AdaColmap | | | 97.23 82 | 96.80 83 | 98.51 103 | 99.99 1 | 95.60 150 | 99.09 210 | 98.84 56 | 93.32 120 | 96.74 125 | 99.72 65 | 86.04 182 | 100.00 1 | 98.01 79 | 99.43 91 | 99.94 64 |
|
CNVR-MVS | | | 99.40 1 | 99.26 1 | 99.84 2 | 99.98 2 | 99.51 2 | 99.98 6 | 98.69 64 | 98.20 3 | 99.93 1 | 99.98 2 | 96.82 11 | 100.00 1 | 99.75 10 | 100.00 1 | 99.99 12 |
|
MCST-MVS | | | 99.32 3 | 99.14 3 | 99.86 1 | 99.97 3 | 99.59 1 | 99.97 12 | 98.64 72 | 98.47 2 | 99.13 56 | 99.92 7 | 96.38 21 | 100.00 1 | 99.74 12 | 100.00 1 | 100.00 1 |
|
mPP-MVS | | | 98.39 43 | 98.20 40 | 98.97 74 | 99.97 3 | 96.92 108 | 99.95 31 | 98.38 132 | 95.04 67 | 98.61 80 | 99.80 44 | 93.39 90 | 100.00 1 | 98.64 61 | 100.00 1 | 99.98 43 |
|
CPTT-MVS | | | 97.64 70 | 97.32 69 | 98.58 97 | 99.97 3 | 95.77 142 | 99.96 19 | 98.35 137 | 89.90 219 | 98.36 89 | 99.79 45 | 91.18 128 | 99.99 26 | 98.37 69 | 99.99 14 | 99.99 12 |
|
DP-MVS Recon | | | 98.41 41 | 98.02 47 | 99.56 16 | 99.97 3 | 98.70 35 | 99.92 52 | 98.44 109 | 92.06 172 | 98.40 88 | 99.84 35 | 95.68 32 | 100.00 1 | 98.19 71 | 99.71 72 | 99.97 53 |
|
PAPR | | | 98.52 34 | 98.16 42 | 99.58 15 | 99.97 3 | 98.77 26 | 99.95 31 | 98.43 115 | 95.35 62 | 98.03 101 | 99.75 61 | 94.03 76 | 99.98 31 | 98.11 75 | 99.83 61 | 99.99 12 |
|
HFP-MVS | | | 98.56 30 | 98.37 32 | 99.14 53 | 99.96 8 | 97.43 85 | 99.95 31 | 98.61 78 | 94.77 73 | 99.31 47 | 99.85 21 | 94.22 69 | 100.00 1 | 98.70 55 | 99.98 25 | 99.98 43 |
|
region2R | | | 98.54 32 | 98.37 32 | 99.05 67 | 99.96 8 | 97.18 99 | 99.96 19 | 98.55 90 | 94.87 71 | 99.45 37 | 99.85 21 | 94.07 75 | 100.00 1 | 98.67 57 | 100.00 1 | 99.98 43 |
|
#test# | | | 98.59 28 | 98.41 27 | 99.14 53 | 99.96 8 | 97.43 85 | 99.95 31 | 98.61 78 | 95.00 68 | 99.31 47 | 99.85 21 | 94.22 69 | 100.00 1 | 98.78 52 | 99.98 25 | 99.98 43 |
|
ACMMPR | | | 98.50 35 | 98.32 36 | 99.05 67 | 99.96 8 | 97.18 99 | 99.95 31 | 98.60 80 | 94.77 73 | 99.31 47 | 99.84 35 | 93.73 85 | 100.00 1 | 98.70 55 | 99.98 25 | 99.98 43 |
|
NCCC | | | 99.37 2 | 99.25 2 | 99.71 5 | 99.96 8 | 99.15 9 | 99.97 12 | 98.62 76 | 98.02 6 | 99.90 2 | 99.95 3 | 97.33 9 | 100.00 1 | 99.54 20 | 100.00 1 | 100.00 1 |
|
CP-MVS | | | 98.45 38 | 98.32 36 | 98.87 80 | 99.96 8 | 96.62 114 | 99.97 12 | 98.39 129 | 94.43 83 | 98.90 66 | 99.87 15 | 94.30 67 | 100.00 1 | 99.04 39 | 99.99 14 | 99.99 12 |
|
XVS | | | 98.70 23 | 98.55 22 | 99.15 51 | 99.94 14 | 97.50 81 | 99.94 45 | 98.42 123 | 96.22 40 | 99.41 41 | 99.78 50 | 94.34 64 | 99.96 42 | 98.92 44 | 99.95 39 | 99.99 12 |
|
test_prior3 | | | 98.99 11 | 98.84 12 | 99.43 27 | 99.94 14 | 98.49 50 | 99.95 31 | 98.65 69 | 95.78 50 | 99.73 14 | 99.76 56 | 96.00 24 | 99.80 90 | 99.78 8 | 100.00 1 | 99.99 12 |
|
X-MVStestdata | | | 93.83 177 | 92.06 199 | 99.15 51 | 99.94 14 | 97.50 81 | 99.94 45 | 98.42 123 | 96.22 40 | 99.41 41 | 41.37 364 | 94.34 64 | 99.96 42 | 98.92 44 | 99.95 39 | 99.99 12 |
|
test_prior | | | | | 99.43 27 | 99.94 14 | 98.49 50 | | 98.65 69 | | | | | 99.80 90 | | | 99.99 12 |
|
MSLP-MVS++ | | | 99.13 5 | 99.01 7 | 99.49 23 | 99.94 14 | 98.46 52 | 99.98 6 | 98.86 54 | 97.10 16 | 99.80 8 | 99.94 4 | 95.92 28 | 100.00 1 | 99.51 21 | 100.00 1 | 100.00 1 |
|
APDe-MVS | | | 99.06 8 | 98.91 10 | 99.51 21 | 99.94 14 | 98.76 32 | 99.91 56 | 98.39 129 | 97.20 15 | 99.46 36 | 99.85 21 | 95.53 37 | 99.79 92 | 99.86 4 | 100.00 1 | 99.99 12 |
|
MP-MVS | | | 98.23 50 | 97.97 50 | 99.03 69 | 99.94 14 | 97.17 102 | 99.95 31 | 98.39 129 | 94.70 76 | 98.26 95 | 99.81 43 | 91.84 119 | 100.00 1 | 98.85 48 | 99.97 34 | 99.93 65 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
CDPH-MVS | | | 98.65 24 | 98.36 34 | 99.49 23 | 99.94 14 | 98.73 33 | 99.87 69 | 98.33 139 | 93.97 101 | 99.76 12 | 99.87 15 | 94.99 50 | 99.75 99 | 98.55 64 | 100.00 1 | 99.98 43 |
|
PAPM_NR | | | 98.12 53 | 97.93 53 | 98.70 87 | 99.94 14 | 96.13 133 | 99.82 96 | 98.43 115 | 94.56 79 | 97.52 110 | 99.70 69 | 94.40 60 | 99.98 31 | 97.00 105 | 99.98 25 | 99.99 12 |
|
MG-MVS | | | 98.91 14 | 98.65 16 | 99.68 7 | 99.94 14 | 99.07 11 | 99.64 151 | 99.44 23 | 97.33 13 | 99.00 64 | 99.72 65 | 94.03 76 | 99.98 31 | 98.73 54 | 100.00 1 | 100.00 1 |
|
HSP-MVS | | | 99.07 6 | 99.11 4 | 98.95 76 | 99.93 24 | 97.24 96 | 99.95 31 | 98.32 140 | 97.50 10 | 99.52 33 | 99.88 12 | 97.43 6 | 99.71 107 | 99.50 22 | 99.98 25 | 99.89 71 |
|
agg_prior1 | | | 98.88 15 | 98.66 15 | 99.54 18 | 99.93 24 | 98.77 26 | 99.96 19 | 98.43 115 | 94.63 78 | 99.63 22 | 99.85 21 | 95.79 31 | 99.85 80 | 99.72 16 | 99.99 14 | 99.99 12 |
|
agg_prior | | | | | | 99.93 24 | 98.77 26 | | 98.43 115 | | 99.63 22 | | | 99.85 80 | | | |
|
TEST9 | | | | | | 99.92 27 | 98.92 15 | 99.96 19 | 98.43 115 | 93.90 105 | 99.71 16 | 99.86 17 | 95.88 29 | 99.85 80 | | | |
|
train_agg | | | 98.88 15 | 98.65 16 | 99.59 14 | 99.92 27 | 98.92 15 | 99.96 19 | 98.43 115 | 94.35 85 | 99.71 16 | 99.86 17 | 95.94 26 | 99.85 80 | 99.69 18 | 99.98 25 | 99.99 12 |
|
test_8 | | | | | | 99.92 27 | 98.88 18 | 99.96 19 | 98.43 115 | 94.35 85 | 99.69 18 | 99.85 21 | 95.94 26 | 99.85 80 | | | |
|
agg_prior3 | | | 98.84 17 | 98.62 18 | 99.47 26 | 99.92 27 | 98.56 46 | 99.96 19 | 98.43 115 | 94.07 95 | 99.67 19 | 99.85 21 | 96.05 22 | 99.85 80 | 99.69 18 | 99.98 25 | 99.99 12 |
|
PGM-MVS | | | 98.34 44 | 98.13 44 | 98.99 73 | 99.92 27 | 97.00 104 | 99.75 117 | 99.50 21 | 93.90 105 | 99.37 45 | 99.76 56 | 93.24 95 | 100.00 1 | 97.75 91 | 99.96 36 | 99.98 43 |
|
ACMMP | | | 97.74 67 | 97.44 64 | 98.66 90 | 99.92 27 | 96.13 133 | 99.18 204 | 99.45 22 | 94.84 72 | 96.41 134 | 99.71 67 | 91.40 122 | 99.99 26 | 97.99 81 | 98.03 120 | 99.87 74 |
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 |
HPM-MVS++ | | | 99.07 6 | 98.88 11 | 99.63 8 | 99.90 33 | 99.02 12 | 99.95 31 | 98.56 86 | 97.56 9 | 99.44 38 | 99.85 21 | 95.38 39 | 100.00 1 | 99.31 29 | 99.99 14 | 99.87 74 |
|
APD-MVS | | | 98.62 25 | 98.35 35 | 99.41 31 | 99.90 33 | 98.51 49 | 99.87 69 | 98.36 136 | 94.08 94 | 99.74 13 | 99.73 64 | 94.08 74 | 99.74 103 | 99.42 26 | 99.99 14 | 99.99 12 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DeepC-MVS_fast | | 96.59 1 | 98.81 18 | 98.54 23 | 99.62 11 | 99.90 33 | 98.85 21 | 99.24 200 | 98.47 105 | 98.14 4 | 99.08 57 | 99.91 8 | 93.09 98 | 100.00 1 | 99.04 39 | 99.99 14 | 100.00 1 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ESAPD | | | 99.26 4 | 99.10 5 | 99.74 3 | 99.89 36 | 99.24 7 | 99.87 69 | 98.44 109 | 97.48 11 | 99.64 21 | 99.94 4 | 96.68 12 | 99.99 26 | 99.99 1 | 100.00 1 | 99.99 12 |
|
test_part2 | | | | | | 99.89 36 | 99.25 6 | | | | 99.49 34 | | | | | | |
|
v1.0 | | | 41.15 337 | 54.87 331 | 0.00 354 | 99.89 36 | 0.00 369 | 0.00 360 | 98.41 125 | 96.14 44 | 99.49 34 | 99.91 8 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
CSCG | | | 97.10 86 | 97.04 77 | 97.27 157 | 99.89 36 | 91.92 239 | 99.90 59 | 99.07 34 | 88.67 238 | 95.26 156 | 99.82 40 | 93.17 97 | 99.98 31 | 98.15 73 | 99.47 88 | 99.90 70 |
|
SMA-MVS | | | 98.76 21 | 98.48 25 | 99.62 11 | 99.87 40 | 98.87 19 | 99.86 86 | 98.38 132 | 93.19 122 | 99.77 11 | 99.94 4 | 95.54 34 | 100.00 1 | 99.74 12 | 99.99 14 | 100.00 1 |
|
PHI-MVS | | | 98.41 41 | 98.21 39 | 99.03 69 | 99.86 41 | 97.10 103 | 99.98 6 | 98.80 59 | 90.78 208 | 99.62 24 | 99.78 50 | 95.30 40 | 100.00 1 | 99.80 6 | 99.93 48 | 99.99 12 |
|
zzz-MVS | | | 98.33 45 | 98.00 48 | 99.30 38 | 99.85 42 | 97.93 68 | 99.80 101 | 98.28 144 | 95.76 52 | 97.18 117 | 99.88 12 | 92.74 103 | 100.00 1 | 98.67 57 | 99.88 56 | 99.99 12 |
|
MTAPA | | | 98.29 46 | 97.96 52 | 99.30 38 | 99.85 42 | 97.93 68 | 99.39 183 | 98.28 144 | 95.76 52 | 97.18 117 | 99.88 12 | 92.74 103 | 100.00 1 | 98.67 57 | 99.88 56 | 99.99 12 |
|
Regformer-1 | | | 98.79 19 | 98.60 20 | 99.36 36 | 99.85 42 | 98.34 54 | 99.87 69 | 98.52 93 | 96.05 45 | 99.41 41 | 99.79 45 | 94.93 52 | 99.76 96 | 99.07 34 | 99.90 52 | 99.99 12 |
|
Regformer-2 | | | 98.78 20 | 98.59 21 | 99.36 36 | 99.85 42 | 98.32 55 | 99.87 69 | 98.52 93 | 96.04 46 | 99.41 41 | 99.79 45 | 94.92 53 | 99.76 96 | 99.05 35 | 99.90 52 | 99.98 43 |
|
LS3D | | | 95.84 139 | 95.11 147 | 98.02 133 | 99.85 42 | 95.10 163 | 98.74 247 | 98.50 103 | 87.22 263 | 93.66 182 | 99.86 17 | 87.45 169 | 99.95 50 | 90.94 202 | 99.81 67 | 99.02 182 |
|
Regformer-3 | | | 98.58 29 | 98.41 27 | 99.10 59 | 99.84 47 | 97.57 77 | 99.66 144 | 98.52 93 | 95.79 49 | 99.01 62 | 99.77 52 | 94.40 60 | 99.75 99 | 98.82 49 | 99.83 61 | 99.98 43 |
|
Regformer-4 | | | 98.56 30 | 98.39 30 | 99.08 61 | 99.84 47 | 97.52 79 | 99.66 144 | 98.52 93 | 95.76 52 | 99.01 62 | 99.77 52 | 94.33 66 | 99.75 99 | 98.80 51 | 99.83 61 | 99.98 43 |
|
HPM-MVS | | | 97.96 57 | 97.72 56 | 98.68 88 | 99.84 47 | 96.39 122 | 99.90 59 | 98.17 157 | 92.61 147 | 98.62 79 | 99.57 84 | 91.87 118 | 99.67 114 | 98.87 47 | 99.99 14 | 99.99 12 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
EI-MVSNet-Vis-set | | | 98.27 47 | 98.11 45 | 98.75 85 | 99.83 50 | 96.59 116 | 99.40 180 | 98.51 99 | 95.29 64 | 98.51 83 | 99.76 56 | 93.60 89 | 99.71 107 | 98.53 65 | 99.52 85 | 99.95 62 |
|
PLC | | 95.54 3 | 97.93 59 | 97.89 54 | 98.05 132 | 99.82 51 | 94.77 171 | 99.92 52 | 98.46 107 | 93.93 104 | 97.20 115 | 99.27 102 | 95.44 38 | 99.97 40 | 97.41 95 | 99.51 87 | 99.41 138 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
APD-MVS_3200maxsize | | | 98.25 49 | 98.08 46 | 98.78 83 | 99.81 52 | 96.60 115 | 99.82 96 | 98.30 142 | 93.95 103 | 99.37 45 | 99.77 52 | 92.84 100 | 99.76 96 | 98.95 41 | 99.92 50 | 99.97 53 |
|
EI-MVSNet-UG-set | | | 98.14 52 | 97.99 49 | 98.60 95 | 99.80 53 | 96.27 124 | 99.36 187 | 98.50 103 | 95.21 66 | 98.30 92 | 99.75 61 | 93.29 94 | 99.73 106 | 98.37 69 | 99.30 95 | 99.81 79 |
|
HPM-MVS_fast | | | 97.80 64 | 97.50 63 | 98.68 88 | 99.79 54 | 96.42 119 | 99.88 66 | 98.16 160 | 91.75 179 | 98.94 65 | 99.54 87 | 91.82 120 | 99.65 116 | 97.62 93 | 99.99 14 | 99.99 12 |
|
旧先验1 | | | | | | 99.76 55 | 97.52 79 | | 98.64 72 | | | 99.85 21 | 95.63 33 | | | 99.94 43 | 99.99 12 |
|
OMC-MVS | | | 97.28 79 | 97.23 70 | 97.41 152 | 99.76 55 | 93.36 205 | 99.65 147 | 97.95 178 | 96.03 47 | 97.41 112 | 99.70 69 | 89.61 142 | 99.51 121 | 96.73 112 | 98.25 115 | 99.38 145 |
|
新几何1 | | | | | 99.42 30 | 99.75 57 | 98.27 57 | | 98.63 75 | 92.69 141 | 99.55 29 | 99.82 40 | 94.40 60 | 100.00 1 | 91.21 195 | 99.94 43 | 99.99 12 |
|
MP-MVS-pluss | | | 98.07 55 | 97.64 58 | 99.38 35 | 99.74 58 | 98.41 53 | 99.74 120 | 98.18 156 | 93.35 119 | 96.45 131 | 99.85 21 | 92.64 106 | 99.97 40 | 98.91 46 | 99.89 54 | 99.77 85 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
TSAR-MVS + MP. | | | 98.93 12 | 98.77 13 | 99.41 31 | 99.74 58 | 98.67 36 | 99.77 110 | 98.38 132 | 96.73 27 | 99.88 3 | 99.74 63 | 94.89 54 | 99.59 118 | 99.80 6 | 99.98 25 | 99.97 53 |
|
1121 | | | 98.03 56 | 97.57 62 | 99.40 33 | 99.74 58 | 98.21 58 | 98.31 280 | 98.62 76 | 92.78 136 | 99.53 30 | 99.83 37 | 95.08 44 | 100.00 1 | 94.36 147 | 99.92 50 | 99.99 12 |
|
test12 | | | | | 99.43 27 | 99.74 58 | 98.56 46 | | 98.40 127 | | 99.65 20 | | 94.76 55 | 99.75 99 | | 99.98 25 | 99.99 12 |
|
原ACMM1 | | | | | 98.96 75 | 99.73 62 | 96.99 105 | | 98.51 99 | 94.06 98 | 99.62 24 | 99.85 21 | 94.97 51 | 99.96 42 | 95.11 130 | 99.95 39 | 99.92 68 |
|
TSAR-MVS + GP. | | | 98.60 26 | 98.51 24 | 98.86 81 | 99.73 62 | 96.63 113 | 99.97 12 | 97.92 182 | 98.07 5 | 98.76 71 | 99.55 85 | 95.00 49 | 99.94 58 | 99.91 3 | 97.68 124 | 99.99 12 |
|
CANet | | | 98.27 47 | 97.82 55 | 99.63 8 | 99.72 64 | 99.10 10 | 99.98 6 | 98.51 99 | 97.00 19 | 98.52 82 | 99.71 67 | 87.80 164 | 99.95 50 | 99.75 10 | 99.38 92 | 99.83 77 |
|
F-COLMAP | | | 96.93 92 | 96.95 79 | 96.87 165 | 99.71 65 | 91.74 245 | 99.85 88 | 97.95 178 | 93.11 125 | 95.72 149 | 99.16 110 | 92.35 108 | 99.94 58 | 95.32 128 | 99.35 93 | 98.92 184 |
|
SD-MVS | | | 98.92 13 | 98.70 14 | 99.56 16 | 99.70 66 | 98.73 33 | 99.94 45 | 98.34 138 | 96.38 35 | 99.81 7 | 99.76 56 | 94.59 57 | 99.98 31 | 99.84 5 | 99.96 36 | 99.97 53 |
|
abl_6 | | | 97.67 69 | 97.34 67 | 98.66 90 | 99.68 67 | 96.11 137 | 99.68 139 | 98.14 163 | 93.80 108 | 99.27 50 | 99.70 69 | 88.65 160 | 99.98 31 | 97.46 94 | 99.72 71 | 99.89 71 |
|
ACMMP_Plus | | | 98.49 36 | 98.14 43 | 99.54 18 | 99.66 68 | 98.62 41 | 99.85 88 | 98.37 135 | 94.68 77 | 99.53 30 | 99.83 37 | 92.87 99 | 100.00 1 | 98.66 60 | 99.84 60 | 99.99 12 |
|
DeepPCF-MVS | | 95.94 2 | 97.71 68 | 98.98 8 | 93.92 254 | 99.63 69 | 81.76 329 | 99.96 19 | 98.56 86 | 99.47 1 | 99.19 54 | 99.99 1 | 94.16 73 | 100.00 1 | 99.92 2 | 99.93 48 | 100.00 1 |
|
EPNet | | | 98.49 36 | 98.40 29 | 98.77 84 | 99.62 70 | 96.80 111 | 99.90 59 | 99.51 20 | 97.60 8 | 99.20 52 | 99.36 100 | 93.71 86 | 99.91 64 | 97.99 81 | 98.71 105 | 99.61 108 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVS_0304 | | | 97.52 72 | 96.79 84 | 99.69 6 | 99.59 71 | 99.30 4 | 99.97 12 | 98.01 172 | 96.99 20 | 98.84 67 | 99.79 45 | 78.90 261 | 99.96 42 | 99.74 12 | 99.32 94 | 99.81 79 |
|
PVSNet_BlendedMVS | | | 96.05 134 | 95.82 125 | 96.72 170 | 99.59 71 | 96.99 105 | 99.95 31 | 99.10 31 | 94.06 98 | 98.27 93 | 95.80 235 | 89.00 155 | 99.95 50 | 99.12 32 | 87.53 242 | 93.24 297 |
|
PVSNet_Blended | | | 97.94 58 | 97.64 58 | 98.83 82 | 99.59 71 | 96.99 105 | 100.00 1 | 99.10 31 | 95.38 61 | 98.27 93 | 99.08 113 | 89.00 155 | 99.95 50 | 99.12 32 | 99.25 96 | 99.57 118 |
|
PatchMatch-RL | | | 96.04 135 | 95.40 137 | 97.95 134 | 99.59 71 | 95.22 162 | 99.52 167 | 99.07 34 | 93.96 102 | 96.49 129 | 98.35 172 | 82.28 208 | 99.82 89 | 90.15 215 | 99.22 97 | 98.81 188 |
|
test222 | | | | | | 99.55 75 | 97.41 88 | 99.34 188 | 98.55 90 | 91.86 176 | 99.27 50 | 99.83 37 | 93.84 83 | | | 99.95 39 | 99.99 12 |
|
CNLPA | | | 97.76 66 | 97.38 65 | 98.92 78 | 99.53 76 | 96.84 109 | 99.87 69 | 98.14 163 | 93.78 109 | 96.55 128 | 99.69 72 | 92.28 110 | 99.98 31 | 97.13 101 | 99.44 90 | 99.93 65 |
|
API-MVS | | | 97.86 60 | 97.66 57 | 98.47 109 | 99.52 77 | 95.41 154 | 99.47 174 | 98.87 53 | 91.68 181 | 98.84 67 | 99.85 21 | 92.34 109 | 99.99 26 | 98.44 67 | 99.96 36 | 100.00 1 |
|
PVSNet | | 91.05 13 | 97.13 85 | 96.69 87 | 98.45 111 | 99.52 77 | 95.81 140 | 99.95 31 | 99.65 16 | 94.73 75 | 99.04 60 | 99.21 108 | 84.48 194 | 99.95 50 | 94.92 133 | 98.74 104 | 99.58 116 |
|
114514_t | | | 97.41 77 | 96.83 81 | 99.14 53 | 99.51 79 | 97.83 70 | 99.89 64 | 98.27 147 | 88.48 241 | 99.06 58 | 99.66 78 | 90.30 136 | 99.64 117 | 96.32 115 | 99.97 34 | 99.96 57 |
|
testdata | | | | | 98.42 114 | 99.47 80 | 95.33 156 | | 98.56 86 | 93.78 109 | 99.79 10 | 99.85 21 | 93.64 88 | 99.94 58 | 94.97 132 | 99.94 43 | 100.00 1 |
|
MAR-MVS | | | 97.43 73 | 97.19 71 | 98.15 128 | 99.47 80 | 94.79 170 | 99.05 222 | 98.76 60 | 92.65 145 | 98.66 77 | 99.82 40 | 88.52 161 | 99.98 31 | 98.12 74 | 99.63 76 | 99.67 98 |
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 |
DP-MVS | | | 94.54 166 | 93.42 177 | 97.91 136 | 99.46 82 | 94.04 182 | 98.93 233 | 97.48 222 | 81.15 319 | 90.04 214 | 99.55 85 | 87.02 174 | 99.95 50 | 88.97 230 | 98.11 116 | 99.73 90 |
|
MVS_111021_LR | | | 98.42 40 | 98.38 31 | 98.53 102 | 99.39 83 | 95.79 141 | 99.87 69 | 99.86 2 | 96.70 28 | 98.78 70 | 99.79 45 | 92.03 115 | 99.90 65 | 99.17 31 | 99.86 59 | 99.88 73 |
|
CHOSEN 280x420 | | | 99.01 10 | 99.03 6 | 98.95 76 | 99.38 84 | 98.87 19 | 98.46 270 | 99.42 25 | 97.03 18 | 99.02 61 | 99.09 112 | 99.35 1 | 98.21 202 | 99.73 15 | 99.78 68 | 99.77 85 |
|
MVS_111021_HR | | | 98.72 22 | 98.62 18 | 99.01 72 | 99.36 85 | 97.18 99 | 99.93 50 | 99.90 1 | 96.81 25 | 98.67 75 | 99.77 52 | 93.92 78 | 99.89 69 | 99.27 30 | 99.94 43 | 99.96 57 |
|
TAPA-MVS | | 92.12 8 | 94.42 170 | 93.60 170 | 96.90 164 | 99.33 86 | 91.78 243 | 99.78 105 | 98.00 173 | 89.89 220 | 94.52 172 | 99.47 91 | 91.97 116 | 99.18 139 | 69.90 331 | 99.52 85 | 99.73 90 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
0601test | | | 97.83 62 | 97.37 66 | 99.21 42 | 99.18 87 | 97.98 66 | 99.64 151 | 99.27 29 | 91.43 189 | 97.88 104 | 98.99 120 | 95.84 30 | 99.84 88 | 98.82 49 | 95.32 171 | 99.79 82 |
|
DeepC-MVS | | 94.51 4 | 96.92 93 | 96.40 95 | 98.45 111 | 99.16 88 | 95.90 139 | 99.66 144 | 98.06 169 | 96.37 38 | 94.37 175 | 99.49 90 | 83.29 203 | 99.90 65 | 97.63 92 | 99.61 80 | 99.55 121 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DELS-MVS | | | 98.54 32 | 98.22 38 | 99.50 22 | 99.15 89 | 98.65 39 | 100.00 1 | 98.58 82 | 97.70 7 | 98.21 97 | 99.24 106 | 92.58 107 | 99.94 58 | 98.63 62 | 99.94 43 | 99.92 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 |
Anonymous202405211 | | | 93.10 192 | 91.99 200 | 96.40 179 | 99.10 90 | 89.65 282 | 98.88 236 | 97.93 180 | 83.71 300 | 94.00 179 | 98.75 151 | 68.79 314 | 99.88 75 | 95.08 131 | 91.71 210 | 99.68 96 |
|
tfpn_ndepth | | | 97.21 83 | 96.63 88 | 98.92 78 | 99.06 91 | 98.28 56 | 99.95 31 | 98.91 43 | 92.96 128 | 96.49 129 | 98.67 154 | 97.40 7 | 99.07 141 | 91.87 191 | 94.38 181 | 99.41 138 |
|
HyFIR lowres test | | | 96.66 108 | 96.43 94 | 97.36 155 | 99.05 92 | 93.91 185 | 99.70 133 | 99.80 3 | 90.54 209 | 96.26 136 | 98.08 177 | 92.15 113 | 98.23 201 | 96.84 111 | 95.46 168 | 99.93 65 |
|
LFMVS | | | 94.75 161 | 93.56 173 | 98.30 121 | 99.03 93 | 95.70 148 | 98.74 247 | 97.98 175 | 87.81 250 | 98.47 84 | 99.39 97 | 67.43 321 | 99.53 119 | 98.01 79 | 95.20 172 | 99.67 98 |
|
AllTest | | | 92.48 203 | 91.64 203 | 95.00 209 | 99.01 94 | 88.43 293 | 98.94 232 | 96.82 290 | 86.50 272 | 88.71 246 | 98.47 170 | 74.73 291 | 99.88 75 | 85.39 271 | 96.18 153 | 96.71 207 |
|
TestCases | | | | | 95.00 209 | 99.01 94 | 88.43 293 | | 96.82 290 | 86.50 272 | 88.71 246 | 98.47 170 | 74.73 291 | 99.88 75 | 85.39 271 | 96.18 153 | 96.71 207 |
|
tfpn1000 | | | 96.90 94 | 96.29 97 | 98.74 86 | 99.00 96 | 98.09 62 | 99.92 52 | 98.91 43 | 92.08 169 | 95.85 142 | 98.65 156 | 97.39 8 | 98.83 148 | 90.56 207 | 94.23 189 | 99.31 153 |
|
COLMAP_ROB | | 90.47 14 | 92.18 209 | 91.49 207 | 94.25 241 | 99.00 96 | 88.04 298 | 98.42 275 | 96.70 293 | 82.30 309 | 88.43 251 | 99.01 117 | 76.97 272 | 99.85 80 | 86.11 265 | 96.50 151 | 94.86 215 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
HY-MVS | | 92.50 7 | 97.79 65 | 97.17 73 | 99.63 8 | 98.98 98 | 99.32 3 | 97.49 301 | 99.52 18 | 95.69 56 | 98.32 91 | 97.41 190 | 93.32 92 | 99.77 94 | 98.08 78 | 95.75 164 | 99.81 79 |
|
VNet | | | 97.21 83 | 96.57 91 | 99.13 58 | 98.97 99 | 97.82 71 | 99.03 224 | 99.21 30 | 94.31 87 | 99.18 55 | 98.88 129 | 86.26 181 | 99.89 69 | 98.93 43 | 94.32 186 | 99.69 95 |
|
thres200 | | | 96.96 90 | 96.21 99 | 99.22 41 | 98.97 99 | 98.84 22 | 99.85 88 | 99.71 5 | 93.17 123 | 96.26 136 | 98.88 129 | 89.87 140 | 99.51 121 | 94.26 151 | 94.91 174 | 99.31 153 |
|
tfpn200view9 | | | 96.79 97 | 95.99 105 | 99.19 43 | 98.94 101 | 98.82 23 | 99.78 105 | 99.71 5 | 92.86 129 | 96.02 139 | 98.87 131 | 89.33 143 | 99.50 123 | 93.84 158 | 94.57 175 | 99.27 158 |
|
thres400 | | | 96.78 98 | 95.99 105 | 99.16 47 | 98.94 101 | 98.82 23 | 99.78 105 | 99.71 5 | 92.86 129 | 96.02 139 | 98.87 131 | 89.33 143 | 99.50 123 | 93.84 158 | 94.57 175 | 99.16 169 |
|
Anonymous20231211 | | | 89.86 261 | 88.44 266 | 94.13 245 | 98.93 103 | 90.68 263 | 98.54 264 | 98.26 148 | 76.28 331 | 86.73 270 | 95.54 243 | 70.60 308 | 97.56 223 | 90.82 205 | 80.27 286 | 94.15 246 |
|
canonicalmvs | | | 97.09 87 | 96.32 96 | 99.39 34 | 98.93 103 | 98.95 14 | 99.72 131 | 97.35 236 | 94.45 81 | 97.88 104 | 99.42 93 | 86.71 176 | 99.52 120 | 98.48 66 | 93.97 199 | 99.72 92 |
|
EPNet_dtu | | | 95.71 142 | 95.39 138 | 96.66 172 | 98.92 105 | 93.41 201 | 99.57 159 | 98.90 51 | 96.19 42 | 97.52 110 | 98.56 164 | 92.65 105 | 97.36 230 | 77.89 316 | 98.33 111 | 99.20 163 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
WTY-MVS | | | 98.10 54 | 97.60 60 | 99.60 13 | 98.92 105 | 99.28 5 | 99.89 64 | 99.52 18 | 95.58 58 | 98.24 96 | 99.39 97 | 93.33 91 | 99.74 103 | 97.98 83 | 95.58 167 | 99.78 84 |
|
CHOSEN 1792x2688 | | | 96.81 96 | 96.53 93 | 97.64 144 | 98.91 107 | 93.07 211 | 99.65 147 | 99.80 3 | 95.64 57 | 95.39 153 | 98.86 133 | 84.35 196 | 99.90 65 | 96.98 106 | 99.16 98 | 99.95 62 |
|
tfpn111 | | | 96.69 104 | 95.87 123 | 99.16 47 | 98.90 108 | 98.77 26 | 99.74 120 | 99.71 5 | 92.59 149 | 95.84 143 | 98.86 133 | 89.25 145 | 99.50 123 | 93.44 169 | 94.50 179 | 99.20 163 |
|
conf200view11 | | | 96.73 103 | 95.92 113 | 99.16 47 | 98.90 108 | 98.77 26 | 99.74 120 | 99.71 5 | 92.59 149 | 95.84 143 | 98.86 133 | 89.25 145 | 99.50 123 | 93.84 158 | 94.57 175 | 99.20 163 |
|
thres100view900 | | | 96.74 101 | 95.92 113 | 99.18 44 | 98.90 108 | 98.77 26 | 99.74 120 | 99.71 5 | 92.59 149 | 95.84 143 | 98.86 133 | 89.25 145 | 99.50 123 | 93.84 158 | 94.57 175 | 99.27 158 |
|
thres600view7 | | | 96.69 104 | 95.87 123 | 99.14 53 | 98.90 108 | 98.78 25 | 99.74 120 | 99.71 5 | 92.59 149 | 95.84 143 | 98.86 133 | 89.25 145 | 99.50 123 | 93.44 169 | 94.50 179 | 99.16 169 |
|
MSDG | | | 94.37 172 | 93.36 181 | 97.40 153 | 98.88 112 | 93.95 184 | 99.37 185 | 97.38 234 | 85.75 284 | 90.80 204 | 99.17 109 | 84.11 198 | 99.88 75 | 86.35 262 | 98.43 109 | 98.36 193 |
|
view600 | | | 96.46 119 | 95.59 129 | 99.06 63 | 98.87 113 | 98.60 42 | 99.69 134 | 99.71 5 | 92.20 162 | 95.23 157 | 98.80 145 | 89.17 149 | 99.43 131 | 92.29 182 | 94.37 182 | 99.16 169 |
|
view800 | | | 96.46 119 | 95.59 129 | 99.06 63 | 98.87 113 | 98.60 42 | 99.69 134 | 99.71 5 | 92.20 162 | 95.23 157 | 98.80 145 | 89.17 149 | 99.43 131 | 92.29 182 | 94.37 182 | 99.16 169 |
|
conf0.05thres1000 | | | 96.46 119 | 95.59 129 | 99.06 63 | 98.87 113 | 98.60 42 | 99.69 134 | 99.71 5 | 92.20 162 | 95.23 157 | 98.80 145 | 89.17 149 | 99.43 131 | 92.29 182 | 94.37 182 | 99.16 169 |
|
tfpn | | | 96.46 119 | 95.59 129 | 99.06 63 | 98.87 113 | 98.60 42 | 99.69 134 | 99.71 5 | 92.20 162 | 95.23 157 | 98.80 145 | 89.17 149 | 99.43 131 | 92.29 182 | 94.37 182 | 99.16 169 |
|
Anonymous20240529 | | | 92.10 210 | 90.65 216 | 96.47 175 | 98.82 117 | 90.61 264 | 98.72 249 | 98.67 68 | 75.54 335 | 93.90 181 | 98.58 162 | 66.23 324 | 99.90 65 | 94.70 141 | 90.67 211 | 98.90 186 |
|
PVSNet_Blended_VisFu | | | 97.27 80 | 96.81 82 | 98.66 90 | 98.81 118 | 96.67 112 | 99.92 52 | 98.64 72 | 94.51 80 | 96.38 135 | 98.49 166 | 89.05 154 | 99.88 75 | 97.10 103 | 98.34 110 | 99.43 136 |
|
PS-MVSNAJ | | | 98.44 39 | 98.20 40 | 99.16 47 | 98.80 119 | 98.92 15 | 99.54 165 | 98.17 157 | 97.34 12 | 99.85 5 | 99.85 21 | 91.20 125 | 99.89 69 | 99.41 27 | 99.67 74 | 98.69 191 |
|
CANet_DTU | | | 96.76 99 | 96.15 100 | 98.60 95 | 98.78 120 | 97.53 78 | 99.84 91 | 97.63 203 | 97.25 14 | 99.20 52 | 99.64 80 | 81.36 230 | 99.98 31 | 92.77 180 | 98.89 100 | 98.28 194 |
|
alignmvs | | | 97.81 63 | 97.33 68 | 99.25 40 | 98.77 121 | 98.66 37 | 99.99 3 | 98.44 109 | 94.40 84 | 98.41 86 | 99.47 91 | 93.65 87 | 99.42 135 | 98.57 63 | 94.26 188 | 99.67 98 |
|
SteuartSystems-ACMMP | | | 99.02 9 | 98.97 9 | 99.18 44 | 98.72 122 | 97.71 73 | 99.98 6 | 98.44 109 | 96.85 21 | 99.80 8 | 99.91 8 | 97.57 4 | 99.85 80 | 99.44 25 | 99.99 14 | 99.99 12 |
Skip Steuart: Steuart Systems R&D Blog. |
xiu_mvs_v2_base | | | 98.23 50 | 97.97 50 | 99.02 71 | 98.69 123 | 98.66 37 | 99.52 167 | 98.08 168 | 97.05 17 | 99.86 4 | 99.86 17 | 90.65 133 | 99.71 107 | 99.39 28 | 98.63 106 | 98.69 191 |
|
conf0.01 | | | 96.52 116 | 95.88 116 | 98.41 117 | 98.59 124 | 97.38 89 | 99.87 69 | 98.91 43 | 91.32 192 | 95.22 161 | 98.83 139 | 96.57 14 | 98.66 163 | 89.55 220 | 94.09 191 | 99.20 163 |
|
conf0.002 | | | 96.52 116 | 95.88 116 | 98.41 117 | 98.59 124 | 97.38 89 | 99.87 69 | 98.91 43 | 91.32 192 | 95.22 161 | 98.83 139 | 96.57 14 | 98.66 163 | 89.55 220 | 94.09 191 | 99.20 163 |
|
thresconf0.02 | | | 96.53 111 | 95.88 116 | 98.48 105 | 98.59 124 | 97.38 89 | 99.87 69 | 98.91 43 | 91.32 192 | 95.22 161 | 98.83 139 | 96.57 14 | 98.66 163 | 89.55 220 | 94.09 191 | 99.40 141 |
|
tfpn_n400 | | | 96.53 111 | 95.88 116 | 98.48 105 | 98.59 124 | 97.38 89 | 99.87 69 | 98.91 43 | 91.32 192 | 95.22 161 | 98.83 139 | 96.57 14 | 98.66 163 | 89.55 220 | 94.09 191 | 99.40 141 |
|
tfpnconf | | | 96.53 111 | 95.88 116 | 98.48 105 | 98.59 124 | 97.38 89 | 99.87 69 | 98.91 43 | 91.32 192 | 95.22 161 | 98.83 139 | 96.57 14 | 98.66 163 | 89.55 220 | 94.09 191 | 99.40 141 |
|
tfpnview11 | | | 96.53 111 | 95.88 116 | 98.48 105 | 98.59 124 | 97.38 89 | 99.87 69 | 98.91 43 | 91.32 192 | 95.22 161 | 98.83 139 | 96.57 14 | 98.66 163 | 89.55 220 | 94.09 191 | 99.40 141 |
|
MVSTER | | | 95.53 145 | 95.22 143 | 96.45 177 | 98.56 130 | 97.72 72 | 99.91 56 | 97.67 201 | 92.38 159 | 91.39 199 | 97.14 196 | 97.24 10 | 97.30 239 | 94.80 137 | 87.85 237 | 94.34 234 |
|
VDD-MVS | | | 93.77 181 | 92.94 184 | 96.27 183 | 98.55 131 | 90.22 272 | 98.77 246 | 97.79 194 | 90.85 206 | 96.82 123 | 99.42 93 | 61.18 338 | 99.77 94 | 98.95 41 | 94.13 190 | 98.82 187 |
|
tpmvs | | | 94.28 173 | 93.57 172 | 96.40 179 | 98.55 131 | 91.50 254 | 95.70 328 | 98.55 90 | 87.47 258 | 92.15 195 | 94.26 295 | 91.42 121 | 98.95 146 | 88.15 236 | 95.85 161 | 98.76 190 |
|
UGNet | | | 95.33 149 | 94.57 155 | 97.62 145 | 98.55 131 | 94.85 166 | 98.67 255 | 99.32 28 | 95.75 55 | 96.80 124 | 96.27 227 | 72.18 301 | 99.96 42 | 94.58 144 | 99.05 99 | 98.04 198 |
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 |
PCF-MVS | | 94.20 5 | 95.18 150 | 94.10 162 | 98.43 113 | 98.55 131 | 95.99 138 | 97.91 297 | 97.31 240 | 90.35 212 | 89.48 234 | 99.22 107 | 85.19 191 | 99.89 69 | 90.40 212 | 98.47 108 | 99.41 138 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
BH-w/o | | | 95.71 142 | 95.38 139 | 96.68 171 | 98.49 135 | 92.28 230 | 99.84 91 | 97.50 220 | 92.12 167 | 92.06 196 | 98.79 150 | 84.69 192 | 98.67 161 | 95.29 129 | 99.66 75 | 99.09 180 |
|
EPMVS | | | 96.53 111 | 96.01 104 | 98.09 131 | 98.43 136 | 96.12 136 | 96.36 318 | 99.43 24 | 93.53 116 | 97.64 108 | 95.04 268 | 94.41 59 | 98.38 190 | 91.13 197 | 98.11 116 | 99.75 87 |
|
sss | | | 97.57 71 | 97.03 78 | 99.18 44 | 98.37 137 | 98.04 64 | 99.73 126 | 99.38 26 | 93.46 117 | 98.76 71 | 99.06 114 | 91.21 124 | 99.89 69 | 96.33 114 | 97.01 143 | 99.62 106 |
|
BH-untuned | | | 95.18 150 | 94.83 150 | 96.22 184 | 98.36 138 | 91.22 256 | 99.80 101 | 97.32 239 | 90.91 204 | 91.08 201 | 98.67 154 | 83.51 200 | 98.54 172 | 94.23 152 | 99.61 80 | 98.92 184 |
|
FMVSNet3 | | | 92.69 200 | 91.58 204 | 95.99 188 | 98.29 139 | 97.42 87 | 99.26 199 | 97.62 205 | 89.80 221 | 89.68 226 | 95.32 254 | 81.62 225 | 96.27 290 | 87.01 255 | 85.65 250 | 94.29 237 |
|
PMMVS | | | 96.76 99 | 96.76 86 | 96.76 168 | 98.28 140 | 92.10 234 | 99.91 56 | 97.98 175 | 94.12 92 | 99.53 30 | 99.39 97 | 86.93 175 | 98.73 157 | 96.95 108 | 97.73 122 | 99.45 133 |
|
PVSNet_0 | | 88.03 19 | 91.80 216 | 90.27 228 | 96.38 181 | 98.27 141 | 90.46 268 | 99.94 45 | 99.61 17 | 93.99 100 | 86.26 280 | 97.39 191 | 71.13 307 | 99.89 69 | 98.77 53 | 67.05 335 | 98.79 189 |
|
PatchFormer-LS_test | | | 97.01 88 | 96.79 84 | 97.69 142 | 98.26 142 | 94.80 168 | 98.66 258 | 98.13 165 | 93.70 112 | 97.86 106 | 98.80 145 | 95.54 34 | 98.67 161 | 94.12 154 | 96.00 156 | 99.60 110 |
|
DWT-MVSNet_test | | | 97.31 78 | 97.19 71 | 97.66 143 | 98.24 143 | 94.67 172 | 98.86 242 | 98.20 155 | 93.60 115 | 98.09 99 | 98.89 127 | 97.51 5 | 98.78 152 | 94.04 155 | 97.28 133 | 99.55 121 |
|
UA-Net | | | 96.54 110 | 95.96 110 | 98.27 122 | 98.23 144 | 95.71 147 | 98.00 295 | 98.45 108 | 93.72 111 | 98.41 86 | 99.27 102 | 88.71 159 | 99.66 115 | 91.19 196 | 97.69 123 | 99.44 135 |
|
GG-mvs-BLEND | | | | | 98.54 101 | 98.21 145 | 98.01 65 | 93.87 334 | 98.52 93 | | 97.92 103 | 97.92 182 | 99.02 2 | 97.94 215 | 98.17 72 | 99.58 82 | 99.67 98 |
|
mvs_anonymous | | | 95.65 144 | 95.03 148 | 97.53 146 | 98.19 146 | 95.74 144 | 99.33 189 | 97.49 221 | 90.87 205 | 90.47 207 | 97.10 199 | 88.23 162 | 97.16 249 | 95.92 121 | 97.66 125 | 99.68 96 |
|
MVS_Test | | | 96.46 119 | 95.74 126 | 98.61 94 | 98.18 147 | 97.23 97 | 99.31 192 | 97.15 251 | 91.07 201 | 98.84 67 | 97.05 203 | 88.17 163 | 98.97 145 | 94.39 146 | 97.50 128 | 99.61 108 |
|
BH-RMVSNet | | | 95.18 150 | 94.31 159 | 97.80 138 | 98.17 148 | 95.23 161 | 99.76 116 | 97.53 215 | 92.52 155 | 94.27 177 | 99.25 105 | 76.84 274 | 98.80 150 | 90.89 204 | 99.54 84 | 99.35 150 |
|
RPSCF | | | 91.80 216 | 92.79 187 | 88.83 318 | 98.15 149 | 69.87 341 | 98.11 291 | 96.60 297 | 83.93 299 | 94.33 176 | 99.27 102 | 79.60 252 | 99.46 130 | 91.99 188 | 93.16 207 | 97.18 205 |
|
IS-MVSNet | | | 96.29 129 | 95.90 115 | 97.45 150 | 98.13 150 | 94.80 168 | 99.08 212 | 97.61 208 | 92.02 173 | 95.54 152 | 98.96 123 | 90.64 134 | 98.08 206 | 93.73 165 | 97.41 131 | 99.47 132 |
|
ab-mvs | | | 94.69 162 | 93.42 177 | 98.51 103 | 98.07 151 | 96.26 125 | 96.49 316 | 98.68 65 | 90.31 213 | 94.54 171 | 97.00 205 | 76.30 279 | 99.71 107 | 95.98 120 | 93.38 204 | 99.56 120 |
|
casdiffmvs1 | | | 96.98 89 | 96.57 91 | 98.22 124 | 98.06 152 | 96.28 123 | 99.58 157 | 97.41 230 | 92.98 127 | 99.06 58 | 97.44 188 | 90.11 139 | 98.77 153 | 96.88 110 | 97.64 126 | 99.59 112 |
|
XVG-OURS-SEG-HR | | | 94.79 158 | 94.70 153 | 95.08 205 | 98.05 153 | 89.19 284 | 99.08 212 | 97.54 213 | 93.66 113 | 94.87 169 | 99.58 83 | 78.78 262 | 99.79 92 | 97.31 97 | 93.40 203 | 96.25 210 |
|
XVG-OURS | | | 94.82 157 | 94.74 152 | 95.06 206 | 98.00 154 | 89.19 284 | 99.08 212 | 97.55 211 | 94.10 93 | 94.71 170 | 99.62 81 | 80.51 244 | 99.74 103 | 96.04 119 | 93.06 208 | 96.25 210 |
|
dp | | | 95.05 154 | 94.43 157 | 96.91 163 | 97.99 155 | 92.73 220 | 96.29 320 | 97.98 175 | 89.70 222 | 95.93 141 | 94.67 286 | 93.83 84 | 98.45 179 | 86.91 258 | 96.53 150 | 99.54 125 |
|
tpmrst | | | 96.27 131 | 95.98 107 | 97.13 159 | 97.96 156 | 93.15 210 | 96.34 319 | 98.17 157 | 92.07 170 | 98.71 74 | 95.12 262 | 93.91 80 | 98.73 157 | 94.91 135 | 96.62 148 | 99.50 130 |
|
TR-MVS | | | 94.54 166 | 93.56 173 | 97.49 148 | 97.96 156 | 94.34 176 | 98.71 250 | 97.51 219 | 90.30 214 | 94.51 173 | 98.69 153 | 75.56 284 | 98.77 153 | 92.82 179 | 95.99 157 | 99.35 150 |
|
casdiffmvs | | | 96.67 107 | 96.14 101 | 98.27 122 | 97.95 158 | 96.49 117 | 99.48 172 | 97.29 241 | 92.09 168 | 98.67 75 | 97.12 197 | 89.10 153 | 98.74 156 | 96.27 116 | 97.25 136 | 99.57 118 |
|
Vis-MVSNet (Re-imp) | | | 96.32 126 | 95.98 107 | 97.35 156 | 97.93 159 | 94.82 167 | 99.47 174 | 98.15 162 | 91.83 177 | 95.09 167 | 99.11 111 | 91.37 123 | 97.47 226 | 93.47 168 | 97.43 129 | 99.74 88 |
|
diffmvs | | | 96.06 133 | 95.46 135 | 97.89 137 | 97.92 160 | 95.28 158 | 99.16 207 | 97.28 242 | 91.73 180 | 98.16 98 | 96.74 215 | 87.48 168 | 98.81 149 | 93.69 166 | 96.95 145 | 99.58 116 |
|
MDTV_nov1_ep13 | | | | 95.69 127 | | 97.90 161 | 94.15 179 | 95.98 324 | 98.44 109 | 93.12 124 | 97.98 102 | 95.74 237 | 95.10 43 | 98.58 169 | 90.02 216 | 96.92 146 | |
|
Fast-Effi-MVS+ | | | 95.02 155 | 94.19 160 | 97.52 147 | 97.88 162 | 94.55 173 | 99.97 12 | 97.08 254 | 88.85 236 | 94.47 174 | 97.96 181 | 84.59 193 | 98.41 182 | 89.84 217 | 97.10 141 | 99.59 112 |
|
ADS-MVSNet2 | | | 93.80 180 | 93.88 166 | 93.55 263 | 97.87 163 | 85.94 307 | 94.24 330 | 96.84 287 | 90.07 216 | 96.43 132 | 94.48 291 | 90.29 137 | 95.37 308 | 87.44 244 | 97.23 137 | 99.36 148 |
|
ADS-MVSNet | | | 94.79 158 | 94.02 163 | 97.11 161 | 97.87 163 | 93.79 187 | 94.24 330 | 98.16 160 | 90.07 216 | 96.43 132 | 94.48 291 | 90.29 137 | 98.19 203 | 87.44 244 | 97.23 137 | 99.36 148 |
|
Effi-MVS+ | | | 96.30 128 | 95.69 127 | 98.16 125 | 97.85 165 | 96.26 125 | 97.41 302 | 97.21 246 | 90.37 211 | 98.65 78 | 98.58 162 | 86.61 178 | 98.70 160 | 97.11 102 | 97.37 132 | 99.52 127 |
|
tpmp4_e23 | | | 95.15 153 | 94.69 154 | 96.55 174 | 97.84 166 | 91.77 244 | 97.10 308 | 97.91 183 | 88.33 244 | 97.19 116 | 95.06 266 | 93.92 78 | 98.51 173 | 89.64 219 | 95.19 173 | 99.37 147 |
|
PatchmatchNet | | | 95.94 137 | 95.45 136 | 97.39 154 | 97.83 167 | 94.41 175 | 96.05 323 | 98.40 127 | 92.86 129 | 97.09 119 | 95.28 259 | 94.21 72 | 98.07 208 | 89.26 228 | 98.11 116 | 99.70 93 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
cascas | | | 94.64 164 | 93.61 168 | 97.74 141 | 97.82 168 | 96.26 125 | 99.96 19 | 97.78 195 | 85.76 281 | 94.00 179 | 97.54 186 | 76.95 273 | 99.21 138 | 97.23 99 | 95.43 169 | 97.76 203 |
|
1112_ss | | | 96.01 136 | 95.20 144 | 98.42 114 | 97.80 169 | 96.41 120 | 99.65 147 | 96.66 294 | 92.71 139 | 92.88 191 | 99.40 95 | 92.16 112 | 99.30 136 | 91.92 189 | 93.66 200 | 99.55 121 |
|
Test_1112_low_res | | | 95.72 140 | 94.83 150 | 98.42 114 | 97.79 170 | 96.41 120 | 99.65 147 | 96.65 295 | 92.70 140 | 92.86 192 | 96.13 231 | 92.15 113 | 99.30 136 | 91.88 190 | 93.64 201 | 99.55 121 |
|
Effi-MVS+-dtu | | | 94.53 168 | 95.30 141 | 92.22 292 | 97.77 171 | 82.54 323 | 99.59 156 | 97.06 255 | 94.92 69 | 95.29 155 | 95.37 252 | 85.81 183 | 97.89 216 | 94.80 137 | 97.07 142 | 96.23 212 |
|
mvs-test1 | | | 95.53 145 | 95.97 109 | 94.20 242 | 97.77 171 | 85.44 312 | 99.95 31 | 97.06 255 | 94.92 69 | 96.58 127 | 98.72 152 | 85.81 183 | 98.98 144 | 94.80 137 | 98.11 116 | 98.18 195 |
|
tpm cat1 | | | 93.51 186 | 92.52 192 | 96.47 175 | 97.77 171 | 91.47 255 | 96.13 321 | 98.06 169 | 80.98 320 | 92.91 190 | 93.78 303 | 89.66 141 | 98.87 147 | 87.03 254 | 96.39 152 | 99.09 180 |
|
xiu_mvs_v1_base_debu | | | 97.43 73 | 97.06 74 | 98.55 98 | 97.74 174 | 98.14 59 | 99.31 192 | 97.86 189 | 96.43 32 | 99.62 24 | 99.69 72 | 85.56 186 | 99.68 111 | 99.05 35 | 98.31 112 | 97.83 200 |
|
xiu_mvs_v1_base | | | 97.43 73 | 97.06 74 | 98.55 98 | 97.74 174 | 98.14 59 | 99.31 192 | 97.86 189 | 96.43 32 | 99.62 24 | 99.69 72 | 85.56 186 | 99.68 111 | 99.05 35 | 98.31 112 | 97.83 200 |
|
xiu_mvs_v1_base_debi | | | 97.43 73 | 97.06 74 | 98.55 98 | 97.74 174 | 98.14 59 | 99.31 192 | 97.86 189 | 96.43 32 | 99.62 24 | 99.69 72 | 85.56 186 | 99.68 111 | 99.05 35 | 98.31 112 | 97.83 200 |
|
EPP-MVSNet | | | 96.69 104 | 96.60 89 | 96.96 162 | 97.74 174 | 93.05 213 | 99.37 185 | 98.56 86 | 88.75 237 | 95.83 147 | 99.01 117 | 96.01 23 | 98.56 170 | 96.92 109 | 97.20 139 | 99.25 160 |
|
gg-mvs-nofinetune | | | 93.51 186 | 91.86 202 | 98.47 109 | 97.72 178 | 97.96 67 | 92.62 339 | 98.51 99 | 74.70 338 | 97.33 113 | 69.59 353 | 98.91 3 | 97.79 218 | 97.77 90 | 99.56 83 | 99.67 98 |
|
IB-MVS | | 92.85 6 | 94.99 156 | 93.94 164 | 98.16 125 | 97.72 178 | 95.69 149 | 99.99 3 | 98.81 57 | 94.28 88 | 92.70 193 | 96.90 207 | 95.08 44 | 99.17 140 | 96.07 118 | 73.88 325 | 99.60 110 |
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 |
Vis-MVSNet | | | 95.72 140 | 95.15 146 | 97.45 150 | 97.62 180 | 94.28 177 | 99.28 197 | 98.24 149 | 94.27 89 | 96.84 122 | 98.94 126 | 79.39 253 | 98.76 155 | 93.25 172 | 98.49 107 | 99.30 155 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
testpf | | | 89.10 273 | 88.73 263 | 90.24 309 | 97.59 181 | 83.48 320 | 74.22 357 | 97.39 233 | 79.66 324 | 89.64 230 | 93.92 299 | 86.38 179 | 95.76 304 | 85.42 270 | 94.31 187 | 91.49 319 |
|
LCM-MVSNet-Re | | | 92.31 207 | 92.60 190 | 91.43 299 | 97.53 182 | 79.27 337 | 99.02 225 | 91.83 351 | 92.07 170 | 80.31 308 | 94.38 294 | 83.50 201 | 95.48 306 | 97.22 100 | 97.58 127 | 99.54 125 |
|
GBi-Net | | | 90.88 238 | 89.82 240 | 94.08 246 | 97.53 182 | 91.97 235 | 98.43 272 | 96.95 276 | 87.05 265 | 89.68 226 | 94.72 282 | 71.34 304 | 96.11 294 | 87.01 255 | 85.65 250 | 94.17 243 |
|
test1 | | | 90.88 238 | 89.82 240 | 94.08 246 | 97.53 182 | 91.97 235 | 98.43 272 | 96.95 276 | 87.05 265 | 89.68 226 | 94.72 282 | 71.34 304 | 96.11 294 | 87.01 255 | 85.65 250 | 94.17 243 |
|
FMVSNet2 | | | 91.02 235 | 89.56 244 | 95.41 199 | 97.53 182 | 95.74 144 | 98.98 227 | 97.41 230 | 87.05 265 | 88.43 251 | 95.00 271 | 71.34 304 | 96.24 292 | 85.12 273 | 85.21 255 | 94.25 240 |
|
CDS-MVSNet | | | 96.34 125 | 96.07 102 | 97.13 159 | 97.37 186 | 94.96 164 | 99.53 166 | 97.91 183 | 91.55 184 | 95.37 154 | 98.32 173 | 95.05 46 | 97.13 255 | 93.80 162 | 95.75 164 | 99.30 155 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
TESTMET0.1,1 | | | 96.74 101 | 96.26 98 | 98.16 125 | 97.36 187 | 96.48 118 | 99.96 19 | 98.29 143 | 91.93 174 | 95.77 148 | 98.07 178 | 95.54 34 | 98.29 196 | 90.55 208 | 98.89 100 | 99.70 93 |
|
MVSFormer | | | 96.94 91 | 96.60 89 | 97.95 134 | 97.28 188 | 97.70 75 | 99.55 163 | 97.27 243 | 91.17 198 | 99.43 39 | 99.54 87 | 90.92 131 | 96.89 271 | 94.67 142 | 99.62 77 | 99.25 160 |
|
lupinMVS | | | 97.85 61 | 97.60 60 | 98.62 93 | 97.28 188 | 97.70 75 | 99.99 3 | 97.55 211 | 95.50 60 | 99.43 39 | 99.67 76 | 90.92 131 | 98.71 159 | 98.40 68 | 99.62 77 | 99.45 133 |
|
Patchmatch-test1 | | | 94.39 171 | 93.46 175 | 97.17 158 | 97.10 190 | 94.44 174 | 98.86 242 | 98.32 140 | 93.30 121 | 96.17 138 | 95.38 250 | 76.48 278 | 97.34 232 | 88.12 238 | 97.43 129 | 99.74 88 |
|
TAMVS | | | 95.85 138 | 95.58 133 | 96.65 173 | 97.07 191 | 93.50 194 | 99.17 205 | 97.82 193 | 91.39 191 | 95.02 168 | 98.01 179 | 92.20 111 | 97.30 239 | 93.75 164 | 95.83 162 | 99.14 175 |
|
Fast-Effi-MVS+-dtu | | | 93.72 183 | 93.86 167 | 93.29 266 | 97.06 192 | 86.16 305 | 99.80 101 | 96.83 288 | 92.66 143 | 92.58 194 | 97.83 183 | 81.39 229 | 97.67 221 | 89.75 218 | 96.87 147 | 96.05 214 |
|
CostFormer | | | 96.10 132 | 95.88 116 | 96.78 167 | 97.03 193 | 92.55 226 | 97.08 309 | 97.83 192 | 90.04 218 | 98.72 73 | 94.89 278 | 95.01 48 | 98.29 196 | 96.54 113 | 95.77 163 | 99.50 130 |
|
test-LLR | | | 96.47 118 | 96.04 103 | 97.78 139 | 97.02 194 | 95.44 152 | 99.96 19 | 98.21 152 | 94.07 95 | 95.55 150 | 96.38 223 | 93.90 81 | 98.27 199 | 90.42 210 | 98.83 102 | 99.64 104 |
|
test-mter | | | 96.39 124 | 95.93 112 | 97.78 139 | 97.02 194 | 95.44 152 | 99.96 19 | 98.21 152 | 91.81 178 | 95.55 150 | 96.38 223 | 95.17 41 | 98.27 199 | 90.42 210 | 98.83 102 | 99.64 104 |
|
gm-plane-assit | | | | | | 96.97 196 | 93.76 191 | | | 91.47 187 | | 98.96 123 | | 98.79 151 | 94.92 133 | | |
|
QAPM | | | 95.40 148 | 94.17 161 | 99.10 59 | 96.92 197 | 97.71 73 | 99.40 180 | 98.68 65 | 89.31 224 | 88.94 245 | 98.89 127 | 82.48 207 | 99.96 42 | 93.12 178 | 99.83 61 | 99.62 106 |
|
tpm2 | | | 95.47 147 | 95.18 145 | 96.35 182 | 96.91 198 | 91.70 249 | 96.96 312 | 97.93 180 | 88.04 248 | 98.44 85 | 95.40 247 | 93.32 92 | 97.97 211 | 94.00 156 | 95.61 166 | 99.38 145 |
|
FMVSNet5 | | | 88.32 279 | 87.47 279 | 90.88 302 | 96.90 199 | 88.39 295 | 97.28 306 | 95.68 313 | 82.60 306 | 84.67 291 | 92.40 315 | 79.83 251 | 91.16 338 | 76.39 324 | 81.51 272 | 93.09 299 |
|
3Dnovator+ | | 91.53 11 | 96.31 127 | 95.24 142 | 99.52 20 | 96.88 200 | 98.64 40 | 99.72 131 | 98.24 149 | 95.27 65 | 88.42 253 | 98.98 121 | 82.76 206 | 99.94 58 | 97.10 103 | 99.83 61 | 99.96 57 |
|
Patchmatch-test | | | 92.65 202 | 91.50 206 | 96.10 187 | 96.85 201 | 90.49 267 | 91.50 344 | 97.19 247 | 82.76 305 | 90.23 208 | 95.59 242 | 95.02 47 | 98.00 210 | 77.41 319 | 96.98 144 | 99.82 78 |
|
MVS | | | 96.60 109 | 95.56 134 | 99.72 4 | 96.85 201 | 99.22 8 | 98.31 280 | 98.94 39 | 91.57 183 | 90.90 203 | 99.61 82 | 86.66 177 | 99.96 42 | 97.36 96 | 99.88 56 | 99.99 12 |
|
3Dnovator | | 91.47 12 | 96.28 130 | 95.34 140 | 99.08 61 | 96.82 203 | 97.47 84 | 99.45 177 | 98.81 57 | 95.52 59 | 89.39 235 | 99.00 119 | 81.97 216 | 99.95 50 | 97.27 98 | 99.83 61 | 99.84 76 |
|
EI-MVSNet | | | 93.73 182 | 93.40 180 | 94.74 223 | 96.80 204 | 92.69 221 | 99.06 219 | 97.67 201 | 88.96 232 | 91.39 199 | 99.02 115 | 88.75 158 | 97.30 239 | 91.07 198 | 87.85 237 | 94.22 241 |
|
CVMVSNet | | | 94.68 163 | 94.94 149 | 93.89 256 | 96.80 204 | 86.92 304 | 99.06 219 | 98.98 37 | 94.45 81 | 94.23 178 | 99.02 115 | 85.60 185 | 95.31 309 | 90.91 203 | 95.39 170 | 99.43 136 |
|
IterMVS-LS | | | 92.69 200 | 92.11 197 | 94.43 237 | 96.80 204 | 92.74 219 | 99.45 177 | 96.89 283 | 88.98 230 | 89.65 229 | 95.38 250 | 88.77 157 | 96.34 288 | 90.98 201 | 82.04 269 | 94.22 241 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
IterMVS | | | 90.91 237 | 90.17 232 | 93.12 269 | 96.78 207 | 90.42 270 | 98.89 235 | 97.05 259 | 89.03 228 | 86.49 275 | 95.42 246 | 76.59 276 | 95.02 312 | 87.22 251 | 84.09 260 | 93.93 269 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
1314 | | | 96.84 95 | 95.96 110 | 99.48 25 | 96.74 208 | 98.52 48 | 98.31 280 | 98.86 54 | 95.82 48 | 89.91 217 | 98.98 121 | 87.49 167 | 99.96 42 | 97.80 87 | 99.73 70 | 99.96 57 |
|
semantic-postprocess | | | | | 92.93 274 | 96.72 209 | 89.96 277 | | 96.99 269 | 88.95 233 | 86.63 272 | 95.67 239 | 76.50 277 | 95.00 313 | 87.04 253 | 84.04 263 | 93.84 277 |
|
MVS-HIRNet | | | 86.22 292 | 83.19 307 | 95.31 200 | 96.71 210 | 90.29 271 | 92.12 341 | 97.33 238 | 62.85 349 | 86.82 269 | 70.37 352 | 69.37 313 | 97.49 225 | 75.12 326 | 97.99 121 | 98.15 196 |
|
VDDNet | | | 93.12 191 | 91.91 201 | 96.76 168 | 96.67 211 | 92.65 224 | 98.69 252 | 98.21 152 | 82.81 304 | 97.75 107 | 99.28 101 | 61.57 336 | 99.48 129 | 98.09 77 | 94.09 191 | 98.15 196 |
|
MIMVSNet | | | 90.30 253 | 88.67 264 | 95.17 204 | 96.45 212 | 91.64 251 | 92.39 340 | 97.15 251 | 85.99 278 | 90.50 206 | 93.19 311 | 66.95 322 | 94.86 316 | 82.01 294 | 93.43 202 | 99.01 183 |
|
CR-MVSNet | | | 93.45 189 | 92.62 189 | 95.94 189 | 96.29 213 | 92.66 222 | 92.01 342 | 96.23 303 | 92.62 146 | 96.94 120 | 93.31 309 | 91.04 129 | 96.03 299 | 79.23 309 | 95.96 158 | 99.13 177 |
|
RPMNet | | | 89.39 269 | 87.20 281 | 95.94 189 | 96.29 213 | 92.66 222 | 92.01 342 | 97.63 203 | 70.19 346 | 96.94 120 | 85.87 345 | 87.25 171 | 96.03 299 | 62.69 342 | 95.96 158 | 99.13 177 |
|
Patchmtry | | | 89.70 263 | 88.49 265 | 93.33 265 | 96.24 215 | 89.94 280 | 91.37 345 | 96.23 303 | 78.22 327 | 87.69 259 | 93.31 309 | 91.04 129 | 96.03 299 | 80.18 303 | 82.10 268 | 94.02 253 |
|
JIA-IIPM | | | 91.76 219 | 90.70 215 | 94.94 213 | 96.11 216 | 87.51 300 | 93.16 337 | 98.13 165 | 75.79 334 | 97.58 109 | 77.68 349 | 92.84 100 | 97.97 211 | 88.47 234 | 96.54 149 | 99.33 152 |
|
OpenMVS | | 90.15 15 | 94.77 160 | 93.59 171 | 98.33 120 | 96.07 217 | 97.48 83 | 99.56 161 | 98.57 84 | 90.46 210 | 86.51 274 | 98.95 125 | 78.57 264 | 99.94 58 | 93.86 157 | 99.74 69 | 97.57 204 |
|
PAPM | | | 98.60 26 | 98.42 26 | 99.14 53 | 96.05 218 | 98.96 13 | 99.90 59 | 99.35 27 | 96.68 29 | 98.35 90 | 99.66 78 | 96.45 20 | 98.51 173 | 99.45 24 | 99.89 54 | 99.96 57 |
|
CLD-MVS | | | 94.06 175 | 93.90 165 | 94.55 232 | 96.02 219 | 90.69 262 | 99.98 6 | 97.72 198 | 96.62 31 | 91.05 202 | 98.85 138 | 77.21 270 | 98.47 175 | 98.11 75 | 89.51 217 | 94.48 220 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
PatchT | | | 90.38 250 | 88.75 262 | 95.25 201 | 95.99 220 | 90.16 273 | 91.22 346 | 97.54 213 | 76.80 330 | 97.26 114 | 86.01 344 | 91.88 117 | 96.07 298 | 66.16 339 | 95.91 160 | 99.51 128 |
|
ACMH+ | | 89.98 16 | 90.35 251 | 89.54 245 | 92.78 277 | 95.99 220 | 86.12 306 | 98.81 244 | 97.18 248 | 89.38 223 | 83.14 299 | 97.76 184 | 68.42 318 | 98.43 180 | 89.11 229 | 86.05 249 | 93.78 280 |
|
DeepMVS_CX | | | | | 82.92 329 | 95.98 222 | 58.66 353 | | 96.01 308 | 92.72 138 | 78.34 315 | 95.51 244 | 58.29 342 | 98.08 206 | 82.57 290 | 85.29 253 | 92.03 313 |
|
ACMP | | 92.05 9 | 92.74 198 | 92.42 194 | 93.73 257 | 95.91 223 | 88.72 288 | 99.81 98 | 97.53 215 | 94.13 91 | 87.00 266 | 98.23 174 | 74.07 295 | 98.47 175 | 96.22 117 | 88.86 224 | 93.99 261 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
HQP-NCC | | | | | | 95.78 224 | | 99.87 69 | | 96.82 22 | 93.37 183 | | | | | | |
|
ACMP_Plane | | | | | | 95.78 224 | | 99.87 69 | | 96.82 22 | 93.37 183 | | | | | | |
|
HQP-MVS | | | 94.61 165 | 94.50 156 | 94.92 215 | 95.78 224 | 91.85 240 | 99.87 69 | 97.89 185 | 96.82 22 | 93.37 183 | 98.65 156 | 80.65 242 | 98.39 186 | 97.92 85 | 89.60 212 | 94.53 216 |
|
NP-MVS | | | | | | 95.77 227 | 91.79 242 | | | | | 98.65 156 | | | | | |
|
plane_prior6 | | | | | | 95.76 228 | 91.72 248 | | | | | | 80.47 246 | | | | |
|
ACMM | | 91.95 10 | 92.88 196 | 92.52 192 | 93.98 253 | 95.75 229 | 89.08 286 | 99.77 110 | 97.52 217 | 93.00 126 | 89.95 216 | 97.99 180 | 76.17 281 | 98.46 178 | 93.63 167 | 88.87 223 | 94.39 228 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
GA-MVS | | | 93.83 177 | 92.84 185 | 96.80 166 | 95.73 230 | 93.57 193 | 99.88 66 | 97.24 245 | 92.57 153 | 92.92 189 | 96.66 216 | 78.73 263 | 97.67 221 | 87.75 241 | 94.06 198 | 99.17 168 |
|
plane_prior1 | | | | | | 95.73 230 | | | | | | | | | | | |
|
jason | | | 97.24 81 | 96.86 80 | 98.38 119 | 95.73 230 | 97.32 95 | 99.97 12 | 97.40 232 | 95.34 63 | 98.60 81 | 99.54 87 | 87.70 165 | 98.56 170 | 97.94 84 | 99.47 88 | 99.25 160 |
jason: jason. |
HQP_MVS | | | 94.49 169 | 94.36 158 | 94.87 218 | 95.71 233 | 91.74 245 | 99.84 91 | 97.87 187 | 96.38 35 | 93.01 187 | 98.59 160 | 80.47 246 | 98.37 191 | 97.79 88 | 89.55 215 | 94.52 218 |
|
plane_prior7 | | | | | | 95.71 233 | 91.59 253 | | | | | | | | | | |
|
ITE_SJBPF | | | | | 92.38 288 | 95.69 235 | 85.14 313 | | 95.71 312 | 92.81 133 | 89.33 238 | 98.11 176 | 70.23 311 | 98.42 181 | 85.91 266 | 88.16 235 | 93.59 288 |
|
ACMH | | 89.72 17 | 90.64 244 | 89.63 242 | 93.66 261 | 95.64 236 | 88.64 291 | 98.55 262 | 97.45 223 | 89.03 228 | 81.62 304 | 97.61 185 | 69.75 312 | 98.41 182 | 89.37 226 | 87.62 241 | 93.92 270 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
FMVSNet1 | | | 88.50 278 | 86.64 282 | 94.08 246 | 95.62 237 | 91.97 235 | 98.43 272 | 96.95 276 | 83.00 303 | 86.08 282 | 94.72 282 | 59.09 341 | 96.11 294 | 81.82 296 | 84.07 261 | 94.17 243 |
|
LPG-MVS_test | | | 92.96 194 | 92.71 188 | 93.71 259 | 95.43 238 | 88.67 289 | 99.75 117 | 97.62 205 | 92.81 133 | 90.05 211 | 98.49 166 | 75.24 287 | 98.40 184 | 95.84 124 | 89.12 219 | 94.07 250 |
|
LGP-MVS_train | | | | | 93.71 259 | 95.43 238 | 88.67 289 | | 97.62 205 | 92.81 133 | 90.05 211 | 98.49 166 | 75.24 287 | 98.40 184 | 95.84 124 | 89.12 219 | 94.07 250 |
|
tpm | | | 93.70 184 | 93.41 179 | 94.58 230 | 95.36 240 | 87.41 302 | 97.01 310 | 96.90 282 | 90.85 206 | 96.72 126 | 94.14 298 | 90.40 135 | 96.84 274 | 90.75 206 | 88.54 230 | 99.51 128 |
|
VPA-MVSNet | | | 92.70 199 | 91.55 205 | 96.16 185 | 95.09 241 | 96.20 130 | 98.88 236 | 99.00 36 | 91.02 203 | 91.82 197 | 95.29 258 | 76.05 283 | 97.96 213 | 95.62 127 | 81.19 274 | 94.30 236 |
|
LP | | | 86.76 285 | 84.85 289 | 92.50 283 | 95.08 242 | 85.89 308 | 89.97 347 | 96.97 274 | 75.28 337 | 84.97 290 | 90.68 321 | 80.78 239 | 95.13 311 | 61.64 343 | 88.31 233 | 96.46 209 |
|
LTVRE_ROB | | 88.28 18 | 90.29 254 | 89.05 257 | 94.02 249 | 95.08 242 | 90.15 274 | 97.19 307 | 97.43 225 | 84.91 291 | 83.99 295 | 97.06 202 | 74.00 296 | 98.28 198 | 84.08 279 | 87.71 239 | 93.62 287 |
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 |
TinyColmap | | | 87.87 282 | 86.51 283 | 91.94 295 | 95.05 244 | 85.57 310 | 97.65 299 | 94.08 340 | 84.40 297 | 81.82 303 | 96.85 211 | 62.14 335 | 98.33 193 | 80.25 302 | 86.37 248 | 91.91 315 |
|
test0.0.03 1 | | | 93.86 176 | 93.61 168 | 94.64 227 | 95.02 245 | 92.18 233 | 99.93 50 | 98.58 82 | 94.07 95 | 87.96 257 | 98.50 165 | 93.90 81 | 94.96 314 | 81.33 297 | 93.17 206 | 96.78 206 |
|
UniMVSNet (Re) | | | 93.07 193 | 92.13 196 | 95.88 191 | 94.84 246 | 96.24 129 | 99.88 66 | 98.98 37 | 92.49 157 | 89.25 239 | 95.40 247 | 87.09 173 | 97.14 253 | 93.13 177 | 78.16 304 | 94.26 238 |
|
USDC | | | 90.00 260 | 88.96 258 | 93.10 271 | 94.81 247 | 88.16 297 | 98.71 250 | 95.54 318 | 93.66 113 | 83.75 297 | 97.20 195 | 65.58 326 | 98.31 195 | 83.96 282 | 87.49 243 | 92.85 306 |
|
VPNet | | | 91.81 214 | 90.46 220 | 95.85 193 | 94.74 248 | 95.54 151 | 98.98 227 | 98.59 81 | 92.14 166 | 90.77 205 | 97.44 188 | 68.73 316 | 97.54 224 | 94.89 136 | 77.89 306 | 94.46 221 |
|
FIs | | | 94.10 174 | 93.43 176 | 96.11 186 | 94.70 249 | 96.82 110 | 99.58 157 | 98.93 42 | 92.54 154 | 89.34 237 | 97.31 192 | 87.62 166 | 97.10 258 | 94.22 153 | 86.58 246 | 94.40 227 |
|
UniMVSNet_NR-MVSNet | | | 92.95 195 | 92.11 197 | 95.49 196 | 94.61 250 | 95.28 158 | 99.83 95 | 99.08 33 | 91.49 185 | 89.21 241 | 96.86 210 | 87.14 172 | 96.73 278 | 93.20 173 | 77.52 310 | 94.46 221 |
|
WR-MVS | | | 92.31 207 | 91.25 209 | 95.48 198 | 94.45 251 | 95.29 157 | 99.60 155 | 98.68 65 | 90.10 215 | 88.07 256 | 96.89 208 | 80.68 241 | 96.80 277 | 93.14 176 | 79.67 295 | 94.36 230 |
|
nrg030 | | | 93.51 186 | 92.53 191 | 96.45 177 | 94.36 252 | 97.20 98 | 99.81 98 | 97.16 250 | 91.60 182 | 89.86 220 | 97.46 187 | 86.37 180 | 97.68 220 | 95.88 122 | 80.31 285 | 94.46 221 |
|
tfpnnormal | | | 89.29 271 | 87.61 277 | 94.34 239 | 94.35 253 | 94.13 180 | 98.95 231 | 98.94 39 | 83.94 298 | 84.47 292 | 95.51 244 | 74.84 290 | 97.39 228 | 77.05 322 | 80.41 283 | 91.48 320 |
|
FC-MVSNet-test | | | 93.81 179 | 93.15 183 | 95.80 194 | 94.30 254 | 96.20 130 | 99.42 179 | 98.89 52 | 92.33 160 | 89.03 244 | 97.27 194 | 87.39 170 | 96.83 275 | 93.20 173 | 86.48 247 | 94.36 230 |
|
MS-PatchMatch | | | 90.65 243 | 90.30 225 | 91.71 298 | 94.22 255 | 85.50 311 | 98.24 285 | 97.70 199 | 88.67 238 | 86.42 277 | 96.37 225 | 67.82 320 | 98.03 209 | 83.62 284 | 99.62 77 | 91.60 318 |
|
WR-MVS_H | | | 91.30 228 | 90.35 223 | 94.15 243 | 94.17 256 | 92.62 225 | 99.17 205 | 98.94 39 | 88.87 235 | 86.48 276 | 94.46 293 | 84.36 195 | 96.61 281 | 88.19 235 | 78.51 300 | 93.21 298 |
|
DU-MVS | | | 92.46 205 | 91.45 208 | 95.49 196 | 94.05 257 | 95.28 158 | 99.81 98 | 98.74 61 | 92.25 161 | 89.21 241 | 96.64 218 | 81.66 223 | 96.73 278 | 93.20 173 | 77.52 310 | 94.46 221 |
|
NR-MVSNet | | | 91.56 221 | 90.22 230 | 95.60 195 | 94.05 257 | 95.76 143 | 98.25 284 | 98.70 63 | 91.16 200 | 80.78 307 | 96.64 218 | 83.23 204 | 96.57 282 | 91.41 194 | 77.73 308 | 94.46 221 |
|
CP-MVSNet | | | 91.23 231 | 90.22 230 | 94.26 240 | 93.96 259 | 92.39 229 | 99.09 210 | 98.57 84 | 88.95 233 | 86.42 277 | 96.57 220 | 79.19 257 | 96.37 286 | 90.29 213 | 78.95 297 | 94.02 253 |
|
XXY-MVS | | | 91.82 213 | 90.46 220 | 95.88 191 | 93.91 260 | 95.40 155 | 98.87 240 | 97.69 200 | 88.63 240 | 87.87 258 | 97.08 200 | 74.38 294 | 97.89 216 | 91.66 193 | 84.07 261 | 94.35 233 |
|
PS-CasMVS | | | 90.63 245 | 89.51 247 | 93.99 252 | 93.83 261 | 91.70 249 | 98.98 227 | 98.52 93 | 88.48 241 | 86.15 281 | 96.53 222 | 75.46 285 | 96.31 289 | 88.83 231 | 78.86 299 | 93.95 267 |
|
test_0402 | | | 85.58 299 | 83.94 302 | 90.50 306 | 93.81 262 | 85.04 314 | 98.55 262 | 95.20 332 | 76.01 332 | 79.72 311 | 95.13 261 | 64.15 331 | 96.26 291 | 66.04 340 | 86.88 245 | 90.21 331 |
|
XVG-ACMP-BASELINE | | | 91.22 232 | 90.75 214 | 92.63 279 | 93.73 263 | 85.61 309 | 98.52 267 | 97.44 224 | 92.77 137 | 89.90 218 | 96.85 211 | 66.64 323 | 98.39 186 | 92.29 182 | 88.61 228 | 93.89 273 |
|
TranMVSNet+NR-MVSNet | | | 91.68 220 | 90.61 217 | 94.87 218 | 93.69 264 | 93.98 183 | 99.69 134 | 98.65 69 | 91.03 202 | 88.44 250 | 96.83 214 | 80.05 250 | 96.18 293 | 90.26 214 | 76.89 317 | 94.45 226 |
|
v7 | | | 91.20 233 | 89.99 238 | 94.82 221 | 93.57 265 | 93.41 201 | 99.57 159 | 96.98 271 | 86.83 269 | 89.88 219 | 95.22 260 | 81.01 235 | 97.14 253 | 85.53 269 | 81.31 273 | 93.90 271 |
|
TransMVSNet (Re) | | | 87.25 283 | 85.28 287 | 93.16 268 | 93.56 266 | 91.03 257 | 98.54 264 | 94.05 341 | 83.69 301 | 81.09 306 | 96.16 229 | 75.32 286 | 96.40 285 | 76.69 323 | 68.41 332 | 92.06 312 |
|
Anonymous20240521 | | | 90.82 240 | 89.48 249 | 94.82 221 | 93.53 267 | 94.07 181 | 99.07 216 | 97.43 225 | 87.10 264 | 84.14 293 | 94.60 288 | 83.11 205 | 97.32 235 | 85.73 268 | 80.61 282 | 93.08 300 |
|
v10 | | | 90.25 255 | 88.82 260 | 94.57 231 | 93.53 267 | 93.43 197 | 99.08 212 | 96.87 286 | 85.00 290 | 87.34 264 | 94.51 289 | 80.93 237 | 97.02 267 | 82.85 289 | 79.23 296 | 93.26 296 |
|
v1neww | | | 91.44 222 | 90.28 226 | 94.91 216 | 93.50 269 | 93.43 197 | 99.73 126 | 97.06 255 | 87.55 252 | 90.08 209 | 95.11 263 | 81.98 214 | 97.32 235 | 87.41 246 | 80.15 288 | 93.99 261 |
|
v7new | | | 91.44 222 | 90.28 226 | 94.91 216 | 93.50 269 | 93.43 197 | 99.73 126 | 97.06 255 | 87.55 252 | 90.08 209 | 95.11 263 | 81.98 214 | 97.32 235 | 87.41 246 | 80.15 288 | 93.99 261 |
|
testgi | | | 89.01 274 | 88.04 273 | 91.90 296 | 93.49 271 | 84.89 315 | 99.73 126 | 95.66 314 | 93.89 107 | 85.14 288 | 98.17 175 | 59.68 340 | 94.66 318 | 77.73 317 | 88.88 222 | 96.16 213 |
|
v16 | | | 86.52 287 | 84.49 291 | 92.60 281 | 93.45 272 | 93.31 206 | 98.60 261 | 95.52 320 | 82.30 309 | 74.59 329 | 87.70 331 | 81.95 218 | 94.18 320 | 79.93 306 | 66.38 337 | 90.30 328 |
|
v6 | | | 91.44 222 | 90.27 228 | 94.93 214 | 93.44 273 | 93.44 196 | 99.73 126 | 97.05 259 | 87.57 251 | 90.05 211 | 95.10 265 | 81.87 219 | 97.39 228 | 87.45 243 | 80.17 287 | 93.98 265 |
|
v8 | | | 90.54 247 | 89.17 253 | 94.66 226 | 93.43 274 | 93.40 204 | 99.20 202 | 96.94 279 | 85.76 281 | 87.56 260 | 94.51 289 | 81.96 217 | 97.19 247 | 84.94 275 | 78.25 303 | 93.38 293 |
|
V42 | | | 91.28 230 | 90.12 236 | 94.74 223 | 93.42 275 | 93.46 195 | 99.68 139 | 97.02 265 | 87.36 260 | 89.85 221 | 95.05 267 | 81.31 231 | 97.34 232 | 87.34 249 | 80.07 290 | 93.40 291 |
|
v18 | | | 86.59 286 | 84.57 290 | 92.65 278 | 93.41 276 | 93.43 197 | 98.69 252 | 95.55 317 | 82.44 307 | 74.71 327 | 87.68 332 | 82.11 211 | 94.21 319 | 80.14 304 | 66.37 338 | 90.32 327 |
|
pm-mvs1 | | | 89.36 270 | 87.81 275 | 94.01 250 | 93.40 277 | 91.93 238 | 98.62 259 | 96.48 301 | 86.25 276 | 83.86 296 | 96.14 230 | 73.68 297 | 97.04 262 | 86.16 264 | 75.73 321 | 93.04 302 |
|
v17 | | | 86.51 288 | 84.49 291 | 92.57 282 | 93.38 278 | 93.29 207 | 98.61 260 | 95.54 318 | 82.32 308 | 74.69 328 | 87.63 333 | 82.03 212 | 94.17 321 | 80.02 305 | 66.17 339 | 90.26 329 |
|
divwei89l23v2f112 | | | 91.37 225 | 90.15 233 | 95.00 209 | 93.35 279 | 93.78 190 | 99.78 105 | 97.05 259 | 87.54 254 | 89.73 225 | 94.89 278 | 82.24 209 | 97.21 245 | 86.91 258 | 79.90 294 | 94.00 258 |
|
v1 | | | 91.36 226 | 90.14 234 | 95.04 207 | 93.35 279 | 93.80 186 | 99.77 110 | 97.05 259 | 87.53 255 | 89.77 223 | 94.91 276 | 81.99 213 | 97.33 234 | 86.90 260 | 79.98 293 | 94.00 258 |
|
v1141 | | | 91.36 226 | 90.14 234 | 95.00 209 | 93.33 281 | 93.79 187 | 99.78 105 | 97.05 259 | 87.52 256 | 89.75 224 | 94.89 278 | 82.13 210 | 97.21 245 | 86.84 261 | 80.00 292 | 94.00 258 |
|
V14 | | | 86.22 292 | 84.15 295 | 92.41 287 | 93.30 282 | 93.16 209 | 98.47 269 | 95.47 321 | 82.10 312 | 74.27 331 | 87.41 334 | 81.73 220 | 94.02 324 | 79.26 308 | 65.37 342 | 90.04 336 |
|
v15 | | | 86.26 291 | 84.19 294 | 92.47 284 | 93.29 283 | 93.28 208 | 98.53 266 | 95.47 321 | 82.24 311 | 74.34 330 | 87.34 335 | 81.71 221 | 94.07 322 | 79.39 307 | 65.42 340 | 90.06 335 |
|
v11 | | | 86.09 296 | 83.98 300 | 92.42 286 | 93.29 283 | 93.41 201 | 98.52 267 | 95.30 328 | 81.73 317 | 74.27 331 | 87.20 337 | 81.24 232 | 93.85 331 | 77.68 318 | 66.61 336 | 90.00 337 |
|
V9 | | | 86.16 294 | 84.07 296 | 92.43 285 | 93.27 285 | 93.04 214 | 98.40 276 | 95.45 323 | 81.98 314 | 74.18 333 | 87.31 336 | 81.58 227 | 94.06 323 | 79.12 311 | 65.33 343 | 90.20 332 |
|
v1144 | | | 91.09 234 | 89.83 239 | 94.87 218 | 93.25 286 | 93.69 192 | 99.62 154 | 96.98 271 | 86.83 269 | 89.64 230 | 94.99 272 | 80.94 236 | 97.05 261 | 85.08 274 | 81.16 275 | 93.87 275 |
|
v13 | | | 86.06 297 | 83.97 301 | 92.34 291 | 93.25 286 | 92.85 217 | 98.26 283 | 95.44 325 | 81.70 318 | 74.02 336 | 87.11 340 | 81.58 227 | 94.00 326 | 78.94 313 | 65.41 341 | 90.18 333 |
|
v12 | | | 86.10 295 | 84.01 297 | 92.37 289 | 93.23 288 | 92.96 215 | 98.33 279 | 95.45 323 | 81.87 315 | 74.05 335 | 87.15 338 | 81.60 226 | 93.98 327 | 79.09 312 | 65.28 344 | 90.18 333 |
|
v1192 | | | 90.62 246 | 89.25 252 | 94.72 225 | 93.13 289 | 93.07 211 | 99.50 169 | 97.02 265 | 86.33 275 | 89.56 233 | 95.01 269 | 79.22 256 | 97.09 260 | 82.34 292 | 81.16 275 | 94.01 255 |
|
v2v482 | | | 91.30 228 | 90.07 237 | 95.01 208 | 93.13 289 | 93.79 187 | 99.77 110 | 97.02 265 | 88.05 247 | 89.25 239 | 95.37 252 | 80.73 240 | 97.15 251 | 87.28 250 | 80.04 291 | 94.09 249 |
|
OPM-MVS | | | 93.21 190 | 92.80 186 | 94.44 235 | 93.12 291 | 90.85 261 | 99.77 110 | 97.61 208 | 96.19 42 | 91.56 198 | 98.65 156 | 75.16 289 | 98.47 175 | 93.78 163 | 89.39 218 | 93.99 261 |
|
v144192 | | | 90.79 241 | 89.52 246 | 94.59 229 | 93.11 292 | 92.77 218 | 99.56 161 | 96.99 269 | 86.38 274 | 89.82 222 | 94.95 275 | 80.50 245 | 97.10 258 | 83.98 281 | 80.41 283 | 93.90 271 |
|
PEN-MVS | | | 90.19 257 | 89.06 256 | 93.57 262 | 93.06 293 | 90.90 260 | 99.06 219 | 98.47 105 | 88.11 246 | 85.91 283 | 96.30 226 | 76.67 275 | 95.94 303 | 87.07 252 | 76.91 316 | 93.89 273 |
|
v1240 | | | 90.20 256 | 88.79 261 | 94.44 235 | 93.05 294 | 92.27 231 | 99.38 184 | 96.92 280 | 85.89 279 | 89.36 236 | 94.87 281 | 77.89 269 | 97.03 265 | 80.66 300 | 81.08 277 | 94.01 255 |
|
v148 | | | 90.70 242 | 89.63 242 | 93.92 254 | 92.97 295 | 90.97 258 | 99.75 117 | 96.89 283 | 87.51 257 | 88.27 254 | 95.01 269 | 81.67 222 | 97.04 262 | 87.40 248 | 77.17 314 | 93.75 281 |
|
v1921920 | | | 90.46 248 | 89.12 254 | 94.50 233 | 92.96 296 | 92.46 227 | 99.49 170 | 96.98 271 | 86.10 277 | 89.61 232 | 95.30 255 | 78.55 265 | 97.03 265 | 82.17 293 | 80.89 281 | 94.01 255 |
|
Baseline_NR-MVSNet | | | 90.33 252 | 89.51 247 | 92.81 276 | 92.84 297 | 89.95 278 | 99.77 110 | 93.94 342 | 84.69 294 | 89.04 243 | 95.66 240 | 81.66 223 | 96.52 283 | 90.99 200 | 76.98 315 | 91.97 314 |
|
pmmvs4 | | | 92.10 210 | 91.07 212 | 95.18 203 | 92.82 298 | 94.96 164 | 99.48 172 | 96.83 288 | 87.45 259 | 88.66 248 | 96.56 221 | 83.78 199 | 96.83 275 | 89.29 227 | 84.77 258 | 93.75 281 |
|
LF4IMVS | | | 89.25 272 | 88.85 259 | 90.45 308 | 92.81 299 | 81.19 331 | 98.12 290 | 94.79 335 | 91.44 188 | 86.29 279 | 97.11 198 | 65.30 328 | 98.11 205 | 88.53 233 | 85.25 254 | 92.07 311 |
|
pcd1.5k->3k | | | 37.58 340 | 39.62 340 | 31.46 351 | 92.73 300 | 0.00 369 | 0.00 360 | 97.52 217 | 0.00 364 | 0.00 366 | 0.00 366 | 78.40 268 | 0.00 366 | 0.00 363 | 87.90 236 | 94.37 229 |
|
DTE-MVSNet | | | 89.40 268 | 88.24 271 | 92.88 275 | 92.66 301 | 89.95 278 | 99.10 209 | 98.22 151 | 87.29 261 | 85.12 289 | 96.22 228 | 76.27 280 | 95.30 310 | 83.56 285 | 75.74 320 | 93.41 290 |
|
EU-MVSNet | | | 90.14 259 | 90.34 224 | 89.54 315 | 92.55 302 | 81.06 332 | 98.69 252 | 98.04 171 | 91.41 190 | 86.59 273 | 96.84 213 | 80.83 238 | 93.31 336 | 86.20 263 | 81.91 270 | 94.26 238 |
|
our_test_3 | | | 90.39 249 | 89.48 249 | 93.12 269 | 92.40 303 | 89.57 283 | 99.33 189 | 96.35 302 | 87.84 249 | 85.30 287 | 94.99 272 | 84.14 197 | 96.09 297 | 80.38 301 | 84.56 259 | 93.71 286 |
|
ppachtmachnet_test | | | 89.58 265 | 88.35 269 | 93.25 267 | 92.40 303 | 90.44 269 | 99.33 189 | 96.73 292 | 85.49 287 | 85.90 284 | 95.77 236 | 81.09 234 | 96.00 302 | 76.00 325 | 82.49 267 | 93.30 294 |
|
v52 | | | 89.55 266 | 88.41 267 | 92.98 272 | 92.32 305 | 90.01 276 | 98.88 236 | 96.89 283 | 84.51 295 | 86.89 267 | 94.22 296 | 79.23 255 | 97.16 249 | 84.46 277 | 78.27 302 | 91.76 316 |
|
v7n | | | 89.65 264 | 88.29 270 | 93.72 258 | 92.22 306 | 90.56 266 | 99.07 216 | 97.10 253 | 85.42 289 | 86.73 270 | 94.72 282 | 80.06 249 | 97.13 255 | 81.14 298 | 78.12 305 | 93.49 289 |
|
V4 | | | 89.55 266 | 88.41 267 | 92.98 272 | 92.21 307 | 90.03 275 | 98.87 240 | 96.91 281 | 84.51 295 | 86.84 268 | 94.21 297 | 79.37 254 | 97.15 251 | 84.45 278 | 78.28 301 | 91.76 316 |
|
PS-MVSNAJss | | | 93.64 185 | 93.31 182 | 94.61 228 | 92.11 308 | 92.19 232 | 99.12 208 | 97.38 234 | 92.51 156 | 88.45 249 | 96.99 206 | 91.20 125 | 97.29 242 | 94.36 147 | 87.71 239 | 94.36 230 |
|
pmmvs5 | | | 90.17 258 | 89.09 255 | 93.40 264 | 92.10 309 | 89.77 281 | 99.74 120 | 95.58 316 | 85.88 280 | 87.24 265 | 95.74 237 | 73.41 298 | 96.48 284 | 88.54 232 | 83.56 264 | 93.95 267 |
|
N_pmnet | | | 80.06 317 | 80.78 314 | 77.89 333 | 91.94 310 | 45.28 362 | 98.80 245 | 56.82 367 | 78.10 328 | 80.08 310 | 93.33 307 | 77.03 271 | 95.76 304 | 68.14 335 | 82.81 266 | 92.64 307 |
|
v748 | | | 88.94 275 | 87.72 276 | 92.61 280 | 91.91 311 | 87.50 301 | 99.07 216 | 96.97 274 | 84.76 292 | 85.79 285 | 93.63 306 | 79.19 257 | 97.04 262 | 83.16 287 | 75.03 324 | 93.28 295 |
|
test_djsdf | | | 92.83 197 | 92.29 195 | 94.47 234 | 91.90 312 | 92.46 227 | 99.55 163 | 97.27 243 | 91.17 198 | 89.96 215 | 96.07 233 | 81.10 233 | 96.89 271 | 94.67 142 | 88.91 221 | 94.05 252 |
|
DI_MVS_plusplus_test | | | 92.48 203 | 90.60 218 | 98.11 130 | 91.88 313 | 96.13 133 | 99.64 151 | 97.73 196 | 92.69 141 | 76.02 321 | 93.79 302 | 70.49 309 | 99.07 141 | 95.88 122 | 97.26 135 | 99.14 175 |
|
test_normal | | | 92.44 206 | 90.54 219 | 98.12 129 | 91.85 314 | 96.18 132 | 99.68 139 | 97.73 196 | 92.66 143 | 75.76 325 | 93.74 304 | 70.49 309 | 99.04 143 | 95.71 126 | 97.27 134 | 99.13 177 |
|
SixPastTwentyTwo | | | 88.73 277 | 88.01 274 | 90.88 302 | 91.85 314 | 82.24 325 | 98.22 287 | 95.18 333 | 88.97 231 | 82.26 302 | 96.89 208 | 71.75 303 | 96.67 280 | 84.00 280 | 82.98 265 | 93.72 285 |
|
K. test v3 | | | 88.05 281 | 87.24 280 | 90.47 307 | 91.82 316 | 82.23 326 | 98.96 230 | 97.42 228 | 89.05 227 | 76.93 318 | 95.60 241 | 68.49 317 | 95.42 307 | 85.87 267 | 81.01 279 | 93.75 281 |
|
OurMVSNet-221017-0 | | | 89.81 262 | 89.48 249 | 90.83 304 | 91.64 317 | 81.21 330 | 98.17 289 | 95.38 327 | 91.48 186 | 85.65 286 | 97.31 192 | 72.66 299 | 97.29 242 | 88.15 236 | 84.83 257 | 93.97 266 |
|
mvs_tets | | | 91.81 214 | 91.08 211 | 94.00 251 | 91.63 318 | 90.58 265 | 98.67 255 | 97.43 225 | 92.43 158 | 87.37 263 | 97.05 203 | 71.76 302 | 97.32 235 | 94.75 140 | 88.68 227 | 94.11 248 |
|
Gipuma | | | 66.95 327 | 65.00 326 | 72.79 338 | 91.52 319 | 67.96 343 | 66.16 358 | 95.15 334 | 47.89 352 | 58.54 349 | 67.99 355 | 29.74 356 | 87.54 347 | 50.20 352 | 77.83 307 | 62.87 357 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
jajsoiax | | | 91.92 212 | 91.18 210 | 94.15 243 | 91.35 320 | 90.95 259 | 99.00 226 | 97.42 228 | 92.61 147 | 87.38 262 | 97.08 200 | 72.46 300 | 97.36 230 | 94.53 145 | 88.77 225 | 94.13 247 |
|
MDA-MVSNet-bldmvs | | | 84.09 309 | 81.52 313 | 91.81 297 | 91.32 321 | 88.00 299 | 98.67 255 | 95.92 310 | 80.22 322 | 55.60 352 | 93.32 308 | 68.29 319 | 93.60 334 | 73.76 327 | 76.61 318 | 93.82 279 |
|
MVP-Stereo | | | 90.93 236 | 90.45 222 | 92.37 289 | 91.25 322 | 88.76 287 | 98.05 294 | 96.17 305 | 87.27 262 | 84.04 294 | 95.30 255 | 78.46 266 | 97.27 244 | 83.78 283 | 99.70 73 | 91.09 321 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
MDA-MVSNet_test_wron | | | 85.51 301 | 83.32 306 | 92.10 293 | 90.96 323 | 88.58 292 | 99.20 202 | 96.52 299 | 79.70 323 | 57.12 351 | 92.69 313 | 79.11 259 | 93.86 330 | 77.10 321 | 77.46 312 | 93.86 276 |
|
YYNet1 | | | 85.50 302 | 83.33 305 | 92.00 294 | 90.89 324 | 88.38 296 | 99.22 201 | 96.55 298 | 79.60 325 | 57.26 350 | 92.72 312 | 79.09 260 | 93.78 332 | 77.25 320 | 77.37 313 | 93.84 277 |
|
anonymousdsp | | | 91.79 218 | 90.92 213 | 94.41 238 | 90.76 325 | 92.93 216 | 98.93 233 | 97.17 249 | 89.08 226 | 87.46 261 | 95.30 255 | 78.43 267 | 96.92 270 | 92.38 181 | 88.73 226 | 93.39 292 |
|
lessismore_v0 | | | | | 90.53 305 | 90.58 326 | 80.90 333 | | 95.80 311 | | 77.01 317 | 95.84 234 | 66.15 325 | 96.95 268 | 83.03 288 | 75.05 323 | 93.74 284 |
|
EG-PatchMatch MVS | | | 85.35 303 | 83.81 304 | 89.99 313 | 90.39 327 | 81.89 328 | 98.21 288 | 96.09 307 | 81.78 316 | 74.73 326 | 93.72 305 | 51.56 349 | 97.12 257 | 79.16 310 | 88.61 228 | 90.96 323 |
|
CMPMVS | | 61.59 21 | 84.75 306 | 85.14 288 | 83.57 326 | 90.32 328 | 62.54 349 | 96.98 311 | 97.59 210 | 74.33 339 | 69.95 340 | 96.66 216 | 64.17 330 | 98.32 194 | 87.88 240 | 88.41 232 | 89.84 339 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
new_pmnet | | | 84.49 308 | 82.92 308 | 89.21 316 | 90.03 329 | 82.60 322 | 96.89 313 | 95.62 315 | 80.59 321 | 75.77 324 | 89.17 323 | 65.04 329 | 94.79 317 | 72.12 328 | 81.02 278 | 90.23 330 |
|
pmmvs6 | | | 85.69 298 | 83.84 303 | 91.26 301 | 90.00 330 | 84.41 317 | 97.82 298 | 96.15 306 | 75.86 333 | 81.29 305 | 95.39 249 | 61.21 337 | 96.87 273 | 83.52 286 | 73.29 327 | 92.50 308 |
|
DSMNet-mixed | | | 88.28 280 | 88.24 271 | 88.42 321 | 89.64 331 | 75.38 339 | 98.06 293 | 89.86 356 | 85.59 286 | 88.20 255 | 92.14 316 | 76.15 282 | 91.95 337 | 78.46 314 | 96.05 155 | 97.92 199 |
|
UnsupCasMVSNet_eth | | | 85.52 300 | 83.99 298 | 90.10 311 | 89.36 332 | 83.51 319 | 96.65 314 | 97.99 174 | 89.14 225 | 75.89 323 | 93.83 301 | 63.25 333 | 93.92 328 | 81.92 295 | 67.90 334 | 92.88 305 |
|
Anonymous20231206 | | | 86.32 290 | 85.42 286 | 89.02 317 | 89.11 333 | 80.53 335 | 99.05 222 | 95.28 329 | 85.43 288 | 82.82 300 | 93.92 299 | 74.40 293 | 93.44 335 | 66.99 337 | 81.83 271 | 93.08 300 |
|
OpenMVS_ROB | | 79.82 20 | 83.77 312 | 81.68 312 | 90.03 312 | 88.30 334 | 82.82 321 | 98.46 270 | 95.22 331 | 73.92 341 | 76.00 322 | 91.29 319 | 55.00 345 | 96.94 269 | 68.40 334 | 88.51 231 | 90.34 326 |
|
test20.03 | | | 84.72 307 | 83.99 298 | 86.91 323 | 88.19 335 | 80.62 334 | 98.88 236 | 95.94 309 | 88.36 243 | 78.87 312 | 94.62 287 | 68.75 315 | 89.11 342 | 66.52 338 | 75.82 319 | 91.00 322 |
|
Test4 | | | 88.80 276 | 85.91 285 | 97.48 149 | 87.33 336 | 95.72 146 | 99.29 196 | 97.04 264 | 92.82 132 | 70.35 339 | 91.46 318 | 44.37 352 | 97.43 227 | 93.37 171 | 97.17 140 | 99.29 157 |
|
MIMVSNet1 | | | 82.58 313 | 80.51 315 | 88.78 319 | 86.68 337 | 84.20 318 | 96.65 314 | 95.41 326 | 78.75 326 | 78.59 314 | 92.44 314 | 51.88 348 | 89.76 341 | 65.26 341 | 78.95 297 | 92.38 309 |
|
test2356 | | | 86.43 289 | 87.59 278 | 82.95 328 | 85.90 338 | 69.43 342 | 99.79 104 | 96.63 296 | 85.76 281 | 83.44 298 | 94.99 272 | 80.45 248 | 86.52 349 | 68.12 336 | 93.21 205 | 92.90 303 |
|
testus | | | 83.91 311 | 84.49 291 | 82.17 330 | 85.68 339 | 66.11 346 | 99.68 139 | 93.53 346 | 86.55 271 | 82.60 301 | 94.91 276 | 56.70 344 | 88.19 345 | 68.46 333 | 92.31 209 | 92.21 310 |
|
UnsupCasMVSNet_bld | | | 79.97 318 | 77.03 321 | 88.78 319 | 85.62 340 | 81.98 327 | 93.66 335 | 97.35 236 | 75.51 336 | 70.79 338 | 83.05 346 | 48.70 350 | 94.91 315 | 78.31 315 | 60.29 349 | 89.46 342 |
|
Patchmatch-RL test | | | 86.90 284 | 85.98 284 | 89.67 314 | 84.45 341 | 75.59 338 | 89.71 348 | 92.43 348 | 86.89 268 | 77.83 316 | 90.94 320 | 94.22 69 | 93.63 333 | 87.75 241 | 69.61 329 | 99.79 82 |
|
pmmvs-eth3d | | | 84.03 310 | 81.97 310 | 90.20 310 | 84.15 342 | 87.09 303 | 98.10 292 | 94.73 337 | 83.05 302 | 74.10 334 | 87.77 330 | 65.56 327 | 94.01 325 | 81.08 299 | 69.24 331 | 89.49 341 |
|
PM-MVS | | | 80.47 315 | 78.88 317 | 85.26 325 | 83.79 343 | 72.22 340 | 95.89 326 | 91.08 352 | 85.71 285 | 76.56 320 | 88.30 324 | 36.64 353 | 93.90 329 | 82.39 291 | 69.57 330 | 89.66 340 |
|
new-patchmatchnet | | | 81.19 314 | 79.34 316 | 86.76 324 | 82.86 344 | 80.36 336 | 97.92 296 | 95.27 330 | 82.09 313 | 72.02 337 | 86.87 341 | 62.81 334 | 90.74 340 | 71.10 329 | 63.08 346 | 89.19 343 |
|
testing_2 | | | 85.10 304 | 81.72 311 | 95.22 202 | 82.25 345 | 94.16 178 | 97.54 300 | 97.01 268 | 88.15 245 | 62.23 346 | 86.43 343 | 44.43 351 | 97.18 248 | 92.28 187 | 85.20 256 | 94.31 235 |
|
pmmvs3 | | | 80.27 316 | 77.77 320 | 87.76 322 | 80.32 346 | 82.43 324 | 98.23 286 | 91.97 350 | 72.74 342 | 78.75 313 | 87.97 327 | 57.30 343 | 90.99 339 | 70.31 330 | 62.37 347 | 89.87 338 |
|
1111 | | | 79.11 319 | 78.74 318 | 80.23 331 | 78.33 347 | 67.13 344 | 97.31 304 | 93.65 344 | 71.34 343 | 68.35 342 | 87.87 328 | 85.42 189 | 88.46 343 | 52.93 350 | 73.46 326 | 85.11 346 |
|
.test1245 | | | 71.48 322 | 71.80 323 | 70.51 341 | 78.33 347 | 67.13 344 | 97.31 304 | 93.65 344 | 71.34 343 | 68.35 342 | 87.87 328 | 85.42 189 | 88.46 343 | 52.93 350 | 11.01 360 | 55.94 359 |
|
test1235678 | | | 78.45 320 | 77.88 319 | 80.16 332 | 77.83 349 | 62.18 350 | 98.36 277 | 93.45 347 | 77.46 329 | 69.08 341 | 88.23 325 | 60.33 339 | 85.41 350 | 58.46 346 | 77.68 309 | 92.90 303 |
|
ambc | | | | | 83.23 327 | 77.17 350 | 62.61 348 | 87.38 351 | 94.55 339 | | 76.72 319 | 86.65 342 | 30.16 355 | 96.36 287 | 84.85 276 | 69.86 328 | 90.73 325 |
|
TDRefinement | | | 84.76 305 | 82.56 309 | 91.38 300 | 74.58 351 | 84.80 316 | 97.36 303 | 94.56 338 | 84.73 293 | 80.21 309 | 96.12 232 | 63.56 332 | 98.39 186 | 87.92 239 | 63.97 345 | 90.95 324 |
|
test12356 | | | 75.26 321 | 75.12 322 | 75.67 337 | 74.02 352 | 60.60 352 | 96.43 317 | 92.15 349 | 74.17 340 | 66.35 344 | 88.11 326 | 52.29 347 | 84.36 352 | 57.41 347 | 75.12 322 | 82.05 347 |
|
E-PMN | | | 52.30 333 | 52.18 334 | 52.67 348 | 71.51 353 | 45.40 361 | 93.62 336 | 76.60 365 | 36.01 358 | 43.50 358 | 64.13 358 | 27.11 358 | 67.31 361 | 31.06 360 | 26.06 355 | 45.30 362 |
|
EMVS | | | 51.44 335 | 51.22 336 | 52.11 349 | 70.71 354 | 44.97 363 | 94.04 332 | 75.66 366 | 35.34 360 | 42.40 359 | 61.56 361 | 28.93 357 | 65.87 362 | 27.64 361 | 24.73 356 | 45.49 361 |
|
PMMVS2 | | | 67.15 326 | 64.15 328 | 76.14 335 | 70.56 355 | 62.07 351 | 93.89 333 | 87.52 360 | 58.09 350 | 60.02 348 | 78.32 348 | 22.38 360 | 84.54 351 | 59.56 345 | 47.03 351 | 81.80 348 |
|
no-one | | | 63.48 329 | 59.26 330 | 76.14 335 | 66.71 356 | 65.06 347 | 92.75 338 | 89.92 355 | 68.96 347 | 46.96 357 | 66.55 356 | 21.74 361 | 87.68 346 | 57.07 348 | 22.69 358 | 75.68 352 |
|
FPMVS | | | 68.72 323 | 68.72 324 | 68.71 342 | 65.95 357 | 44.27 364 | 95.97 325 | 94.74 336 | 51.13 351 | 53.26 354 | 90.50 322 | 25.11 359 | 83.00 353 | 60.80 344 | 80.97 280 | 78.87 350 |
|
PNet_i23d | | | 56.44 330 | 53.54 332 | 65.14 345 | 65.34 358 | 50.33 359 | 89.06 350 | 79.57 362 | 45.77 353 | 35.75 361 | 68.95 354 | 10.75 366 | 74.40 357 | 48.48 354 | 38.20 352 | 70.70 353 |
|
wuyk23d | | | 20.37 342 | 20.84 343 | 18.99 353 | 65.34 358 | 27.73 366 | 50.43 359 | 7.67 370 | 9.50 363 | 8.01 365 | 6.34 365 | 6.13 368 | 26.24 363 | 23.40 362 | 10.69 362 | 2.99 363 |
|
testmv | | | 67.54 325 | 65.93 325 | 72.37 339 | 64.46 360 | 54.05 356 | 95.09 329 | 90.07 354 | 68.90 348 | 55.16 353 | 77.63 350 | 30.39 354 | 82.61 354 | 49.42 353 | 62.26 348 | 80.45 349 |
|
LCM-MVSNet | | | 67.77 324 | 64.73 327 | 76.87 334 | 62.95 361 | 56.25 355 | 89.37 349 | 93.74 343 | 44.53 354 | 61.99 347 | 80.74 347 | 20.42 362 | 86.53 348 | 69.37 332 | 59.50 350 | 87.84 344 |
|
MVE | | 53.74 22 | 51.54 334 | 47.86 337 | 62.60 346 | 59.56 362 | 50.93 358 | 79.41 354 | 77.69 364 | 35.69 359 | 36.27 360 | 61.76 360 | 5.79 370 | 69.63 359 | 37.97 359 | 36.61 353 | 67.24 355 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuykxyi23d | | | 50.36 336 | 45.43 338 | 65.16 344 | 51.13 363 | 51.75 357 | 77.46 355 | 78.42 363 | 41.45 355 | 26.98 364 | 54.30 363 | 6.13 368 | 74.03 358 | 46.82 356 | 26.19 354 | 69.71 354 |
|
ANet_high | | | 56.10 331 | 52.24 333 | 67.66 343 | 49.27 364 | 56.82 354 | 83.94 352 | 82.02 361 | 70.47 345 | 33.28 362 | 64.54 357 | 17.23 364 | 69.16 360 | 45.59 357 | 23.85 357 | 77.02 351 |
|
tmp_tt | | | 65.23 328 | 62.94 329 | 72.13 340 | 44.90 365 | 50.03 360 | 81.05 353 | 89.42 359 | 38.45 356 | 48.51 356 | 99.90 11 | 54.09 346 | 78.70 356 | 91.84 192 | 18.26 359 | 87.64 345 |
|
PMVS | | 49.05 23 | 53.75 332 | 51.34 335 | 60.97 347 | 40.80 366 | 34.68 365 | 74.82 356 | 89.62 358 | 37.55 357 | 28.67 363 | 72.12 351 | 7.09 367 | 81.63 355 | 43.17 358 | 68.21 333 | 66.59 356 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test123 | | | 37.68 339 | 39.14 341 | 33.31 350 | 19.94 367 | 24.83 367 | 98.36 277 | 9.75 369 | 15.53 362 | 51.31 355 | 87.14 339 | 19.62 363 | 17.74 364 | 47.10 355 | 3.47 363 | 57.36 358 |
|
testmvs | | | 40.60 338 | 44.45 339 | 29.05 352 | 19.49 368 | 14.11 368 | 99.68 139 | 18.47 368 | 20.74 361 | 64.59 345 | 98.48 169 | 10.95 365 | 17.09 365 | 56.66 349 | 11.01 360 | 55.94 359 |
|
cdsmvs_eth3d_5k | | | 23.43 341 | 31.24 342 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 98.09 167 | 0.00 364 | 0.00 366 | 99.67 76 | 83.37 202 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
pcd_1.5k_mvsjas | | | 7.60 344 | 10.13 345 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 0.00 371 | 0.00 364 | 0.00 366 | 0.00 366 | 91.20 125 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
sosnet-low-res | | | 0.00 345 | 0.00 346 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 0.00 371 | 0.00 364 | 0.00 366 | 0.00 366 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
sosnet | | | 0.00 345 | 0.00 346 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 0.00 371 | 0.00 364 | 0.00 366 | 0.00 366 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
uncertanet | | | 0.00 345 | 0.00 346 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 0.00 371 | 0.00 364 | 0.00 366 | 0.00 366 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
Regformer | | | 0.00 345 | 0.00 346 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 0.00 371 | 0.00 364 | 0.00 366 | 0.00 366 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
ab-mvs-re | | | 8.28 343 | 11.04 344 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 0.00 371 | 0.00 364 | 0.00 366 | 99.40 95 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
uanet | | | 0.00 345 | 0.00 346 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 0.00 371 | 0.00 364 | 0.00 366 | 0.00 366 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
GSMVS | | | | | | | | | | | | | | | | | 99.59 112 |
|
test_part1 | | | | | 0.00 354 | | 0.00 369 | 0.00 360 | 98.41 125 | | | | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
sam_mvs1 | | | | | | | | | | | | | 94.72 56 | | | | 99.59 112 |
|
sam_mvs | | | | | | | | | | | | | 94.25 68 | | | | |
|
MTGPA | | | | | | | | | 98.28 144 | | | | | | | | |
|
test_post1 | | | | | | | | 95.78 327 | | | | 59.23 362 | 93.20 96 | 97.74 219 | 91.06 199 | | |
|
test_post | | | | | | | | | | | | 63.35 359 | 94.43 58 | 98.13 204 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 91.70 317 | 95.12 42 | 97.95 214 | | | |
|
MTMP | | | | | | | | 99.87 69 | 96.49 300 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 99.71 17 | 99.99 14 | 100.00 1 |
|
agg_prior2 | | | | | | | | | | | | | | | 99.48 23 | 100.00 1 | 100.00 1 |
|
test_prior4 | | | | | | | 98.05 63 | 99.94 45 | | | | | | | | | |
|
test_prior2 | | | | | | | | 99.95 31 | | 95.78 50 | 99.73 14 | 99.76 56 | 96.00 24 | | 99.78 8 | 100.00 1 | |
|
旧先验2 | | | | | | | | 99.46 176 | | 94.21 90 | 99.85 5 | | | 99.95 50 | 96.96 107 | | |
|
新几何2 | | | | | | | | 99.40 180 | | | | | | | | | |
|
无先验 | | | | | | | | 99.49 170 | 98.71 62 | 93.46 117 | | | | 100.00 1 | 94.36 147 | | 99.99 12 |
|
原ACMM2 | | | | | | | | 99.90 59 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.99 26 | 90.54 209 | | |
|
segment_acmp | | | | | | | | | | | | | 96.68 12 | | | | |
|
testdata1 | | | | | | | | 99.28 197 | | 96.35 39 | | | | | | | |
|
plane_prior5 | | | | | | | | | 97.87 187 | | | | | 98.37 191 | 97.79 88 | 89.55 215 | 94.52 218 |
|
plane_prior4 | | | | | | | | | | | | 98.59 160 | | | | | |
|
plane_prior3 | | | | | | | 91.64 251 | | | 96.63 30 | 93.01 187 | | | | | | |
|
plane_prior2 | | | | | | | | 99.84 91 | | 96.38 35 | | | | | | | |
|
plane_prior | | | | | | | 91.74 245 | 99.86 86 | | 96.76 26 | | | | | | 89.59 214 | |
|
n2 | | | | | | | | | 0.00 371 | | | | | | | | |
|
nn | | | | | | | | | 0.00 371 | | | | | | | | |
|
door-mid | | | | | | | | | 89.69 357 | | | | | | | | |
|
test11 | | | | | | | | | 98.44 109 | | | | | | | | |
|
door | | | | | | | | | 90.31 353 | | | | | | | | |
|
HQP5-MVS | | | | | | | 91.85 240 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 97.92 85 | | |
|
HQP4-MVS | | | | | | | | | | | 93.37 183 | | | 98.39 186 | | | 94.53 216 |
|
HQP3-MVS | | | | | | | | | 97.89 185 | | | | | | | 89.60 212 | |
|
HQP2-MVS | | | | | | | | | | | | | 80.65 242 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 96.26 125 | 96.11 322 | | 91.89 175 | 98.06 100 | | 94.40 60 | | 94.30 150 | | 99.67 98 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.04 244 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.23 234 | |
|
Test By Simon | | | | | | | | | | | | | 92.82 102 | | | | |
|