 42 | 98.09 29 | 85.33 54 | 96.86 17 | 97.45 7 | 88.33 64 | 90.15 89 | 97.03 27 | 81.44 77 | 99.51 14 | 90.85 61 | 95.74 84 | 98.04 49 |
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 |
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 94.24 23 | 94.07 25 | 94.75 26 | 98.06 30 | 86.90 16 | 95.88 43 | 96.94 41 | 85.68 124 | 95.05 13 | 97.18 22 | 87.31 19 | 99.07 44 | 91.90 46 | 98.61 33 | 98.28 28 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
CSCG | | | 93.23 49 | 93.05 43 | 93.76 56 | 98.04 31 | 84.07 78 | 96.22 29 | 97.37 11 | 84.15 157 | 90.05 90 | 95.66 80 | 87.77 13 | 99.15 38 | 89.91 67 | 98.27 42 | 98.07 46 |
|
ACMMP_Plus | | | 94.74 11 | 94.56 12 | 95.28 5 | 98.02 32 | 87.70 5 | 95.68 51 | 97.34 12 | 88.28 66 | 95.30 12 | 97.67 5 | 85.90 33 | 99.54 10 | 93.91 11 | 98.95 4 | 98.60 8 |
|
APD-MVS_3200maxsize | | | 93.78 33 | 93.77 32 | 93.80 55 | 97.92 33 | 84.19 76 | 96.30 26 | 96.87 47 | 86.96 99 | 93.92 23 | 97.47 9 | 83.88 53 | 98.96 64 | 92.71 24 | 97.87 52 | 98.26 33 |
|
NCCC | | | 94.81 10 | 94.69 11 | 95.17 8 | 97.83 34 | 87.46 10 | 95.66 53 | 96.93 42 | 92.34 2 | 93.94 22 | 96.58 46 | 87.74 14 | 99.44 20 | 92.83 22 | 98.40 39 | 98.62 7 |
|
CDPH-MVS | | | 92.83 53 | 92.30 55 | 94.44 36 | 97.79 35 | 86.11 43 | 94.06 170 | 96.66 64 | 80.09 247 | 92.77 43 | 96.63 43 | 86.62 25 | 99.04 49 | 87.40 94 | 98.66 28 | 98.17 38 |
|
DP-MVS | | | 87.25 189 | 85.36 210 | 92.90 78 | 97.65 36 | 83.24 97 | 94.81 104 | 92.00 262 | 74.99 295 | 81.92 254 | 95.00 96 | 72.66 186 | 99.05 46 | 66.92 305 | 92.33 142 | 96.40 107 |
|
PAPM_NR | | | 91.22 75 | 90.78 76 | 92.52 92 | 97.60 37 | 81.46 140 | 94.37 140 | 96.24 88 | 86.39 111 | 87.41 129 | 94.80 104 | 82.06 71 | 98.48 92 | 82.80 149 | 95.37 91 | 97.61 70 |
|
TEST9 | | | | | | 97.53 38 | 86.49 30 | 94.07 167 | 96.78 52 | 81.61 233 | 92.77 43 | 96.20 60 | 87.71 15 | 99.12 41 | | | |
|
train_agg | | | 93.44 40 | 93.08 42 | 94.52 34 | 97.53 38 | 86.49 30 | 94.07 167 | 96.78 52 | 81.86 228 | 92.77 43 | 96.20 60 | 87.63 16 | 99.12 41 | 92.14 36 | 98.69 21 | 97.94 55 |
|
abl_6 | | | 93.18 50 | 93.05 43 | 93.57 59 | 97.52 40 | 84.27 75 | 95.53 60 | 96.67 63 | 87.85 76 | 93.20 35 | 97.22 18 | 80.35 85 | 99.18 34 | 91.91 43 | 97.21 62 | 97.26 80 |
|
test_8 | | | | | | 97.49 41 | 86.30 38 | 94.02 173 | 96.76 55 | 81.86 228 | 92.70 47 | 96.20 60 | 87.63 16 | 99.02 53 | | | |
|
agg_prior3 | | | 93.27 45 | 92.89 48 | 94.40 40 | 97.49 41 | 86.12 42 | 94.07 167 | 96.73 56 | 81.46 236 | 92.46 55 | 96.05 68 | 86.90 23 | 99.15 38 | 92.14 36 | 98.69 21 | 97.94 55 |
|
DeepC-MVS_fast | | 89.43 2 | 94.04 27 | 93.79 30 | 94.80 23 | 97.48 43 | 86.78 19 | 95.65 56 | 96.89 44 | 89.40 38 | 92.81 42 | 96.97 28 | 85.37 38 | 99.24 31 | 90.87 60 | 98.69 21 | 98.38 22 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 89.89 103 | 89.07 106 | 92.37 99 | 97.41 44 | 83.03 103 | 94.42 133 | 95.92 111 | 82.81 200 | 86.34 149 | 94.65 108 | 73.89 169 | 99.02 53 | 80.69 181 | 95.51 87 | 95.05 150 |
|
agg_prior1 | | | 93.29 44 | 92.97 46 | 94.26 43 | 97.38 45 | 85.92 45 | 93.92 178 | 96.72 58 | 81.96 215 | 92.16 59 | 96.23 58 | 87.85 12 | 98.97 61 | 91.95 42 | 98.55 37 | 97.90 60 |
|
agg_prior | | | | | | 97.38 45 | 85.92 45 | | 96.72 58 | | 92.16 59 | | | 98.97 61 | | | |
|
原ACMM1 | | | | | 92.01 110 | 97.34 47 | 81.05 152 | | 96.81 50 | 78.89 257 | 90.45 85 | 95.92 71 | 82.65 60 | 98.84 75 | 80.68 182 | 98.26 43 | 96.14 114 |
|
MSLP-MVS++ | | | 93.72 34 | 94.08 24 | 92.65 86 | 97.31 48 | 83.43 93 | 95.79 46 | 97.33 15 | 90.03 27 | 93.58 29 | 96.96 29 | 84.87 45 | 97.76 144 | 92.19 33 | 98.66 28 | 96.76 100 |
|
新几何1 | | | | | 93.10 68 | 97.30 49 | 84.35 74 | | 95.56 138 | 71.09 327 | 91.26 76 | 96.24 57 | 82.87 59 | 98.86 70 | 79.19 212 | 98.10 47 | 96.07 120 |
|
test_prior3 | | | 93.60 37 | 93.53 36 | 93.82 52 | 97.29 50 | 84.49 65 | 94.12 158 | 96.88 45 | 87.67 82 | 92.63 48 | 96.39 53 | 86.62 25 | 98.87 67 | 91.50 50 | 98.67 26 | 98.11 44 |
|
test_prior | | | | | 93.82 52 | 97.29 50 | 84.49 65 | | 96.88 45 | | | | | 98.87 67 | | | 98.11 44 |
|
1121 | | | 90.42 91 | 89.49 95 | 93.20 63 | 97.27 52 | 84.46 68 | 92.63 235 | 95.51 145 | 71.01 328 | 91.20 77 | 96.21 59 | 82.92 58 | 99.05 46 | 80.56 184 | 98.07 48 | 96.10 118 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 84.53 7 | 89.06 125 | 88.03 131 | 92.15 107 | 97.27 52 | 82.69 118 | 94.29 145 | 95.44 155 | 79.71 251 | 84.01 221 | 94.18 123 | 76.68 122 | 98.75 79 | 77.28 229 | 93.41 126 | 95.02 151 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
SD-MVS | | | 94.96 8 | 95.33 5 | 93.88 50 | 97.25 54 | 86.69 22 | 96.19 30 | 97.11 30 | 90.42 24 | 96.95 3 | 97.27 14 | 89.53 4 | 96.91 224 | 94.38 7 | 98.85 8 | 98.03 50 |
|
test12 | | | | | 94.34 41 | 97.13 55 | 86.15 41 | | 96.29 84 | | 91.04 79 | | 85.08 41 | 99.01 55 | | 98.13 46 | 97.86 61 |
|
MG-MVS | | | 91.77 63 | 91.70 60 | 92.00 112 | 97.08 56 | 80.03 178 | 93.60 199 | 95.18 175 | 87.85 76 | 90.89 80 | 96.47 51 | 82.06 71 | 98.36 97 | 85.07 117 | 97.04 65 | 97.62 69 |
|
SteuartSystems-ACMMP | | | 95.20 5 | 95.32 6 | 94.85 17 | 96.99 57 | 86.33 35 | 97.33 3 | 97.30 18 | 91.38 12 | 95.39 10 | 97.46 10 | 88.98 9 | 99.40 21 | 94.12 9 | 98.89 7 | 98.82 3 |
Skip Steuart: Steuart Systems R&D Blog. |
MVS_111021_HR | | | 93.45 39 | 93.31 38 | 93.84 51 | 96.99 57 | 84.84 57 | 93.24 215 | 97.24 21 | 88.76 54 | 91.60 71 | 95.85 74 | 86.07 31 | 98.66 81 | 91.91 43 | 98.16 45 | 98.03 50 |
|
CNLPA | | | 89.07 124 | 87.98 132 | 92.34 100 | 96.87 59 | 84.78 59 | 94.08 165 | 93.24 236 | 81.41 237 | 84.46 208 | 95.13 94 | 75.57 146 | 96.62 240 | 77.21 230 | 93.84 117 | 95.61 138 |
|
PHI-MVS | | | 93.89 32 | 93.65 34 | 94.62 31 | 96.84 60 | 86.43 32 | 96.69 21 | 97.49 5 | 85.15 136 | 93.56 31 | 96.28 56 | 85.60 35 | 99.31 28 | 92.45 25 | 98.79 11 | 98.12 43 |
|
旧先验1 | | | | | | 96.79 61 | 81.81 132 | | 95.67 130 | | | 96.81 34 | 86.69 24 | | | 97.66 56 | 96.97 95 |
|
LFMVS | | | 90.08 96 | 89.13 105 | 92.95 76 | 96.71 62 | 82.32 125 | 96.08 35 | 89.91 316 | 86.79 104 | 92.15 61 | 96.81 34 | 62.60 290 | 98.34 100 | 87.18 98 | 93.90 115 | 98.19 37 |
|
Anonymous202405211 | | | 87.68 164 | 86.13 189 | 92.31 101 | 96.66 63 | 80.74 162 | 94.87 100 | 91.49 280 | 80.47 244 | 89.46 96 | 95.44 83 | 54.72 326 | 98.23 105 | 82.19 159 | 89.89 172 | 97.97 53 |
|
TAPA-MVS | | 84.62 6 | 88.16 145 | 87.01 156 | 91.62 129 | 96.64 64 | 80.65 163 | 94.39 136 | 96.21 92 | 76.38 281 | 86.19 152 | 95.44 83 | 79.75 92 | 98.08 128 | 62.75 326 | 95.29 94 | 96.13 115 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MAR-MVS | | | 90.30 92 | 89.37 99 | 93.07 72 | 96.61 65 | 84.48 67 | 95.68 51 | 95.67 130 | 82.36 208 | 87.85 118 | 92.85 170 | 76.63 123 | 98.80 77 | 80.01 194 | 96.68 71 | 95.91 125 |
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 |
VNet | | | 92.24 59 | 91.91 58 | 93.24 62 | 96.59 66 | 83.43 93 | 94.84 102 | 96.44 74 | 89.19 43 | 94.08 20 | 95.90 72 | 77.85 115 | 98.17 110 | 88.90 75 | 93.38 127 | 98.13 42 |
|
TSAR-MVS + GP. | | | 93.66 36 | 93.41 37 | 94.41 39 | 96.59 66 | 86.78 19 | 94.40 134 | 93.93 226 | 89.77 32 | 94.21 17 | 95.59 82 | 87.35 18 | 98.61 86 | 92.72 23 | 96.15 80 | 97.83 63 |
|
test222 | | | | | | 96.55 68 | 81.70 133 | 92.22 248 | 95.01 182 | 68.36 334 | 90.20 88 | 96.14 65 | 80.26 88 | | | 97.80 54 | 96.05 122 |
|
Anonymous20240529 | | | 88.09 148 | 86.59 179 | 92.58 89 | 96.53 69 | 81.92 131 | 95.99 38 | 95.84 119 | 74.11 303 | 89.06 101 | 95.21 91 | 61.44 298 | 98.81 76 | 83.67 139 | 87.47 212 | 97.01 93 |
|
Anonymous20231211 | | | 86.59 207 | 85.13 213 | 90.98 153 | 96.52 70 | 81.50 136 | 96.14 32 | 96.16 93 | 73.78 305 | 83.65 229 | 92.15 194 | 63.26 288 | 97.37 187 | 82.82 148 | 81.74 272 | 94.06 206 |
|
DeepPCF-MVS | | 89.96 1 | 94.20 26 | 94.77 10 | 92.49 93 | 96.52 70 | 80.00 179 | 94.00 175 | 97.08 31 | 90.05 26 | 95.65 9 | 97.29 13 | 89.66 3 | 98.97 61 | 93.95 10 | 98.71 19 | 98.50 11 |
|
testdata | | | | | 90.49 169 | 96.40 72 | 77.89 251 | | 95.37 161 | 72.51 317 | 93.63 27 | 96.69 39 | 82.08 69 | 97.65 149 | 83.08 142 | 97.39 60 | 95.94 124 |
|
PVSNet_Blended_VisFu | | | 91.38 71 | 90.91 73 | 92.80 81 | 96.39 73 | 83.17 99 | 94.87 100 | 96.66 64 | 83.29 180 | 89.27 97 | 94.46 113 | 80.29 87 | 99.17 35 | 87.57 92 | 95.37 91 | 96.05 122 |
|
API-MVS | | | 90.66 84 | 90.07 86 | 92.45 95 | 96.36 74 | 84.57 63 | 96.06 36 | 95.22 174 | 82.39 206 | 89.13 98 | 94.27 121 | 80.32 86 | 98.46 93 | 80.16 193 | 96.71 70 | 94.33 194 |
|
F-COLMAP | | | 87.95 154 | 86.80 163 | 91.40 135 | 96.35 75 | 80.88 158 | 94.73 109 | 95.45 153 | 79.65 252 | 82.04 252 | 94.61 109 | 71.13 203 | 98.50 91 | 76.24 240 | 91.05 154 | 94.80 169 |
|
VDD-MVS | | | 90.74 81 | 89.92 91 | 93.20 63 | 96.27 76 | 83.02 105 | 95.73 48 | 93.86 227 | 88.42 63 | 92.53 51 | 96.84 32 | 62.09 293 | 98.64 83 | 90.95 59 | 92.62 139 | 97.93 58 |
|
OMC-MVS | | | 91.23 74 | 90.62 77 | 93.08 70 | 96.27 76 | 84.07 78 | 93.52 201 | 95.93 110 | 86.95 100 | 89.51 94 | 96.13 66 | 78.50 107 | 98.35 99 | 85.84 112 | 92.90 136 | 96.83 99 |
|
view600 | | | 87.62 173 | 86.65 172 | 90.53 161 | 96.19 78 | 78.52 230 | 95.29 67 | 91.09 287 | 87.08 94 | 87.84 119 | 93.03 164 | 68.86 240 | 98.11 116 | 69.44 286 | 91.02 156 | 94.96 156 |
|
view800 | | | 87.62 173 | 86.65 172 | 90.53 161 | 96.19 78 | 78.52 230 | 95.29 67 | 91.09 287 | 87.08 94 | 87.84 119 | 93.03 164 | 68.86 240 | 98.11 116 | 69.44 286 | 91.02 156 | 94.96 156 |
|
conf0.05thres1000 | | | 87.62 173 | 86.65 172 | 90.53 161 | 96.19 78 | 78.52 230 | 95.29 67 | 91.09 287 | 87.08 94 | 87.84 119 | 93.03 164 | 68.86 240 | 98.11 116 | 69.44 286 | 91.02 156 | 94.96 156 |
|
tfpn | | | 87.62 173 | 86.65 172 | 90.53 161 | 96.19 78 | 78.52 230 | 95.29 67 | 91.09 287 | 87.08 94 | 87.84 119 | 93.03 164 | 68.86 240 | 98.11 116 | 69.44 286 | 91.02 156 | 94.96 156 |
|
CHOSEN 1792x2688 | | | 88.84 130 | 87.69 136 | 92.30 102 | 96.14 82 | 81.42 142 | 90.01 280 | 95.86 118 | 74.52 300 | 87.41 129 | 93.94 132 | 75.46 148 | 98.36 97 | 80.36 188 | 95.53 86 | 97.12 89 |
|
tfpn111 | | | 87.63 170 | 86.68 170 | 90.47 171 | 96.12 83 | 78.55 226 | 95.03 88 | 91.58 273 | 87.15 89 | 88.06 113 | 92.29 189 | 68.91 236 | 98.15 113 | 69.88 284 | 91.10 148 | 94.71 171 |
|
conf200view11 | | | 87.65 166 | 86.71 167 | 90.46 173 | 96.12 83 | 78.55 226 | 95.03 88 | 91.58 273 | 87.15 89 | 88.06 113 | 92.29 189 | 68.91 236 | 98.10 120 | 70.13 279 | 91.10 148 | 94.71 171 |
|
thres100view900 | | | 87.63 170 | 86.71 167 | 90.38 177 | 96.12 83 | 78.55 226 | 95.03 88 | 91.58 273 | 87.15 89 | 88.06 113 | 92.29 189 | 68.91 236 | 98.10 120 | 70.13 279 | 91.10 148 | 94.48 190 |
|
PVSNet_BlendedMVS | | | 89.98 98 | 89.70 92 | 90.82 155 | 96.12 83 | 81.25 145 | 93.92 178 | 96.83 48 | 83.49 174 | 89.10 99 | 92.26 192 | 81.04 81 | 98.85 73 | 86.72 107 | 87.86 210 | 92.35 282 |
|
PVSNet_Blended | | | 90.73 82 | 90.32 81 | 91.98 113 | 96.12 83 | 81.25 145 | 92.55 239 | 96.83 48 | 82.04 214 | 89.10 99 | 92.56 180 | 81.04 81 | 98.85 73 | 86.72 107 | 95.91 82 | 95.84 129 |
|
UA-Net | | | 92.83 53 | 92.54 53 | 93.68 57 | 96.10 88 | 84.71 60 | 95.66 53 | 96.39 80 | 91.92 4 | 93.22 34 | 96.49 50 | 83.16 56 | 98.87 67 | 84.47 126 | 95.47 89 | 97.45 76 |
|
thres600view7 | | | 87.65 166 | 86.67 171 | 90.59 158 | 96.08 89 | 78.72 221 | 94.88 99 | 91.58 273 | 87.06 98 | 88.08 112 | 92.30 188 | 68.91 236 | 98.10 120 | 70.05 283 | 91.10 148 | 94.96 156 |
|
DeepC-MVS | | 88.79 3 | 93.31 43 | 92.99 45 | 94.26 43 | 96.07 90 | 85.83 49 | 94.89 97 | 96.99 34 | 89.02 49 | 89.56 93 | 97.37 11 | 82.51 61 | 99.38 22 | 92.20 32 | 98.30 41 | 97.57 72 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
LS3D | | | 87.89 155 | 86.32 185 | 92.59 88 | 96.07 90 | 82.92 109 | 95.23 75 | 94.92 189 | 75.66 288 | 82.89 240 | 95.98 69 | 72.48 190 | 99.21 32 | 68.43 296 | 95.23 96 | 95.64 137 |
|
HyFIR lowres test | | | 88.09 148 | 86.81 162 | 91.93 116 | 96.00 92 | 80.63 164 | 90.01 280 | 95.79 123 | 73.42 308 | 87.68 127 | 92.10 199 | 73.86 170 | 97.96 135 | 80.75 180 | 91.70 144 | 97.19 85 |
|
tfpn200view9 | | | 87.58 179 | 86.64 176 | 90.41 174 | 95.99 93 | 78.64 223 | 94.58 121 | 91.98 264 | 86.94 101 | 88.09 110 | 91.77 210 | 69.18 233 | 98.10 120 | 70.13 279 | 91.10 148 | 94.48 190 |
|
thres400 | | | 87.62 173 | 86.64 176 | 90.57 159 | 95.99 93 | 78.64 223 | 94.58 121 | 91.98 264 | 86.94 101 | 88.09 110 | 91.77 210 | 69.18 233 | 98.10 120 | 70.13 279 | 91.10 148 | 94.96 156 |
|
MVS_111021_LR | | | 92.47 56 | 92.29 56 | 92.98 75 | 95.99 93 | 84.43 72 | 93.08 220 | 96.09 98 | 88.20 69 | 91.12 78 | 95.72 79 | 81.33 79 | 97.76 144 | 91.74 47 | 97.37 61 | 96.75 101 |
|
PatchMatch-RL | | | 86.77 203 | 85.54 203 | 90.47 171 | 95.88 96 | 82.71 117 | 90.54 273 | 92.31 252 | 79.82 250 | 84.32 215 | 91.57 220 | 68.77 244 | 96.39 253 | 73.16 263 | 93.48 125 | 92.32 283 |
|
EPP-MVSNet | | | 91.70 67 | 91.56 61 | 92.13 109 | 95.88 96 | 80.50 169 | 97.33 3 | 95.25 168 | 86.15 115 | 89.76 92 | 95.60 81 | 83.42 55 | 98.32 102 | 87.37 96 | 93.25 130 | 97.56 73 |
|
IS-MVSNet | | | 91.43 70 | 91.09 70 | 92.46 94 | 95.87 98 | 81.38 143 | 96.95 9 | 93.69 231 | 89.72 34 | 89.50 95 | 95.98 69 | 78.57 106 | 97.77 143 | 83.02 144 | 96.50 76 | 98.22 35 |
|
PAPR | | | 90.02 97 | 89.27 103 | 92.29 103 | 95.78 99 | 80.95 156 | 92.68 234 | 96.22 89 | 81.91 218 | 86.66 142 | 93.75 144 | 82.23 65 | 98.44 95 | 79.40 211 | 94.79 98 | 97.48 75 |
|
Vis-MVSNet (Re-imp) | | | 89.59 108 | 89.44 97 | 90.03 199 | 95.74 100 | 75.85 280 | 95.61 57 | 90.80 300 | 87.66 84 | 87.83 123 | 95.40 86 | 76.79 120 | 96.46 250 | 78.37 217 | 96.73 69 | 97.80 64 |
|
0601test | | | 90.69 83 | 90.02 90 | 92.71 84 | 95.72 101 | 82.41 124 | 94.11 160 | 95.12 177 | 85.63 125 | 91.49 72 | 94.70 105 | 74.75 156 | 98.42 96 | 86.13 111 | 92.53 140 | 97.31 79 |
|
MVS_0304 | | | 93.25 47 | 92.62 51 | 95.14 9 | 95.72 101 | 87.58 8 | 94.71 114 | 96.59 69 | 91.78 7 | 91.46 73 | 96.18 64 | 75.45 149 | 99.55 7 | 93.53 12 | 98.19 44 | 98.28 28 |
|
canonicalmvs | | | 93.27 45 | 92.75 50 | 94.85 17 | 95.70 103 | 87.66 6 | 96.33 25 | 96.41 77 | 90.00 28 | 94.09 19 | 94.60 110 | 82.33 63 | 98.62 85 | 92.40 28 | 92.86 137 | 98.27 31 |
|
CANet | | | 93.54 38 | 93.20 41 | 94.55 33 | 95.65 104 | 85.73 51 | 94.94 94 | 96.69 62 | 91.89 5 | 90.69 82 | 95.88 73 | 81.99 73 | 99.54 10 | 93.14 20 | 97.95 51 | 98.39 21 |
|
3Dnovator+ | | 87.14 4 | 92.42 58 | 91.37 62 | 95.55 3 | 95.63 105 | 88.73 2 | 97.07 8 | 96.77 54 | 90.84 17 | 84.02 220 | 96.62 44 | 75.95 138 | 99.34 23 | 87.77 89 | 97.68 55 | 98.59 9 |
|
alignmvs | | | 93.08 51 | 92.50 54 | 94.81 22 | 95.62 106 | 87.61 7 | 95.99 38 | 96.07 100 | 89.77 32 | 94.12 18 | 94.87 99 | 80.56 83 | 98.66 81 | 92.42 27 | 93.10 133 | 98.15 40 |
|
tfpn1000 | | | 86.06 216 | 84.92 220 | 89.49 223 | 95.54 107 | 77.79 254 | 94.72 112 | 89.07 330 | 82.05 212 | 85.36 188 | 91.94 206 | 68.32 259 | 96.65 238 | 67.04 302 | 90.24 166 | 94.02 209 |
|
Regformer-1 | | | 94.22 24 | 94.13 23 | 94.51 35 | 95.54 107 | 86.36 34 | 94.57 123 | 96.44 74 | 91.69 10 | 94.32 16 | 96.56 48 | 87.05 22 | 99.03 50 | 93.35 18 | 97.65 57 | 98.15 40 |
|
Regformer-2 | | | 94.33 20 | 94.22 18 | 94.68 28 | 95.54 107 | 86.75 21 | 94.57 123 | 96.70 60 | 91.84 6 | 94.41 14 | 96.56 48 | 87.19 20 | 99.13 40 | 93.50 13 | 97.65 57 | 98.16 39 |
|
tfpn_ndepth | | | 86.10 215 | 84.98 216 | 89.43 225 | 95.52 110 | 78.29 241 | 94.62 119 | 89.60 322 | 81.88 227 | 85.43 180 | 90.54 256 | 68.47 250 | 96.85 228 | 68.46 295 | 90.34 165 | 93.15 258 |
|
WTY-MVS | | | 89.60 107 | 88.92 110 | 91.67 128 | 95.47 111 | 81.15 150 | 92.38 244 | 94.78 196 | 83.11 184 | 89.06 101 | 94.32 116 | 78.67 104 | 96.61 242 | 81.57 170 | 90.89 160 | 97.24 81 |
|
DELS-MVS | | | 93.43 41 | 93.25 39 | 93.97 47 | 95.42 112 | 85.04 56 | 93.06 222 | 97.13 27 | 90.74 20 | 91.84 65 | 95.09 95 | 86.32 28 | 99.21 32 | 91.22 54 | 98.45 38 | 97.65 68 |
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 |
Regformer-3 | | | 93.68 35 | 93.64 35 | 93.81 54 | 95.36 113 | 84.61 61 | 94.68 115 | 95.83 120 | 91.27 13 | 93.60 28 | 96.71 37 | 85.75 34 | 98.86 70 | 92.87 21 | 96.65 72 | 97.96 54 |
|
Regformer-4 | | | 93.91 31 | 93.81 29 | 94.19 45 | 95.36 113 | 85.47 52 | 94.68 115 | 96.41 77 | 91.60 11 | 93.75 24 | 96.71 37 | 85.95 32 | 99.10 43 | 93.21 19 | 96.65 72 | 98.01 52 |
|
thres200 | | | 87.21 192 | 86.24 188 | 90.12 190 | 95.36 113 | 78.53 229 | 93.26 213 | 92.10 257 | 86.42 110 | 88.00 116 | 91.11 245 | 69.24 232 | 98.00 133 | 69.58 285 | 91.04 155 | 93.83 220 |
|
casdiffmvs1 | | | 92.43 57 | 92.18 57 | 93.17 65 | 95.33 116 | 83.03 103 | 95.08 84 | 96.41 77 | 83.18 183 | 93.20 35 | 94.49 112 | 83.84 54 | 98.29 104 | 92.16 34 | 95.96 81 | 98.20 36 |
|
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 91.75 64 | 91.23 66 | 93.29 60 | 95.32 117 | 83.78 83 | 96.14 32 | 95.98 107 | 89.89 29 | 90.45 85 | 96.58 46 | 75.09 153 | 98.31 103 | 84.75 123 | 96.90 66 | 97.78 66 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
BH-RMVSNet | | | 88.37 139 | 87.48 139 | 91.02 148 | 95.28 118 | 79.45 195 | 92.89 229 | 93.07 239 | 85.45 129 | 86.91 137 | 94.84 103 | 70.35 217 | 97.76 144 | 73.97 258 | 94.59 103 | 95.85 128 |
|
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 80.39 16 | 83.96 263 | 82.04 269 | 89.74 210 | 95.28 118 | 79.75 185 | 94.25 147 | 92.28 253 | 75.17 293 | 78.02 290 | 93.77 142 | 58.60 314 | 97.84 141 | 65.06 319 | 85.92 224 | 91.63 295 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
conf0.01 | | | 85.83 223 | 84.54 229 | 89.71 212 | 95.26 120 | 77.63 260 | 94.21 150 | 89.33 323 | 81.89 220 | 84.94 195 | 91.51 224 | 68.43 252 | 96.80 229 | 66.05 308 | 89.23 184 | 94.71 171 |
|
conf0.002 | | | 85.83 223 | 84.54 229 | 89.71 212 | 95.26 120 | 77.63 260 | 94.21 150 | 89.33 323 | 81.89 220 | 84.94 195 | 91.51 224 | 68.43 252 | 96.80 229 | 66.05 308 | 89.23 184 | 94.71 171 |
|
thresconf0.02 | | | 85.75 227 | 84.54 229 | 89.38 228 | 95.26 120 | 77.63 260 | 94.21 150 | 89.33 323 | 81.89 220 | 84.94 195 | 91.51 224 | 68.43 252 | 96.80 229 | 66.05 308 | 89.23 184 | 93.70 230 |
|
tfpn_n400 | | | 85.75 227 | 84.54 229 | 89.38 228 | 95.26 120 | 77.63 260 | 94.21 150 | 89.33 323 | 81.89 220 | 84.94 195 | 91.51 224 | 68.43 252 | 96.80 229 | 66.05 308 | 89.23 184 | 93.70 230 |
|
tfpnconf | | | 85.75 227 | 84.54 229 | 89.38 228 | 95.26 120 | 77.63 260 | 94.21 150 | 89.33 323 | 81.89 220 | 84.94 195 | 91.51 224 | 68.43 252 | 96.80 229 | 66.05 308 | 89.23 184 | 93.70 230 |
|
tfpnview11 | | | 85.75 227 | 84.54 229 | 89.38 228 | 95.26 120 | 77.63 260 | 94.21 150 | 89.33 323 | 81.89 220 | 84.94 195 | 91.51 224 | 68.43 252 | 96.80 229 | 66.05 308 | 89.23 184 | 93.70 230 |
|
PS-MVSNAJ | | | 91.18 76 | 90.92 72 | 91.96 114 | 95.26 120 | 82.60 121 | 92.09 253 | 95.70 129 | 86.27 112 | 91.84 65 | 92.46 181 | 79.70 94 | 98.99 59 | 89.08 73 | 95.86 83 | 94.29 195 |
|
BH-untuned | | | 88.60 135 | 88.13 130 | 90.01 201 | 95.24 127 | 78.50 235 | 93.29 211 | 94.15 213 | 84.75 144 | 84.46 208 | 93.40 146 | 75.76 143 | 97.40 182 | 77.59 226 | 94.52 105 | 94.12 201 |
|
ab-mvs | | | 89.41 116 | 88.35 122 | 92.60 87 | 95.15 128 | 82.65 119 | 92.20 249 | 95.60 136 | 83.97 160 | 88.55 105 | 93.70 145 | 74.16 166 | 98.21 108 | 82.46 155 | 89.37 180 | 96.94 96 |
|
VDDNet | | | 89.56 109 | 88.49 120 | 92.76 83 | 95.07 129 | 82.09 127 | 96.30 26 | 93.19 237 | 81.05 241 | 91.88 64 | 96.86 31 | 61.16 303 | 98.33 101 | 88.43 81 | 92.49 141 | 97.84 62 |
|
AllTest | | | 83.42 268 | 81.39 272 | 89.52 220 | 95.01 130 | 77.79 254 | 93.12 217 | 90.89 298 | 77.41 273 | 76.12 307 | 93.34 147 | 54.08 329 | 97.51 158 | 68.31 297 | 84.27 239 | 93.26 252 |
|
TestCases | | | | | 89.52 220 | 95.01 130 | 77.79 254 | | 90.89 298 | 77.41 273 | 76.12 307 | 93.34 147 | 54.08 329 | 97.51 158 | 68.31 297 | 84.27 239 | 93.26 252 |
|
EI-MVSNet-Vis-set | | | 93.01 52 | 92.92 47 | 93.29 60 | 95.01 130 | 83.51 92 | 94.48 126 | 95.77 124 | 90.87 16 | 92.52 52 | 96.67 41 | 84.50 48 | 99.00 58 | 91.99 39 | 94.44 109 | 97.36 77 |
|
xiu_mvs_v2_base | | | 91.13 77 | 90.89 74 | 91.86 119 | 94.97 133 | 82.42 122 | 92.24 247 | 95.64 135 | 86.11 117 | 91.74 70 | 93.14 159 | 79.67 97 | 98.89 66 | 89.06 74 | 95.46 90 | 94.28 196 |
|
Test_1112_low_res | | | 87.65 166 | 86.51 181 | 91.08 144 | 94.94 134 | 79.28 209 | 91.77 256 | 94.30 209 | 76.04 286 | 83.51 233 | 92.37 185 | 77.86 114 | 97.73 148 | 78.69 216 | 89.13 192 | 96.22 112 |
|
1112_ss | | | 88.42 137 | 87.33 143 | 91.72 125 | 94.92 135 | 80.98 154 | 92.97 227 | 94.54 201 | 78.16 270 | 83.82 224 | 93.88 137 | 78.78 102 | 97.91 139 | 79.45 207 | 89.41 179 | 96.26 111 |
|
QAPM | | | 89.51 110 | 88.15 129 | 93.59 58 | 94.92 135 | 84.58 62 | 96.82 18 | 96.70 60 | 78.43 265 | 83.41 235 | 96.19 63 | 73.18 180 | 99.30 29 | 77.11 232 | 96.54 75 | 96.89 98 |
|
BH-w/o | | | 87.57 180 | 87.05 155 | 89.12 237 | 94.90 137 | 77.90 250 | 92.41 242 | 93.51 233 | 82.89 199 | 83.70 227 | 91.34 231 | 75.75 144 | 97.07 211 | 75.49 244 | 93.49 123 | 92.39 280 |
|
EI-MVSNet-UG-set | | | 92.74 55 | 92.62 51 | 93.12 67 | 94.86 138 | 83.20 98 | 94.40 134 | 95.74 127 | 90.71 21 | 92.05 62 | 96.60 45 | 84.00 51 | 98.99 59 | 91.55 49 | 93.63 120 | 97.17 86 |
|
HY-MVS | | 83.01 12 | 89.03 126 | 87.94 134 | 92.29 103 | 94.86 138 | 82.77 111 | 92.08 254 | 94.49 202 | 81.52 235 | 86.93 136 | 92.79 176 | 78.32 110 | 98.23 105 | 79.93 197 | 90.55 161 | 95.88 127 |
|
Fast-Effi-MVS+ | | | 89.41 116 | 88.64 115 | 91.71 126 | 94.74 140 | 80.81 160 | 93.54 200 | 95.10 178 | 83.11 184 | 86.82 140 | 90.67 252 | 79.74 93 | 97.75 147 | 80.51 186 | 93.55 121 | 96.57 105 |
|
ACMP | | 84.23 8 | 89.01 128 | 88.35 122 | 90.99 151 | 94.73 141 | 81.27 144 | 95.07 85 | 95.89 116 | 86.48 108 | 83.67 228 | 94.30 117 | 69.33 228 | 97.99 134 | 87.10 103 | 88.55 197 | 93.72 229 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PVSNet | | 78.82 18 | 85.55 233 | 84.65 227 | 88.23 266 | 94.72 142 | 71.93 309 | 87.12 314 | 92.75 245 | 78.80 260 | 84.95 194 | 90.53 258 | 64.43 284 | 96.71 237 | 74.74 252 | 93.86 116 | 96.06 121 |
|
LCM-MVSNet-Re | | | 88.30 142 | 88.32 125 | 88.27 263 | 94.71 143 | 72.41 308 | 93.15 216 | 90.98 295 | 87.77 78 | 79.25 284 | 91.96 205 | 78.35 109 | 95.75 278 | 83.04 143 | 95.62 85 | 96.65 103 |
|
HQP_MVS | | | 90.60 88 | 90.19 83 | 91.82 122 | 94.70 144 | 82.73 115 | 95.85 44 | 96.22 89 | 90.81 18 | 86.91 137 | 94.86 100 | 74.23 162 | 98.12 114 | 88.15 83 | 89.99 169 | 94.63 176 |
|
plane_prior7 | | | | | | 94.70 144 | 82.74 114 | | | | | | | | | | |
|
casdiffmvs | | | 91.72 66 | 91.26 65 | 93.10 68 | 94.66 146 | 83.75 84 | 94.77 107 | 96.00 106 | 83.98 159 | 90.74 81 | 93.96 131 | 82.08 69 | 98.19 109 | 91.47 52 | 93.68 118 | 97.36 77 |
|
ACMH+ | | 81.04 14 | 85.05 242 | 83.46 254 | 89.82 206 | 94.66 146 | 79.37 203 | 94.44 131 | 94.12 215 | 82.19 210 | 78.04 289 | 92.82 173 | 58.23 315 | 97.54 155 | 73.77 260 | 82.90 253 | 92.54 274 |
|
ACMM | | 84.12 9 | 89.14 122 | 88.48 121 | 91.12 141 | 94.65 148 | 81.22 147 | 95.31 63 | 96.12 97 | 85.31 132 | 85.92 155 | 94.34 114 | 70.19 220 | 98.06 130 | 85.65 113 | 88.86 195 | 94.08 205 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
plane_prior1 | | | | | | 94.59 149 | | | | | | | | | | | |
|
3Dnovator | | 86.66 5 | 91.73 65 | 90.82 75 | 94.44 36 | 94.59 149 | 86.37 33 | 97.18 6 | 97.02 33 | 89.20 42 | 84.31 216 | 96.66 42 | 73.74 173 | 99.17 35 | 86.74 104 | 97.96 50 | 97.79 65 |
|
plane_prior6 | | | | | | 94.52 151 | 82.75 112 | | | | | | 74.23 162 | | | | |
|
UGNet | | | 89.95 100 | 88.95 109 | 92.95 76 | 94.51 152 | 83.31 96 | 95.70 50 | 95.23 172 | 89.37 39 | 87.58 128 | 93.94 132 | 64.00 285 | 98.78 78 | 83.92 136 | 96.31 79 | 96.74 102 |
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 |
LPG-MVS_test | | | 89.45 113 | 88.90 111 | 91.12 141 | 94.47 153 | 81.49 138 | 95.30 65 | 96.14 94 | 86.73 105 | 85.45 177 | 95.16 92 | 69.89 221 | 98.10 120 | 87.70 90 | 89.23 184 | 93.77 225 |
|
LGP-MVS_train | | | | | 91.12 141 | 94.47 153 | 81.49 138 | | 96.14 94 | 86.73 105 | 85.45 177 | 95.16 92 | 69.89 221 | 98.10 120 | 87.70 90 | 89.23 184 | 93.77 225 |
|
ACMH | | 80.38 17 | 85.36 235 | 83.68 247 | 90.39 175 | 94.45 155 | 80.63 164 | 94.73 109 | 94.85 192 | 82.09 211 | 77.24 296 | 92.65 178 | 60.01 309 | 97.58 152 | 72.25 267 | 84.87 234 | 92.96 263 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
LTVRE_ROB | | 82.13 13 | 86.26 213 | 84.90 221 | 90.34 180 | 94.44 156 | 81.50 136 | 92.31 246 | 94.89 190 | 83.03 191 | 79.63 281 | 92.67 177 | 69.69 224 | 97.79 142 | 71.20 271 | 86.26 223 | 91.72 293 |
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
MVS_Test | | | 91.31 73 | 91.11 68 | 91.93 116 | 94.37 157 | 80.14 173 | 93.46 204 | 95.80 122 | 86.46 109 | 91.35 75 | 93.77 142 | 82.21 66 | 98.09 127 | 87.57 92 | 94.95 97 | 97.55 74 |
|
NP-MVS | | | | | | 94.37 157 | 82.42 122 | | | | | 93.98 129 | | | | | |
|
TR-MVS | | | 86.78 201 | 85.76 201 | 89.82 206 | 94.37 157 | 78.41 237 | 92.47 241 | 92.83 242 | 81.11 240 | 86.36 148 | 92.40 183 | 68.73 245 | 97.48 160 | 73.75 261 | 89.85 174 | 93.57 243 |
|
Effi-MVS+ | | | 91.59 69 | 91.11 68 | 93.01 74 | 94.35 160 | 83.39 95 | 94.60 120 | 95.10 178 | 87.10 93 | 90.57 83 | 93.10 161 | 81.43 78 | 98.07 129 | 89.29 72 | 94.48 106 | 97.59 71 |
|
CLD-MVS | | | 89.47 112 | 88.90 111 | 91.18 140 | 94.22 161 | 82.07 128 | 92.13 251 | 96.09 98 | 87.90 74 | 85.37 187 | 92.45 182 | 74.38 160 | 97.56 154 | 87.15 99 | 90.43 162 | 93.93 211 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
HQP-NCC | | | | | | 94.17 162 | | 94.39 136 | | 88.81 51 | 85.43 180 | | | | | | |
|
ACMP_Plane | | | | | | 94.17 162 | | 94.39 136 | | 88.81 51 | 85.43 180 | | | | | | |
|
HQP-MVS | | | 89.80 104 | 89.28 102 | 91.34 136 | 94.17 162 | 81.56 134 | 94.39 136 | 96.04 104 | 88.81 51 | 85.43 180 | 93.97 130 | 73.83 171 | 97.96 135 | 87.11 101 | 89.77 175 | 94.50 187 |
|
XVG-OURS | | | 89.40 118 | 88.70 114 | 91.52 131 | 94.06 165 | 81.46 140 | 91.27 268 | 96.07 100 | 86.14 116 | 88.89 103 | 95.77 77 | 68.73 245 | 97.26 196 | 87.39 95 | 89.96 171 | 95.83 130 |
|
sss | | | 88.93 129 | 88.26 128 | 90.94 154 | 94.05 166 | 80.78 161 | 91.71 259 | 95.38 159 | 81.55 234 | 88.63 104 | 93.91 136 | 75.04 154 | 95.47 289 | 82.47 154 | 91.61 145 | 96.57 105 |
|
PCF-MVS | | 84.11 10 | 87.74 163 | 86.08 193 | 92.70 85 | 94.02 167 | 84.43 72 | 89.27 291 | 95.87 117 | 73.62 307 | 84.43 210 | 94.33 115 | 78.48 108 | 98.86 70 | 70.27 275 | 94.45 108 | 94.81 168 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
GBi-Net | | | 87.26 187 | 85.98 195 | 91.08 144 | 94.01 168 | 83.10 100 | 95.14 81 | 94.94 185 | 83.57 170 | 84.37 211 | 91.64 213 | 66.59 268 | 96.34 256 | 78.23 220 | 85.36 229 | 93.79 221 |
|
test1 | | | 87.26 187 | 85.98 195 | 91.08 144 | 94.01 168 | 83.10 100 | 95.14 81 | 94.94 185 | 83.57 170 | 84.37 211 | 91.64 213 | 66.59 268 | 96.34 256 | 78.23 220 | 85.36 229 | 93.79 221 |
|
FMVSNet2 | | | 87.19 193 | 85.82 200 | 91.30 137 | 94.01 168 | 83.67 87 | 94.79 105 | 94.94 185 | 83.57 170 | 83.88 222 | 92.05 203 | 66.59 268 | 96.51 246 | 77.56 227 | 85.01 233 | 93.73 228 |
|
XVG-OURS-SEG-HR | | | 89.95 100 | 89.45 96 | 91.47 133 | 94.00 171 | 81.21 148 | 91.87 255 | 96.06 102 | 85.78 120 | 88.55 105 | 95.73 78 | 74.67 158 | 97.27 194 | 88.71 77 | 89.64 177 | 95.91 125 |
|
FIs | | | 90.51 89 | 90.35 79 | 90.99 151 | 93.99 172 | 80.98 154 | 95.73 48 | 97.54 3 | 89.15 44 | 86.72 141 | 94.68 106 | 81.83 75 | 97.24 198 | 85.18 116 | 88.31 205 | 94.76 170 |
|
xiu_mvs_v1_base_debu | | | 90.64 85 | 90.05 87 | 92.40 96 | 93.97 173 | 84.46 68 | 93.32 206 | 95.46 149 | 85.17 133 | 92.25 56 | 94.03 124 | 70.59 212 | 98.57 88 | 90.97 56 | 94.67 99 | 94.18 197 |
|
xiu_mvs_v1_base | | | 90.64 85 | 90.05 87 | 92.40 96 | 93.97 173 | 84.46 68 | 93.32 206 | 95.46 149 | 85.17 133 | 92.25 56 | 94.03 124 | 70.59 212 | 98.57 88 | 90.97 56 | 94.67 99 | 94.18 197 |
|
xiu_mvs_v1_base_debi | | | 90.64 85 | 90.05 87 | 92.40 96 | 93.97 173 | 84.46 68 | 93.32 206 | 95.46 149 | 85.17 133 | 92.25 56 | 94.03 124 | 70.59 212 | 98.57 88 | 90.97 56 | 94.67 99 | 94.18 197 |
|
VPA-MVSNet | | | 89.62 106 | 88.96 108 | 91.60 130 | 93.86 176 | 82.89 110 | 95.46 61 | 97.33 15 | 87.91 73 | 88.43 107 | 93.31 151 | 74.17 165 | 97.40 182 | 87.32 97 | 82.86 254 | 94.52 185 |
|
MVSFormer | | | 91.68 68 | 91.30 63 | 92.80 81 | 93.86 176 | 83.88 81 | 95.96 40 | 95.90 114 | 84.66 146 | 91.76 68 | 94.91 97 | 77.92 112 | 97.30 190 | 89.64 70 | 97.11 63 | 97.24 81 |
|
lupinMVS | | | 90.92 79 | 90.21 82 | 93.03 73 | 93.86 176 | 83.88 81 | 92.81 231 | 93.86 227 | 79.84 249 | 91.76 68 | 94.29 118 | 77.92 112 | 98.04 131 | 90.48 65 | 97.11 63 | 97.17 86 |
|
IterMVS-LS | | | 88.36 140 | 87.91 135 | 89.70 214 | 93.80 179 | 78.29 241 | 93.73 190 | 95.08 180 | 85.73 122 | 84.75 202 | 91.90 208 | 79.88 90 | 96.92 223 | 83.83 137 | 82.51 256 | 93.89 213 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MSDG | | | 84.86 249 | 83.09 260 | 90.14 189 | 93.80 179 | 80.05 176 | 89.18 294 | 93.09 238 | 78.89 257 | 78.19 287 | 91.91 207 | 65.86 278 | 97.27 194 | 68.47 294 | 88.45 201 | 93.11 259 |
|
FMVSNet3 | | | 87.40 185 | 86.11 191 | 91.30 137 | 93.79 181 | 83.64 88 | 94.20 156 | 94.81 195 | 83.89 161 | 84.37 211 | 91.87 209 | 68.45 251 | 96.56 243 | 78.23 220 | 85.36 229 | 93.70 230 |
|
FC-MVSNet-test | | | 90.27 93 | 90.18 84 | 90.53 161 | 93.71 182 | 79.85 183 | 95.77 47 | 97.59 2 | 89.31 40 | 86.27 150 | 94.67 107 | 81.93 74 | 97.01 215 | 84.26 131 | 88.09 208 | 94.71 171 |
|
TAMVS | | | 89.21 121 | 88.29 126 | 91.96 114 | 93.71 182 | 82.62 120 | 93.30 210 | 94.19 211 | 82.22 209 | 87.78 125 | 93.94 132 | 78.83 101 | 96.95 221 | 77.70 225 | 92.98 135 | 96.32 109 |
|
CDS-MVSNet | | | 89.45 113 | 88.51 117 | 92.29 103 | 93.62 184 | 83.61 90 | 93.01 224 | 94.68 198 | 81.95 216 | 87.82 124 | 93.24 155 | 78.69 103 | 96.99 216 | 80.34 189 | 93.23 131 | 96.28 110 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
UniMVSNet (Re) | | | 89.80 104 | 89.07 106 | 92.01 110 | 93.60 185 | 84.52 64 | 94.78 106 | 97.47 6 | 89.26 41 | 86.44 147 | 92.32 187 | 82.10 68 | 97.39 186 | 84.81 122 | 80.84 286 | 94.12 201 |
|
VPNet | | | 88.20 144 | 87.47 140 | 90.39 175 | 93.56 186 | 79.46 193 | 94.04 171 | 95.54 141 | 88.67 56 | 86.96 135 | 94.58 111 | 69.33 228 | 97.15 204 | 84.05 135 | 80.53 291 | 94.56 183 |
|
mvs_anonymous | | | 89.37 119 | 89.32 100 | 89.51 222 | 93.47 187 | 74.22 287 | 91.65 262 | 94.83 194 | 82.91 198 | 85.45 177 | 93.79 141 | 81.23 80 | 96.36 255 | 86.47 110 | 94.09 112 | 97.94 55 |
|
CANet_DTU | | | 90.26 94 | 89.41 98 | 92.81 80 | 93.46 188 | 83.01 106 | 93.48 202 | 94.47 203 | 89.43 37 | 87.76 126 | 94.23 122 | 70.54 216 | 99.03 50 | 84.97 118 | 96.39 78 | 96.38 108 |
|
diffmvs1 | | | 91.33 72 | 91.22 67 | 91.68 127 | 93.43 189 | 79.77 184 | 93.02 223 | 95.50 146 | 87.72 79 | 90.47 84 | 93.87 139 | 81.76 76 | 97.52 157 | 89.84 68 | 95.36 93 | 97.74 67 |
|
UniMVSNet_NR-MVSNet | | | 89.92 102 | 89.29 101 | 91.81 124 | 93.39 190 | 83.72 85 | 94.43 132 | 97.12 28 | 89.80 31 | 86.46 144 | 93.32 150 | 83.16 56 | 97.23 200 | 84.92 119 | 81.02 282 | 94.49 189 |
|
Effi-MVS+-dtu | | | 88.65 134 | 88.35 122 | 89.54 219 | 93.33 191 | 76.39 275 | 94.47 129 | 94.36 206 | 87.70 80 | 85.43 180 | 89.56 274 | 73.45 176 | 97.26 196 | 85.57 114 | 91.28 147 | 94.97 153 |
|
mvs-test1 | | | 89.45 113 | 89.14 104 | 90.38 177 | 93.33 191 | 77.63 260 | 94.95 93 | 94.36 206 | 87.70 80 | 87.10 134 | 92.81 174 | 73.45 176 | 98.03 132 | 85.57 114 | 93.04 134 | 95.48 140 |
|
WR-MVS | | | 88.38 138 | 87.67 137 | 90.52 167 | 93.30 193 | 80.18 171 | 93.26 213 | 95.96 109 | 88.57 60 | 85.47 176 | 92.81 174 | 76.12 127 | 96.91 224 | 81.24 172 | 82.29 258 | 94.47 192 |
|
WR-MVS_H | | | 87.80 161 | 87.37 142 | 89.10 239 | 93.23 194 | 78.12 245 | 95.61 57 | 97.30 18 | 87.90 74 | 83.72 226 | 92.01 204 | 79.65 98 | 96.01 267 | 76.36 237 | 80.54 290 | 93.16 256 |
|
test_0402 | | | 81.30 291 | 79.17 295 | 87.67 275 | 93.19 195 | 78.17 244 | 92.98 226 | 91.71 269 | 75.25 292 | 76.02 310 | 90.31 262 | 59.23 312 | 96.37 254 | 50.22 343 | 83.63 246 | 88.47 337 |
|
OPM-MVS | | | 90.12 95 | 89.56 94 | 91.82 122 | 93.14 196 | 83.90 80 | 94.16 157 | 95.74 127 | 88.96 50 | 87.86 117 | 95.43 85 | 72.48 190 | 97.91 139 | 88.10 86 | 90.18 168 | 93.65 235 |
|
CP-MVSNet | | | 87.63 170 | 87.26 146 | 88.74 244 | 93.12 197 | 76.59 274 | 95.29 67 | 96.58 71 | 88.43 62 | 83.49 234 | 92.98 168 | 75.28 150 | 95.83 274 | 78.97 213 | 81.15 279 | 93.79 221 |
|
nrg030 | | | 91.08 78 | 90.39 78 | 93.17 65 | 93.07 198 | 86.91 15 | 96.41 24 | 96.26 85 | 88.30 65 | 88.37 108 | 94.85 102 | 82.19 67 | 97.64 151 | 91.09 55 | 82.95 252 | 94.96 156 |
|
PAPM | | | 86.68 204 | 85.39 209 | 90.53 161 | 93.05 199 | 79.33 208 | 89.79 284 | 94.77 197 | 78.82 259 | 81.95 253 | 93.24 155 | 76.81 119 | 97.30 190 | 66.94 303 | 93.16 132 | 94.95 163 |
|
diffmvs | | | 90.50 90 | 90.33 80 | 91.02 148 | 93.04 200 | 78.59 225 | 92.85 230 | 95.07 181 | 87.32 88 | 88.32 109 | 93.34 147 | 80.46 84 | 97.40 182 | 88.50 79 | 94.06 113 | 97.07 91 |
|
DU-MVS | | | 89.34 120 | 88.50 118 | 91.85 120 | 93.04 200 | 83.72 85 | 94.47 129 | 96.59 69 | 89.50 36 | 86.46 144 | 93.29 153 | 77.25 116 | 97.23 200 | 84.92 119 | 81.02 282 | 94.59 180 |
|
NR-MVSNet | | | 88.58 136 | 87.47 140 | 91.93 116 | 93.04 200 | 84.16 77 | 94.77 107 | 96.25 87 | 89.05 46 | 80.04 278 | 93.29 153 | 79.02 100 | 97.05 213 | 81.71 169 | 80.05 296 | 94.59 180 |
|
jason | | | 90.80 80 | 90.10 85 | 92.90 78 | 93.04 200 | 83.53 91 | 93.08 220 | 94.15 213 | 80.22 245 | 91.41 74 | 94.91 97 | 76.87 118 | 97.93 138 | 90.28 66 | 96.90 66 | 97.24 81 |
jason: jason. |
PS-CasMVS | | | 87.32 186 | 86.88 158 | 88.63 247 | 92.99 204 | 76.33 277 | 95.33 62 | 96.61 68 | 88.22 68 | 83.30 237 | 93.07 162 | 73.03 182 | 95.79 277 | 78.36 218 | 81.00 284 | 93.75 227 |
|
MVSTER | | | 88.84 130 | 88.29 126 | 90.51 168 | 92.95 205 | 80.44 170 | 93.73 190 | 95.01 182 | 84.66 146 | 87.15 132 | 93.12 160 | 72.79 184 | 97.21 202 | 87.86 88 | 87.36 215 | 93.87 216 |
|
RPSCF | | | 85.07 241 | 84.27 235 | 87.48 281 | 92.91 206 | 70.62 321 | 91.69 261 | 92.46 250 | 76.20 285 | 82.67 243 | 95.22 90 | 63.94 286 | 97.29 193 | 77.51 228 | 85.80 226 | 94.53 184 |
|
Anonymous20240521 | | | 86.87 198 | 85.95 197 | 89.64 216 | 92.89 207 | 78.88 220 | 95.66 53 | 96.05 103 | 84.77 143 | 81.92 254 | 92.39 184 | 71.54 198 | 96.96 219 | 76.46 236 | 81.87 268 | 93.08 261 |
|
FMVSNet1 | | | 85.85 221 | 84.11 237 | 91.08 144 | 92.81 208 | 83.10 100 | 95.14 81 | 94.94 185 | 81.64 231 | 82.68 242 | 91.64 213 | 59.01 313 | 96.34 256 | 75.37 246 | 83.78 242 | 93.79 221 |
|
tfpnnormal | | | 84.72 255 | 83.23 259 | 89.20 236 | 92.79 209 | 80.05 176 | 94.48 126 | 95.81 121 | 82.38 207 | 81.08 264 | 91.21 239 | 69.01 235 | 96.95 221 | 61.69 328 | 80.59 289 | 90.58 320 |
|
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 83.78 11 | 88.74 133 | 87.29 144 | 93.08 70 | 92.70 210 | 85.39 53 | 96.57 22 | 96.43 76 | 78.74 262 | 80.85 266 | 96.07 67 | 69.64 225 | 99.01 55 | 78.01 223 | 96.65 72 | 94.83 167 |
|
TranMVSNet+NR-MVSNet | | | 88.84 130 | 87.95 133 | 91.49 132 | 92.68 211 | 83.01 106 | 94.92 96 | 96.31 83 | 89.88 30 | 85.53 171 | 93.85 140 | 76.63 123 | 96.96 219 | 81.91 165 | 79.87 301 | 94.50 187 |
|
MVS | | | 87.44 183 | 86.10 192 | 91.44 134 | 92.61 212 | 83.62 89 | 92.63 235 | 95.66 132 | 67.26 338 | 81.47 258 | 92.15 194 | 77.95 111 | 98.22 107 | 79.71 203 | 95.48 88 | 92.47 277 |
|
CHOSEN 280x420 | | | 85.15 240 | 83.99 239 | 88.65 246 | 92.47 213 | 78.40 238 | 79.68 346 | 92.76 244 | 74.90 297 | 81.41 260 | 89.59 272 | 69.85 223 | 95.51 285 | 79.92 198 | 95.29 94 | 92.03 287 |
|
1314 | | | 87.51 181 | 86.57 180 | 90.34 180 | 92.42 214 | 79.74 186 | 92.63 235 | 95.35 163 | 78.35 266 | 80.14 276 | 91.62 217 | 74.05 167 | 97.15 204 | 81.05 173 | 93.53 122 | 94.12 201 |
|
pcd1.5k->3k | | | 37.02 338 | 38.84 339 | 31.53 350 | 92.33 215 | 0.00 370 | 0.00 361 | 96.13 96 | 0.00 365 | 0.00 367 | 0.00 367 | 72.70 185 | 0.00 367 | 0.00 364 | 88.43 202 | 94.60 179 |
|
PEN-MVS | | | 86.80 200 | 86.27 187 | 88.40 260 | 92.32 216 | 75.71 281 | 95.18 78 | 96.38 81 | 87.97 71 | 82.82 241 | 93.15 158 | 73.39 178 | 95.92 270 | 76.15 241 | 79.03 305 | 93.59 242 |
|
Patchmatch-test1 | | | 85.81 225 | 84.71 225 | 89.12 237 | 92.15 217 | 76.60 273 | 91.12 271 | 91.69 271 | 83.53 173 | 85.50 174 | 88.56 287 | 66.79 266 | 95.00 306 | 72.69 265 | 90.35 164 | 95.76 133 |
|
XXY-MVS | | | 87.65 166 | 86.85 160 | 90.03 199 | 92.14 218 | 80.60 166 | 93.76 187 | 95.23 172 | 82.94 196 | 84.60 204 | 94.02 127 | 74.27 161 | 95.49 288 | 81.04 174 | 83.68 245 | 94.01 210 |
|
IB-MVS | | 80.51 15 | 85.24 239 | 83.26 258 | 91.19 139 | 92.13 219 | 79.86 182 | 91.75 257 | 91.29 285 | 83.28 181 | 80.66 269 | 88.49 288 | 61.28 299 | 98.46 93 | 80.99 177 | 79.46 303 | 95.25 147 |
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 |
cascas | | | 86.43 211 | 84.98 216 | 90.80 156 | 92.10 220 | 80.92 157 | 90.24 276 | 95.91 113 | 73.10 311 | 83.57 232 | 88.39 289 | 65.15 280 | 97.46 162 | 84.90 121 | 91.43 146 | 94.03 208 |
|
Fast-Effi-MVS+-dtu | | | 87.44 183 | 86.72 166 | 89.63 217 | 92.04 221 | 77.68 259 | 94.03 172 | 93.94 225 | 85.81 119 | 82.42 244 | 91.32 234 | 70.33 218 | 97.06 212 | 80.33 190 | 90.23 167 | 94.14 200 |
|
PS-MVSNAJss | | | 89.97 99 | 89.62 93 | 91.02 148 | 91.90 222 | 80.85 159 | 95.26 74 | 95.98 107 | 86.26 113 | 86.21 151 | 94.29 118 | 79.70 94 | 97.65 149 | 88.87 76 | 88.10 206 | 94.57 182 |
|
ITE_SJBPF | | | | | 88.24 265 | 91.88 223 | 77.05 270 | | 92.92 240 | 85.54 127 | 80.13 277 | 93.30 152 | 57.29 318 | 96.20 260 | 72.46 266 | 84.71 235 | 91.49 297 |
|
EI-MVSNet | | | 89.10 123 | 88.86 113 | 89.80 209 | 91.84 224 | 78.30 240 | 93.70 194 | 95.01 182 | 85.73 122 | 87.15 132 | 95.28 87 | 79.87 91 | 97.21 202 | 83.81 138 | 87.36 215 | 93.88 215 |
|
CVMVSNet | | | 84.69 257 | 84.79 224 | 84.37 315 | 91.84 224 | 64.92 338 | 93.70 194 | 91.47 281 | 66.19 340 | 86.16 153 | 95.28 87 | 67.18 264 | 93.33 321 | 80.89 179 | 90.42 163 | 94.88 165 |
|
MVS-HIRNet | | | 73.70 315 | 72.20 315 | 78.18 328 | 91.81 226 | 56.42 350 | 82.94 340 | 82.58 351 | 55.24 349 | 68.88 335 | 66.48 350 | 55.32 324 | 95.13 302 | 58.12 335 | 88.42 203 | 83.01 344 |
|
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 85.85 221 | 84.70 226 | 89.29 233 | 91.76 227 | 75.54 282 | 88.49 302 | 91.30 284 | 81.63 232 | 85.05 192 | 88.70 284 | 71.71 194 | 96.24 259 | 74.61 254 | 89.05 193 | 96.08 119 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
TransMVSNet (Re) | | | 84.43 260 | 83.06 261 | 88.54 256 | 91.72 228 | 78.44 236 | 95.18 78 | 92.82 243 | 82.73 202 | 79.67 280 | 92.12 196 | 73.49 175 | 95.96 269 | 71.10 274 | 68.73 340 | 91.21 302 |
|
semantic-postprocess | | | | | 88.18 267 | 91.71 229 | 76.87 272 | | 92.65 248 | 85.40 130 | 81.44 259 | 90.54 256 | 66.21 272 | 95.00 306 | 81.04 174 | 81.05 280 | 92.66 272 |
|
TinyColmap | | | 79.76 301 | 77.69 301 | 85.97 303 | 91.71 229 | 73.12 297 | 89.55 285 | 90.36 306 | 75.03 294 | 72.03 330 | 90.19 263 | 46.22 343 | 96.19 261 | 63.11 324 | 81.03 281 | 88.59 334 |
|
MDTV_nov1_ep13 | | | | 83.56 250 | | 91.69 231 | 69.93 325 | 87.75 310 | 91.54 278 | 78.60 263 | 84.86 201 | 88.90 280 | 69.54 226 | 96.03 265 | 70.25 276 | 88.93 194 | |
|
DTE-MVSNet | | | 86.11 214 | 85.48 207 | 87.98 270 | 91.65 232 | 74.92 284 | 94.93 95 | 95.75 126 | 87.36 87 | 82.26 246 | 93.04 163 | 72.85 183 | 95.82 275 | 74.04 257 | 77.46 311 | 93.20 254 |
|
PatchFormer-LS_test | | | 86.02 217 | 85.13 213 | 88.70 245 | 91.52 233 | 74.12 290 | 91.19 270 | 92.09 258 | 82.71 203 | 84.30 217 | 87.24 305 | 70.87 207 | 96.98 217 | 81.04 174 | 85.17 232 | 95.00 152 |
|
MIMVSNet | | | 82.59 276 | 80.53 279 | 88.76 243 | 91.51 234 | 78.32 239 | 86.57 317 | 90.13 310 | 79.32 253 | 80.70 268 | 88.69 285 | 52.98 331 | 93.07 326 | 66.03 314 | 88.86 195 | 94.90 164 |
|
tpmp4_e23 | | | 83.87 266 | 82.33 267 | 88.48 257 | 91.46 235 | 72.82 300 | 89.82 283 | 91.57 277 | 73.02 313 | 81.86 256 | 89.05 277 | 66.20 273 | 96.97 218 | 71.57 269 | 86.39 222 | 95.66 136 |
|
pm-mvs1 | | | 86.61 205 | 85.54 203 | 89.82 206 | 91.44 236 | 80.18 171 | 95.28 73 | 94.85 192 | 83.84 162 | 81.66 257 | 92.62 179 | 72.45 192 | 96.48 248 | 79.67 204 | 78.06 307 | 92.82 269 |
|
Baseline_NR-MVSNet | | | 87.07 195 | 86.63 178 | 88.40 260 | 91.44 236 | 77.87 252 | 94.23 149 | 92.57 249 | 84.12 158 | 85.74 162 | 92.08 200 | 77.25 116 | 96.04 264 | 82.29 158 | 79.94 299 | 91.30 301 |
|
IterMVS | | | 84.88 248 | 83.98 240 | 87.60 276 | 91.44 236 | 76.03 279 | 90.18 278 | 92.41 251 | 83.24 182 | 81.06 265 | 90.42 261 | 66.60 267 | 94.28 312 | 79.46 206 | 80.98 285 | 92.48 276 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DWT-MVSNet_test | | | 84.95 246 | 83.68 247 | 88.77 242 | 91.43 239 | 73.75 293 | 91.74 258 | 90.98 295 | 80.66 243 | 83.84 223 | 87.36 303 | 62.44 291 | 97.11 208 | 78.84 215 | 85.81 225 | 95.46 141 |
|
MS-PatchMatch | | | 85.05 242 | 84.16 236 | 87.73 274 | 91.42 240 | 78.51 234 | 91.25 269 | 93.53 232 | 77.50 272 | 80.15 275 | 91.58 218 | 61.99 294 | 95.51 285 | 75.69 243 | 94.35 111 | 89.16 328 |
|
v16 | | | 84.96 245 | 83.74 244 | 88.62 248 | 91.40 241 | 79.48 191 | 93.83 181 | 94.04 216 | 83.03 191 | 76.54 301 | 86.59 309 | 76.11 130 | 95.42 291 | 80.33 190 | 71.80 324 | 90.95 309 |
|
tpm2 | | | 84.08 262 | 82.94 262 | 87.48 281 | 91.39 242 | 71.27 313 | 89.23 293 | 90.37 305 | 71.95 321 | 84.64 203 | 89.33 275 | 67.30 261 | 96.55 245 | 75.17 248 | 87.09 219 | 94.63 176 |
|
v1neww | | | 87.98 151 | 87.25 147 | 90.16 184 | 91.38 243 | 79.41 197 | 94.37 140 | 95.28 164 | 84.48 149 | 85.77 158 | 91.53 222 | 76.12 127 | 97.45 164 | 84.45 128 | 81.89 265 | 93.61 240 |
|
v7new | | | 87.98 151 | 87.25 147 | 90.16 184 | 91.38 243 | 79.41 197 | 94.37 140 | 95.28 164 | 84.48 149 | 85.77 158 | 91.53 222 | 76.12 127 | 97.45 164 | 84.45 128 | 81.89 265 | 93.61 240 |
|
v8 | | | 87.50 182 | 86.71 167 | 89.89 204 | 91.37 245 | 79.40 201 | 94.50 125 | 95.38 159 | 84.81 142 | 83.60 231 | 91.33 232 | 76.05 131 | 97.42 175 | 82.84 147 | 80.51 293 | 92.84 267 |
|
v18 | | | 84.97 244 | 83.76 242 | 88.60 250 | 91.36 246 | 79.41 197 | 93.82 183 | 94.04 216 | 83.00 194 | 76.61 300 | 86.60 308 | 76.19 125 | 95.43 290 | 80.39 187 | 71.79 325 | 90.96 307 |
|
v17 | | | 84.93 247 | 83.70 246 | 88.62 248 | 91.36 246 | 79.48 191 | 93.83 181 | 94.03 218 | 83.04 190 | 76.51 302 | 86.57 310 | 76.05 131 | 95.42 291 | 80.31 192 | 71.65 326 | 90.96 307 |
|
v6 | | | 87.98 151 | 87.25 147 | 90.16 184 | 91.36 246 | 79.39 202 | 94.37 140 | 95.27 167 | 84.48 149 | 85.78 157 | 91.51 224 | 76.15 126 | 97.46 162 | 84.46 127 | 81.88 267 | 93.62 239 |
|
ADS-MVSNet2 | | | 81.66 284 | 79.71 290 | 87.50 279 | 91.35 249 | 74.19 288 | 83.33 337 | 88.48 334 | 72.90 314 | 82.24 247 | 85.77 320 | 64.98 281 | 93.20 323 | 64.57 320 | 83.74 243 | 95.12 148 |
|
ADS-MVSNet | | | 81.56 286 | 79.78 288 | 86.90 294 | 91.35 249 | 71.82 310 | 83.33 337 | 89.16 329 | 72.90 314 | 82.24 247 | 85.77 320 | 64.98 281 | 93.76 316 | 64.57 320 | 83.74 243 | 95.12 148 |
|
V9 | | | 84.77 252 | 83.50 253 | 88.58 251 | 91.33 251 | 79.46 193 | 93.75 188 | 94.00 222 | 83.07 186 | 76.07 309 | 86.43 311 | 75.97 136 | 95.37 294 | 79.91 199 | 70.93 332 | 90.91 311 |
|
GA-MVS | | | 86.61 205 | 85.27 212 | 90.66 157 | 91.33 251 | 78.71 222 | 90.40 274 | 93.81 230 | 85.34 131 | 85.12 191 | 89.57 273 | 61.25 300 | 97.11 208 | 80.99 177 | 89.59 178 | 96.15 113 |
|
v12 | | | 84.74 253 | 83.46 254 | 88.58 251 | 91.32 253 | 79.50 188 | 93.75 188 | 94.01 219 | 83.06 187 | 75.98 311 | 86.41 315 | 75.82 142 | 95.36 297 | 79.87 200 | 70.89 333 | 90.89 313 |
|
v11 | | | 84.67 258 | 83.41 257 | 88.44 259 | 91.32 253 | 79.13 216 | 93.69 197 | 93.99 224 | 82.81 200 | 76.20 305 | 86.24 318 | 75.48 147 | 95.35 298 | 79.53 205 | 71.48 328 | 90.85 315 |
|
V14 | | | 84.79 250 | 83.52 252 | 88.57 254 | 91.32 253 | 79.43 196 | 93.72 192 | 94.01 219 | 83.06 187 | 76.22 304 | 86.43 311 | 76.01 135 | 95.37 294 | 79.96 196 | 70.99 330 | 90.91 311 |
|
v13 | | | 84.72 255 | 83.44 256 | 88.58 251 | 91.31 256 | 79.52 187 | 93.77 186 | 94.00 222 | 83.03 191 | 75.85 312 | 86.38 316 | 75.84 141 | 95.35 298 | 79.83 201 | 70.95 331 | 90.87 314 |
|
v15 | | | 84.79 250 | 83.53 251 | 88.57 254 | 91.30 257 | 79.41 197 | 93.70 194 | 94.01 219 | 83.06 187 | 76.27 303 | 86.42 314 | 76.03 134 | 95.38 293 | 80.01 194 | 71.00 329 | 90.92 310 |
|
v1141 | | | 87.84 157 | 87.09 151 | 90.11 195 | 91.23 258 | 79.25 211 | 94.08 165 | 95.24 169 | 84.44 153 | 85.69 165 | 91.31 235 | 75.91 139 | 97.44 171 | 84.17 133 | 81.74 272 | 93.63 238 |
|
divwei89l23v2f112 | | | 87.84 157 | 87.09 151 | 90.10 197 | 91.23 258 | 79.24 213 | 94.09 163 | 95.24 169 | 84.44 153 | 85.70 163 | 91.31 235 | 75.91 139 | 97.44 171 | 84.17 133 | 81.73 274 | 93.64 236 |
|
v1 | | | 87.85 156 | 87.10 150 | 90.11 195 | 91.21 260 | 79.24 213 | 94.09 163 | 95.24 169 | 84.44 153 | 85.70 163 | 91.31 235 | 75.96 137 | 97.45 164 | 84.18 132 | 81.73 274 | 93.64 236 |
|
v7 | | | 87.75 162 | 86.96 157 | 90.12 190 | 91.20 261 | 79.50 188 | 94.28 146 | 95.46 149 | 83.45 175 | 85.75 160 | 91.56 221 | 75.13 151 | 97.43 173 | 83.60 140 | 82.18 260 | 93.42 249 |
|
XVG-ACMP-BASELINE | | | 86.00 218 | 84.84 223 | 89.45 224 | 91.20 261 | 78.00 247 | 91.70 260 | 95.55 139 | 85.05 138 | 82.97 239 | 92.25 193 | 54.49 327 | 97.48 160 | 82.93 145 | 87.45 214 | 92.89 265 |
|
v10 | | | 87.25 189 | 86.38 182 | 89.85 205 | 91.19 263 | 79.50 188 | 94.48 126 | 95.45 153 | 83.79 165 | 83.62 230 | 91.19 240 | 75.13 151 | 97.42 175 | 81.94 164 | 80.60 288 | 92.63 273 |
|
FMVSNet5 | | | 81.52 287 | 79.60 291 | 87.27 283 | 91.17 264 | 77.95 248 | 91.49 264 | 92.26 254 | 76.87 278 | 76.16 306 | 87.91 298 | 51.67 332 | 92.34 328 | 67.74 301 | 81.16 277 | 91.52 296 |
|
USDC | | | 82.76 273 | 81.26 274 | 87.26 284 | 91.17 264 | 74.55 285 | 89.27 291 | 93.39 235 | 78.26 268 | 75.30 314 | 92.08 200 | 54.43 328 | 96.63 239 | 71.64 268 | 85.79 227 | 90.61 317 |
|
CostFormer | | | 85.77 226 | 84.94 219 | 88.26 264 | 91.16 266 | 72.58 307 | 89.47 289 | 91.04 294 | 76.26 284 | 86.45 146 | 89.97 267 | 70.74 210 | 96.86 227 | 82.35 156 | 87.07 220 | 95.34 146 |
|
tpm cat1 | | | 81.96 279 | 80.27 282 | 87.01 291 | 91.09 267 | 71.02 317 | 87.38 313 | 91.53 279 | 66.25 339 | 80.17 274 | 86.35 317 | 68.22 260 | 96.15 262 | 69.16 290 | 82.29 258 | 93.86 218 |
|
tpmvs | | | 83.35 271 | 82.07 268 | 87.20 289 | 91.07 268 | 71.00 318 | 88.31 305 | 91.70 270 | 78.91 256 | 80.49 272 | 87.18 306 | 69.30 231 | 97.08 210 | 68.12 300 | 83.56 247 | 93.51 247 |
|
v1144 | | | 87.61 178 | 86.79 164 | 90.06 198 | 91.01 269 | 79.34 205 | 93.95 177 | 95.42 158 | 83.36 179 | 85.66 167 | 91.31 235 | 74.98 155 | 97.42 175 | 83.37 141 | 82.06 261 | 93.42 249 |
|
v2v482 | | | 87.84 157 | 87.06 154 | 90.17 183 | 90.99 270 | 79.23 215 | 94.00 175 | 95.13 176 | 84.87 140 | 85.53 171 | 92.07 202 | 74.45 159 | 97.45 164 | 84.71 124 | 81.75 271 | 93.85 219 |
|
SixPastTwentyTwo | | | 83.91 264 | 82.90 263 | 86.92 293 | 90.99 270 | 70.67 320 | 93.48 202 | 91.99 263 | 85.54 127 | 77.62 294 | 92.11 198 | 60.59 305 | 96.87 226 | 76.05 242 | 77.75 308 | 93.20 254 |
|
test-LLR | | | 85.87 220 | 85.41 208 | 87.25 285 | 90.95 272 | 71.67 311 | 89.55 285 | 89.88 317 | 83.41 176 | 84.54 206 | 87.95 296 | 67.25 262 | 95.11 303 | 81.82 166 | 93.37 128 | 94.97 153 |
|
test-mter | | | 84.54 259 | 83.64 249 | 87.25 285 | 90.95 272 | 71.67 311 | 89.55 285 | 89.88 317 | 79.17 254 | 84.54 206 | 87.95 296 | 55.56 322 | 95.11 303 | 81.82 166 | 93.37 128 | 94.97 153 |
|
v148 | | | 87.04 196 | 86.32 185 | 89.21 235 | 90.94 274 | 77.26 268 | 93.71 193 | 94.43 204 | 84.84 141 | 84.36 214 | 90.80 250 | 76.04 133 | 97.05 213 | 82.12 160 | 79.60 302 | 93.31 251 |
|
mvs_tets | | | 88.06 150 | 87.28 145 | 90.38 177 | 90.94 274 | 79.88 181 | 95.22 76 | 95.66 132 | 85.10 137 | 84.21 219 | 93.94 132 | 63.53 287 | 97.40 182 | 88.50 79 | 88.40 204 | 93.87 216 |
|
MVP-Stereo | | | 85.97 219 | 84.86 222 | 89.32 232 | 90.92 276 | 82.19 126 | 92.11 252 | 94.19 211 | 78.76 261 | 78.77 286 | 91.63 216 | 68.38 258 | 96.56 243 | 75.01 251 | 93.95 114 | 89.20 327 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
Patchmatch-test | | | 81.37 289 | 79.30 293 | 87.58 277 | 90.92 276 | 74.16 289 | 80.99 343 | 87.68 340 | 70.52 329 | 76.63 299 | 88.81 281 | 71.21 202 | 92.76 327 | 60.01 334 | 86.93 221 | 95.83 130 |
|
jajsoiax | | | 88.24 143 | 87.50 138 | 90.48 170 | 90.89 278 | 80.14 173 | 95.31 63 | 95.65 134 | 84.97 139 | 84.24 218 | 94.02 127 | 65.31 279 | 97.42 175 | 88.56 78 | 88.52 199 | 93.89 213 |
|
tpmrst | | | 85.35 236 | 84.99 215 | 86.43 299 | 90.88 279 | 67.88 331 | 88.71 299 | 91.43 282 | 80.13 246 | 86.08 154 | 88.80 282 | 73.05 181 | 96.02 266 | 82.48 153 | 83.40 251 | 95.40 143 |
|
gg-mvs-nofinetune | | | 81.77 282 | 79.37 292 | 88.99 240 | 90.85 280 | 77.73 258 | 86.29 318 | 79.63 357 | 74.88 298 | 83.19 238 | 69.05 349 | 60.34 306 | 96.11 263 | 75.46 245 | 94.64 102 | 93.11 259 |
|
OurMVSNet-221017-0 | | | 85.35 236 | 84.64 228 | 87.49 280 | 90.77 281 | 72.59 306 | 94.01 174 | 94.40 205 | 84.72 145 | 79.62 282 | 93.17 157 | 61.91 295 | 96.72 235 | 81.99 163 | 81.16 277 | 93.16 256 |
|
v1192 | | | 87.25 189 | 86.33 184 | 90.00 202 | 90.76 282 | 79.04 217 | 93.80 184 | 95.48 148 | 82.57 205 | 85.48 175 | 91.18 241 | 73.38 179 | 97.42 175 | 82.30 157 | 82.06 261 | 93.53 244 |
|
test_djsdf | | | 89.03 126 | 88.64 115 | 90.21 182 | 90.74 283 | 79.28 209 | 95.96 40 | 95.90 114 | 84.66 146 | 85.33 189 | 92.94 169 | 74.02 168 | 97.30 190 | 89.64 70 | 88.53 198 | 94.05 207 |
|
v7n | | | 86.81 199 | 85.76 201 | 89.95 203 | 90.72 284 | 79.25 211 | 95.07 85 | 95.92 111 | 84.45 152 | 82.29 245 | 90.86 249 | 72.60 188 | 97.53 156 | 79.42 210 | 80.52 292 | 93.08 261 |
|
PVSNet_0 | | 73.20 20 | 77.22 308 | 74.83 311 | 84.37 315 | 90.70 285 | 71.10 316 | 83.09 339 | 89.67 320 | 72.81 316 | 73.93 321 | 83.13 330 | 60.79 304 | 93.70 317 | 68.54 293 | 50.84 351 | 88.30 338 |
|
DI_MVS_plusplus_test | | | 88.15 146 | 86.82 161 | 92.14 108 | 90.67 286 | 81.07 151 | 93.01 224 | 94.59 200 | 83.83 164 | 77.78 291 | 90.63 253 | 68.51 248 | 98.16 111 | 88.02 87 | 94.37 110 | 97.17 86 |
|
v144192 | | | 87.19 193 | 86.35 183 | 89.74 210 | 90.64 287 | 78.24 243 | 93.92 178 | 95.43 156 | 81.93 217 | 85.51 173 | 91.05 247 | 74.21 164 | 97.45 164 | 82.86 146 | 81.56 276 | 93.53 244 |
|
V42 | | | 87.68 164 | 86.86 159 | 90.15 188 | 90.58 288 | 80.14 173 | 94.24 148 | 95.28 164 | 83.66 167 | 85.67 166 | 91.33 232 | 74.73 157 | 97.41 180 | 84.43 130 | 81.83 269 | 92.89 265 |
|
CR-MVSNet | | | 85.35 236 | 83.76 242 | 90.12 190 | 90.58 288 | 79.34 205 | 85.24 326 | 91.96 266 | 78.27 267 | 85.55 169 | 87.87 299 | 71.03 205 | 95.61 280 | 73.96 259 | 89.36 181 | 95.40 143 |
|
RPMNet | | | 83.18 272 | 80.87 278 | 90.12 190 | 90.58 288 | 79.34 205 | 85.24 326 | 90.78 301 | 71.44 323 | 85.55 169 | 82.97 331 | 70.87 207 | 95.61 280 | 61.01 330 | 89.36 181 | 95.40 143 |
|
test_normal | | | 88.13 147 | 86.78 165 | 92.18 106 | 90.55 291 | 81.19 149 | 92.74 233 | 94.64 199 | 83.84 162 | 77.49 295 | 90.51 259 | 68.49 249 | 98.16 111 | 88.22 82 | 94.55 104 | 97.21 84 |
|
v1921920 | | | 86.97 197 | 86.06 194 | 89.69 215 | 90.53 292 | 78.11 246 | 93.80 184 | 95.43 156 | 81.90 219 | 85.33 189 | 91.05 247 | 72.66 186 | 97.41 180 | 82.05 162 | 81.80 270 | 93.53 244 |
|
v1240 | | | 86.78 201 | 85.85 199 | 89.56 218 | 90.45 293 | 77.79 254 | 93.61 198 | 95.37 161 | 81.65 230 | 85.43 180 | 91.15 243 | 71.50 200 | 97.43 173 | 81.47 171 | 82.05 263 | 93.47 248 |
|
tpm | | | 84.73 254 | 84.02 238 | 86.87 296 | 90.33 294 | 68.90 328 | 89.06 295 | 89.94 315 | 80.85 242 | 85.75 160 | 89.86 269 | 68.54 247 | 95.97 268 | 77.76 224 | 84.05 241 | 95.75 134 |
|
EG-PatchMatch MVS | | | 82.37 278 | 80.34 281 | 88.46 258 | 90.27 295 | 79.35 204 | 92.80 232 | 94.33 208 | 77.14 277 | 73.26 325 | 90.18 264 | 47.47 341 | 96.72 235 | 70.25 276 | 87.32 217 | 89.30 325 |
|
v748 | | | 86.27 212 | 85.28 211 | 89.25 234 | 90.26 296 | 77.58 267 | 94.89 97 | 95.50 146 | 84.28 156 | 81.41 260 | 90.46 260 | 72.57 189 | 97.32 189 | 79.81 202 | 78.36 306 | 92.84 267 |
|
EPNet_dtu | | | 86.49 210 | 85.94 198 | 88.14 268 | 90.24 297 | 72.82 300 | 94.11 160 | 92.20 255 | 86.66 107 | 79.42 283 | 92.36 186 | 73.52 174 | 95.81 276 | 71.26 270 | 93.66 119 | 95.80 132 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPMVS | | | 83.90 265 | 82.70 266 | 87.51 278 | 90.23 298 | 72.67 303 | 88.62 301 | 81.96 353 | 81.37 238 | 85.01 193 | 88.34 290 | 66.31 271 | 94.45 309 | 75.30 247 | 87.12 218 | 95.43 142 |
|
EPNet | | | 91.79 62 | 91.02 71 | 94.10 46 | 90.10 299 | 85.25 55 | 96.03 37 | 92.05 260 | 92.83 1 | 87.39 131 | 95.78 76 | 79.39 99 | 99.01 55 | 88.13 85 | 97.48 59 | 98.05 48 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PatchT | | | 82.68 275 | 81.27 273 | 86.89 295 | 90.09 300 | 70.94 319 | 84.06 333 | 90.15 309 | 74.91 296 | 85.63 168 | 83.57 328 | 69.37 227 | 94.87 308 | 65.19 316 | 88.50 200 | 94.84 166 |
|
Patchmtry | | | 82.71 274 | 80.93 277 | 88.06 269 | 90.05 301 | 76.37 276 | 84.74 328 | 91.96 266 | 72.28 319 | 81.32 262 | 87.87 299 | 71.03 205 | 95.50 287 | 68.97 291 | 80.15 295 | 92.32 283 |
|
pmmvs4 | | | 85.43 234 | 83.86 241 | 90.16 184 | 90.02 302 | 82.97 108 | 90.27 275 | 92.67 247 | 75.93 287 | 80.73 267 | 91.74 212 | 71.05 204 | 95.73 279 | 78.85 214 | 83.46 249 | 91.78 290 |
|
TESTMET0.1,1 | | | 83.74 267 | 82.85 264 | 86.42 300 | 89.96 303 | 71.21 315 | 89.55 285 | 87.88 337 | 77.41 273 | 83.37 236 | 87.31 304 | 56.71 319 | 93.65 318 | 80.62 183 | 92.85 138 | 94.40 193 |
|
dp | | | 81.47 288 | 80.23 283 | 85.17 310 | 89.92 304 | 65.49 337 | 86.74 315 | 90.10 311 | 76.30 283 | 81.10 263 | 87.12 307 | 62.81 289 | 95.92 270 | 68.13 299 | 79.88 300 | 94.09 204 |
|
K. test v3 | | | 81.59 285 | 80.15 285 | 85.91 304 | 89.89 305 | 69.42 327 | 92.57 238 | 87.71 339 | 85.56 126 | 73.44 323 | 89.71 271 | 55.58 321 | 95.52 284 | 77.17 231 | 69.76 336 | 92.78 270 |
|
MDA-MVSNet-bldmvs | | | 78.85 306 | 76.31 307 | 86.46 298 | 89.76 306 | 73.88 292 | 88.79 298 | 90.42 304 | 79.16 255 | 59.18 347 | 88.33 291 | 60.20 307 | 94.04 314 | 62.00 327 | 68.96 338 | 91.48 298 |
|
GG-mvs-BLEND | | | | | 87.94 272 | 89.73 307 | 77.91 249 | 87.80 308 | 78.23 359 | | 80.58 270 | 83.86 326 | 59.88 310 | 95.33 300 | 71.20 271 | 92.22 143 | 90.60 319 |
|
gm-plane-assit | | | | | | 89.60 308 | 68.00 330 | | | 77.28 276 | | 88.99 278 | | 97.57 153 | 79.44 208 | | |
|
v52 | | | 86.50 208 | 85.53 206 | 89.39 226 | 89.17 309 | 78.99 218 | 94.72 112 | 95.54 141 | 83.59 168 | 82.10 249 | 90.60 255 | 71.59 197 | 97.45 164 | 82.52 151 | 79.99 298 | 91.73 292 |
|
anonymousdsp | | | 87.84 157 | 87.09 151 | 90.12 190 | 89.13 310 | 80.54 167 | 94.67 117 | 95.55 139 | 82.05 212 | 83.82 224 | 92.12 196 | 71.47 201 | 97.15 204 | 87.15 99 | 87.80 211 | 92.67 271 |
|
V4 | | | 86.50 208 | 85.54 203 | 89.39 226 | 89.13 310 | 78.99 218 | 94.73 109 | 95.54 141 | 83.59 168 | 82.10 249 | 90.61 254 | 71.60 196 | 97.45 164 | 82.52 151 | 80.01 297 | 91.74 291 |
|
N_pmnet | | | 68.89 321 | 68.44 322 | 70.23 337 | 89.07 312 | 28.79 366 | 88.06 306 | 19.50 368 | 69.47 332 | 71.86 331 | 84.93 323 | 61.24 301 | 91.75 333 | 54.70 338 | 77.15 312 | 90.15 321 |
|
pmmvs5 | | | 84.21 261 | 82.84 265 | 88.34 262 | 88.95 313 | 76.94 271 | 92.41 242 | 91.91 268 | 75.63 289 | 80.28 273 | 91.18 241 | 64.59 283 | 95.57 282 | 77.09 233 | 83.47 248 | 92.53 275 |
|
PMMVS | | | 85.71 232 | 84.96 218 | 87.95 271 | 88.90 314 | 77.09 269 | 88.68 300 | 90.06 312 | 72.32 318 | 86.47 143 | 90.76 251 | 72.15 193 | 94.40 310 | 81.78 168 | 93.49 123 | 92.36 281 |
|
JIA-IIPM | | | 81.04 292 | 78.98 298 | 87.25 285 | 88.64 315 | 73.48 295 | 81.75 342 | 89.61 321 | 73.19 310 | 82.05 251 | 73.71 346 | 66.07 277 | 95.87 273 | 71.18 273 | 84.60 236 | 92.41 279 |
|
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 57.99 329 | 54.91 330 | 67.24 342 | 88.51 316 | 65.59 336 | 52.21 359 | 90.33 307 | 43.58 355 | 42.84 355 | 51.18 357 | 20.29 361 | 85.07 351 | 34.77 356 | 70.45 334 | 51.05 357 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
EU-MVSNet | | | 81.32 290 | 80.95 276 | 82.42 322 | 88.50 317 | 63.67 339 | 93.32 206 | 91.33 283 | 64.02 344 | 80.57 271 | 92.83 172 | 61.21 302 | 92.27 329 | 76.34 238 | 80.38 294 | 91.32 300 |
|
our_test_3 | | | 81.93 280 | 80.46 280 | 86.33 301 | 88.46 318 | 73.48 295 | 88.46 303 | 91.11 286 | 76.46 279 | 76.69 298 | 88.25 292 | 66.89 265 | 94.36 311 | 68.75 292 | 79.08 304 | 91.14 304 |
|
ppachtmachnet_test | | | 81.84 281 | 80.07 286 | 87.15 290 | 88.46 318 | 74.43 286 | 89.04 296 | 92.16 256 | 75.33 291 | 77.75 292 | 88.99 278 | 66.20 273 | 95.37 294 | 65.12 318 | 77.60 309 | 91.65 294 |
|
lessismore_v0 | | | | | 86.04 302 | 88.46 318 | 68.78 329 | | 80.59 355 | | 73.01 326 | 90.11 265 | 55.39 323 | 96.43 252 | 75.06 250 | 65.06 342 | 92.90 264 |
|
test0.0.03 1 | | | 82.41 277 | 81.69 270 | 84.59 313 | 88.23 321 | 72.89 299 | 90.24 276 | 87.83 338 | 83.41 176 | 79.86 279 | 89.78 270 | 67.25 262 | 88.99 339 | 65.18 317 | 83.42 250 | 91.90 289 |
|
MDA-MVSNet_test_wron | | | 79.21 305 | 77.19 305 | 85.29 308 | 88.22 322 | 72.77 302 | 85.87 321 | 90.06 312 | 74.34 301 | 62.62 346 | 87.56 302 | 66.14 275 | 91.99 331 | 66.90 306 | 73.01 319 | 91.10 306 |
|
YYNet1 | | | 79.22 304 | 77.20 304 | 85.28 309 | 88.20 323 | 72.66 304 | 85.87 321 | 90.05 314 | 74.33 302 | 62.70 345 | 87.61 301 | 66.09 276 | 92.03 330 | 66.94 303 | 72.97 320 | 91.15 303 |
|
Test4 | | | 85.75 227 | 83.72 245 | 91.83 121 | 88.08 324 | 81.03 153 | 92.48 240 | 95.54 141 | 83.38 178 | 73.40 324 | 88.57 286 | 50.99 334 | 97.37 187 | 86.61 109 | 94.47 107 | 97.09 90 |
|
pmmvs6 | | | 83.42 268 | 81.60 271 | 88.87 241 | 88.01 325 | 77.87 252 | 94.96 92 | 94.24 210 | 74.67 299 | 78.80 285 | 91.09 246 | 60.17 308 | 96.49 247 | 77.06 234 | 75.40 316 | 92.23 285 |
|
testgi | | | 80.94 295 | 80.20 284 | 83.18 319 | 87.96 326 | 66.29 334 | 91.28 267 | 90.70 303 | 83.70 166 | 78.12 288 | 92.84 171 | 51.37 333 | 90.82 336 | 63.34 323 | 82.46 257 | 92.43 278 |
|
LP | | | 75.51 312 | 72.15 316 | 85.61 306 | 87.86 327 | 73.93 291 | 80.20 345 | 88.43 335 | 67.39 335 | 70.05 333 | 80.56 339 | 58.18 316 | 93.18 324 | 46.28 349 | 70.36 335 | 89.71 324 |
|
Anonymous20231206 | | | 81.03 293 | 79.77 289 | 84.82 312 | 87.85 328 | 70.26 323 | 91.42 265 | 92.08 259 | 73.67 306 | 77.75 292 | 89.25 276 | 62.43 292 | 93.08 325 | 61.50 329 | 82.00 264 | 91.12 305 |
|
OpenMVS_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 74.94 19 | 79.51 302 | 77.03 306 | 86.93 292 | 87.00 329 | 76.23 278 | 92.33 245 | 90.74 302 | 68.93 333 | 74.52 318 | 88.23 293 | 49.58 336 | 96.62 240 | 57.64 336 | 84.29 238 | 87.94 339 |
|
LF4IMVS | | | 80.37 297 | 79.07 297 | 84.27 317 | 86.64 330 | 69.87 326 | 89.39 290 | 91.05 293 | 76.38 281 | 74.97 316 | 90.00 266 | 47.85 340 | 94.25 313 | 74.55 255 | 80.82 287 | 88.69 333 |
|
MIMVSNet1 | | | 79.38 303 | 77.28 303 | 85.69 305 | 86.35 331 | 73.67 294 | 91.61 263 | 92.75 245 | 78.11 271 | 72.64 328 | 88.12 294 | 48.16 339 | 91.97 332 | 60.32 331 | 77.49 310 | 91.43 299 |
|
test20.03 | | | 79.95 299 | 79.08 296 | 82.55 321 | 85.79 332 | 67.74 332 | 91.09 272 | 91.08 291 | 81.23 239 | 74.48 319 | 89.96 268 | 61.63 296 | 90.15 337 | 60.08 332 | 76.38 313 | 89.76 322 |
|
Patchmatch-RL test | | | 81.67 283 | 79.96 287 | 86.81 297 | 85.42 333 | 71.23 314 | 82.17 341 | 87.50 342 | 78.47 264 | 77.19 297 | 82.50 332 | 70.81 209 | 93.48 319 | 82.66 150 | 72.89 321 | 95.71 135 |
|
UnsupCasMVSNet_eth | | | 80.07 298 | 78.27 300 | 85.46 307 | 85.24 334 | 72.63 305 | 88.45 304 | 94.87 191 | 82.99 195 | 71.64 332 | 88.07 295 | 56.34 320 | 91.75 333 | 73.48 262 | 63.36 346 | 92.01 288 |
|
testing_2 | | | 83.40 270 | 81.02 275 | 90.56 160 | 85.06 335 | 80.51 168 | 91.37 266 | 95.57 137 | 82.92 197 | 67.06 339 | 85.54 322 | 49.47 337 | 97.24 198 | 86.74 104 | 85.44 228 | 93.93 211 |
|
pmmvs-eth3d | | | 80.97 294 | 78.72 299 | 87.74 273 | 84.99 336 | 79.97 180 | 90.11 279 | 91.65 272 | 75.36 290 | 73.51 322 | 86.03 319 | 59.45 311 | 93.96 315 | 75.17 248 | 72.21 322 | 89.29 326 |
|
testpf | | | 71.41 319 | 72.11 317 | 69.30 339 | 84.53 337 | 59.79 343 | 62.74 356 | 83.14 350 | 71.11 326 | 68.83 337 | 81.57 337 | 46.70 342 | 84.83 352 | 74.51 256 | 75.86 315 | 63.30 352 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 59.16 21 | 80.52 296 | 79.20 294 | 84.48 314 | 83.98 338 | 67.63 333 | 89.95 282 | 93.84 229 | 64.79 343 | 66.81 340 | 91.14 244 | 57.93 317 | 95.17 301 | 76.25 239 | 88.10 206 | 90.65 316 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
UnsupCasMVSNet_bld | | | 76.23 311 | 73.27 313 | 85.09 311 | 83.79 339 | 72.92 298 | 85.65 325 | 93.47 234 | 71.52 322 | 68.84 336 | 79.08 342 | 49.77 335 | 93.21 322 | 66.81 307 | 60.52 348 | 89.13 330 |
|
PM-MVS | | | 78.11 307 | 76.12 309 | 84.09 318 | 83.54 340 | 70.08 324 | 88.97 297 | 85.27 347 | 79.93 248 | 74.73 317 | 86.43 311 | 34.70 352 | 93.48 319 | 79.43 209 | 72.06 323 | 88.72 332 |
|
DSMNet-mixed | | | 76.94 309 | 76.29 308 | 78.89 325 | 83.10 341 | 56.11 351 | 87.78 309 | 79.77 356 | 60.65 347 | 75.64 313 | 88.71 283 | 61.56 297 | 88.34 341 | 60.07 333 | 89.29 183 | 92.21 286 |
|
new_pmnet | | | 72.15 317 | 70.13 319 | 78.20 326 | 82.95 342 | 65.68 335 | 83.91 334 | 82.40 352 | 62.94 346 | 64.47 343 | 79.82 341 | 42.85 346 | 86.26 347 | 57.41 337 | 74.44 318 | 82.65 345 |
|
new-patchmatchnet | | | 76.41 310 | 75.17 310 | 80.13 324 | 82.65 343 | 59.61 344 | 87.66 311 | 91.08 291 | 78.23 269 | 69.85 334 | 83.22 329 | 54.76 325 | 91.63 335 | 64.14 322 | 64.89 343 | 89.16 328 |
|
testus | | | 74.41 314 | 73.35 312 | 77.59 330 | 82.49 344 | 57.08 347 | 86.02 319 | 90.21 308 | 72.28 319 | 72.89 327 | 84.32 325 | 37.08 350 | 86.96 345 | 52.24 340 | 82.65 255 | 88.73 331 |
|
test2356 | | | 74.50 313 | 73.27 313 | 78.20 326 | 80.81 345 | 59.84 342 | 83.76 336 | 88.33 336 | 71.43 324 | 72.37 329 | 81.84 335 | 45.60 344 | 86.26 347 | 50.97 341 | 84.32 237 | 88.50 335 |
|
ambc | | | | | 83.06 320 | 79.99 346 | 63.51 340 | 77.47 349 | 92.86 241 | | 74.34 320 | 84.45 324 | 28.74 353 | 95.06 305 | 73.06 264 | 68.89 339 | 90.61 317 |
|
1111 | | | 70.54 320 | 69.71 320 | 73.04 334 | 79.30 347 | 44.83 359 | 84.23 331 | 88.96 331 | 67.33 336 | 65.42 341 | 82.28 333 | 41.11 348 | 88.11 342 | 47.12 347 | 71.60 327 | 86.19 341 |
|
.test1245 | | | 57.63 330 | 61.79 327 | 45.14 348 | 79.30 347 | 44.83 359 | 84.23 331 | 88.96 331 | 67.33 336 | 65.42 341 | 82.28 333 | 41.11 348 | 88.11 342 | 47.12 347 | 0.39 362 | 2.46 363 |
|
TDRefinement | | | 79.81 300 | 77.34 302 | 87.22 288 | 79.24 349 | 75.48 283 | 93.12 217 | 92.03 261 | 76.45 280 | 75.01 315 | 91.58 218 | 49.19 338 | 96.44 251 | 70.22 278 | 69.18 337 | 89.75 323 |
|
test1235678 | | | 72.22 316 | 70.31 318 | 77.93 329 | 78.04 350 | 58.04 346 | 85.76 323 | 89.80 319 | 70.15 331 | 63.43 344 | 80.20 340 | 42.24 347 | 87.24 344 | 48.68 345 | 74.50 317 | 88.50 335 |
|
pmmvs3 | | | 71.81 318 | 68.71 321 | 81.11 323 | 75.86 351 | 70.42 322 | 86.74 315 | 83.66 349 | 58.95 348 | 68.64 338 | 80.89 338 | 36.93 351 | 89.52 338 | 63.10 325 | 63.59 345 | 83.39 343 |
|
DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | 56.31 346 | 74.23 352 | 51.81 355 | | 56.67 366 | 44.85 353 | 48.54 353 | 75.16 344 | 27.87 355 | 58.74 362 | 40.92 353 | 52.22 350 | 58.39 356 |
|
test12356 | | | 64.99 324 | 63.78 323 | 68.61 341 | 72.69 353 | 39.14 362 | 78.46 347 | 87.61 341 | 64.91 342 | 55.77 348 | 77.48 343 | 28.10 354 | 85.59 349 | 44.69 350 | 64.35 344 | 81.12 347 |
|
FPMVS | | | 64.63 325 | 62.55 325 | 70.88 336 | 70.80 354 | 56.71 348 | 84.42 330 | 84.42 348 | 51.78 351 | 49.57 351 | 81.61 336 | 23.49 358 | 81.48 354 | 40.61 354 | 76.25 314 | 74.46 351 |
|
PMMVS2 | | | 59.60 327 | 56.40 329 | 69.21 340 | 68.83 355 | 46.58 357 | 73.02 354 | 77.48 360 | 55.07 350 | 49.21 352 | 72.95 348 | 17.43 363 | 80.04 355 | 49.32 344 | 44.33 352 | 80.99 348 |
|
testmv | | | 65.49 323 | 62.66 324 | 73.96 333 | 68.78 356 | 53.14 354 | 84.70 329 | 88.56 333 | 65.94 341 | 52.35 350 | 74.65 345 | 25.02 357 | 85.14 350 | 43.54 351 | 60.40 349 | 83.60 342 |
|
PNet_i23d | | | 50.48 333 | 47.18 334 | 60.36 344 | 68.59 357 | 44.56 361 | 72.75 355 | 72.61 361 | 43.92 354 | 33.91 358 | 60.19 355 | 6.16 365 | 73.52 358 | 38.50 355 | 28.04 355 | 63.01 353 |
|
wuyk23d | | | 21.27 341 | 20.48 342 | 23.63 352 | 68.59 357 | 36.41 364 | 49.57 360 | 6.85 369 | 9.37 361 | 7.89 364 | 4.46 366 | 4.03 368 | 31.37 363 | 17.47 361 | 16.07 361 | 3.12 361 |
|
no-one | | | 61.56 326 | 56.58 328 | 76.49 332 | 67.80 359 | 62.76 341 | 78.13 348 | 86.11 343 | 63.16 345 | 43.24 354 | 64.70 352 | 26.12 356 | 88.95 340 | 50.84 342 | 29.15 354 | 77.77 349 |
|
E-PMN | | | 43.23 335 | 42.29 336 | 46.03 347 | 65.58 360 | 37.41 363 | 73.51 351 | 64.62 362 | 33.99 358 | 28.47 361 | 47.87 358 | 19.90 362 | 67.91 359 | 22.23 359 | 24.45 357 | 32.77 358 |
|
LCM-MVSNet | | | 66.00 322 | 62.16 326 | 77.51 331 | 64.51 361 | 58.29 345 | 83.87 335 | 90.90 297 | 48.17 352 | 54.69 349 | 73.31 347 | 16.83 364 | 86.75 346 | 65.47 315 | 61.67 347 | 87.48 340 |
|
EMVS | | | 42.07 336 | 41.12 337 | 44.92 349 | 63.45 362 | 35.56 365 | 73.65 350 | 63.48 363 | 33.05 359 | 26.88 362 | 45.45 360 | 21.27 360 | 67.14 360 | 19.80 360 | 23.02 359 | 32.06 359 |
|
wuykxyi23d | | | 50.55 332 | 44.13 335 | 69.81 338 | 56.77 363 | 54.58 353 | 73.22 353 | 80.78 354 | 39.79 357 | 22.08 363 | 46.69 359 | 4.03 368 | 79.71 356 | 47.65 346 | 26.13 356 | 75.14 350 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 39.65 23 | 43.39 334 | 38.59 340 | 57.77 345 | 56.52 364 | 48.77 356 | 55.38 358 | 58.64 365 | 29.33 360 | 28.96 360 | 52.65 356 | 4.68 367 | 64.62 361 | 28.11 358 | 33.07 353 | 59.93 355 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
ANet_high | | | 58.88 328 | 54.22 331 | 72.86 335 | 56.50 365 | 56.67 349 | 80.75 344 | 86.00 344 | 73.09 312 | 37.39 356 | 64.63 353 | 22.17 359 | 79.49 357 | 43.51 352 | 23.96 358 | 82.43 346 |
|
PMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 47.18 22 | 52.22 331 | 48.46 333 | 63.48 343 | 45.72 366 | 46.20 358 | 73.41 352 | 78.31 358 | 41.03 356 | 30.06 359 | 65.68 351 | 6.05 366 | 83.43 353 | 30.04 357 | 65.86 341 | 60.80 354 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
tmp_tt | | | 35.64 339 | 39.24 338 | 24.84 351 | 14.87 367 | 23.90 367 | 62.71 357 | 51.51 367 | 6.58 362 | 36.66 357 | 62.08 354 | 44.37 345 | 30.34 364 | 52.40 339 | 22.00 360 | 20.27 360 |
|
testmvs | | | 8.92 342 | 11.52 343 | 1.12 354 | 1.06 368 | 0.46 369 | 86.02 319 | 0.65 370 | 0.62 363 | 2.74 365 | 9.52 364 | 0.31 371 | 0.45 366 | 2.38 362 | 0.39 362 | 2.46 363 |
|
test123 | | | 8.76 343 | 11.22 344 | 1.39 353 | 0.85 369 | 0.97 368 | 85.76 323 | 0.35 371 | 0.54 364 | 2.45 366 | 8.14 365 | 0.60 370 | 0.48 365 | 2.16 363 | 0.17 364 | 2.71 362 |
|
cdsmvs_eth3d_5k | | | 22.14 340 | 29.52 341 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 95.76 125 | 0.00 365 | 0.00 367 | 94.29 118 | 75.66 145 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
pcd_1.5k_mvsjas | | | 6.64 345 | 8.86 346 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 79.70 94 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
sosnet-low-res | | | 0.00 346 | 0.00 347 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
sosnet | | | 0.00 346 | 0.00 347 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
uncertanet | | | 0.00 346 | 0.00 347 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
Regformer | | | 0.00 346 | 0.00 347 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
ab-mvs-re | | | 7.82 344 | 10.43 345 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 93.88 137 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
uanet | | | 0.00 346 | 0.00 347 | 0.00 355 | 0.00 370 | 0.00 370 | 0.00 361 | 0.00 372 | 0.00 365 | 0.00 367 | 0.00 367 | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
GSMVS | | | | | | | | | | | | | | | | | 96.12 116 |
|
test_part1 | | | | | 0.00 355 | | 0.00 370 | 0.00 361 | 97.45 7 | | | | 0.00 372 | 0.00 367 | 0.00 364 | 0.00 365 | 0.00 365 |
|
sam_mvs1 | | | | | | | | | | | | | 71.70 195 | | | | 96.12 116 |
|
sam_mvs | | | | | | | | | | | | | 70.60 211 | | | | |
|
MTGPA | ![Method available as binary. binary](img/icon_binary.png) | | | | | | | | 96.97 36 | | | | | | | | |
|
test_post1 | | | | | | | | 88.00 307 | | | | 9.81 363 | 69.31 230 | 95.53 283 | 76.65 235 | | |
|
test_post | | | | | | | | | | | | 10.29 362 | 70.57 215 | 95.91 272 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 83.76 327 | 71.53 199 | 96.48 248 | | | |
|
MTMP | | | | | | | | 96.16 31 | 60.64 364 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 91.91 43 | 98.71 19 | 98.07 46 |
|
agg_prior2 | | | | | | | | | | | | | | | 90.54 63 | 98.68 24 | 98.27 31 |
|
test_prior4 | | | | | | | 85.96 44 | 94.11 160 | | | | | | | | | |
|
test_prior2 | | | | | | | | 94.12 158 | | 87.67 82 | 92.63 48 | 96.39 53 | 86.62 25 | | 91.50 50 | 98.67 26 | |
|
旧先验2 | | | | | | | | 93.36 205 | | 71.25 325 | 94.37 15 | | | 97.13 207 | 86.74 104 | | |
|
新几何2 | | | | | | | | 93.11 219 | | | | | | | | | |
|
无先验 | | | | | | | | 93.28 212 | 96.26 85 | 73.95 304 | | | | 99.05 46 | 80.56 184 | | 96.59 104 |
|
原ACMM2 | | | | | | | | 92.94 228 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 98.75 79 | 78.30 219 | | |
|
segment_acmp | | | | | | | | | | | | | 87.16 21 | | | | |
|
testdata1 | | | | | | | | 92.15 250 | | 87.94 72 | | | | | | | |
|
plane_prior5 | | | | | | | | | 96.22 89 | | | | | 98.12 114 | 88.15 83 | 89.99 169 | 94.63 176 |
|
plane_prior4 | | | | | | | | | | | | 94.86 100 | | | | | |
|
plane_prior3 | | | | | | | 82.75 112 | | | 90.26 25 | 86.91 137 | | | | | | |
|
plane_prior2 | | | | | | | | 95.85 44 | | 90.81 18 | | | | | | | |
|
plane_prior | | | | | | | 82.73 115 | 95.21 77 | | 89.66 35 | | | | | | 89.88 173 | |
|
n2 | | | | | | | | | 0.00 372 | | | | | | | | |
|
nn | | | | | | | | | 0.00 372 | | | | | | | | |
|
door-mid | | | | | | | | | 85.49 345 | | | | | | | | |
|
test11 | | | | | | | | | 96.57 72 | | | | | | | | |
|
door | | | | | | | | | 85.33 346 | | | | | | | | |
|
HQP5-MVS | | | | | | | 81.56 134 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.11 101 | | |
|
HQP4-MVS | | | | | | | | | | | 85.43 180 | | | 97.96 135 | | | 94.51 186 |
|
HQP3-MVS | | | | | | | | | 96.04 104 | | | | | | | 89.77 175 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.83 171 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 55.91 352 | 87.62 312 | | 73.32 309 | 84.59 205 | | 70.33 218 | | 74.65 253 | | 95.50 139 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.47 212 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.01 209 | |
|
Test By Simon | | | | | | | | | | | | | 80.02 89 | | | | |
|