region2R | | | 94.43 16 | 94.27 17 | 94.92 12 | 98.65 1 | 86.67 24 | 96.92 14 | 97.23 22 | 88.60 58 | 93.58 28 | 97.27 14 | 85.22 40 | 99.54 11 | 92.21 32 | 98.74 19 | 98.56 11 |
|
ACMMPR | | | 94.43 16 | 94.28 16 | 94.91 13 | 98.63 2 | 86.69 22 | 96.94 10 | 97.32 16 | 88.63 56 | 93.53 31 | 97.26 16 | 85.04 43 | 99.54 11 | 92.35 30 | 98.78 14 | 98.50 12 |
|
HFP-MVS | | | 94.52 12 | 94.40 13 | 94.86 15 | 98.61 3 | 86.81 17 | 96.94 10 | 97.34 11 | 88.63 56 | 93.65 24 | 97.21 19 | 86.10 30 | 99.49 17 | 92.35 30 | 98.77 15 | 98.30 27 |
|
#test# | | | 94.32 21 | 94.14 22 | 94.86 15 | 98.61 3 | 86.81 17 | 96.43 23 | 97.34 11 | 87.51 84 | 93.65 24 | 97.21 19 | 86.10 30 | 99.49 17 | 91.68 48 | 98.77 15 | 98.30 27 |
|
test_part2 | | | | | | 98.55 5 | 87.22 11 | | | | 96.40 3 | | | | | | |
|
ESAPD | | | 95.32 3 | 95.38 3 | 95.17 7 | 98.55 5 | 87.22 11 | 95.99 37 | 97.45 6 | 88.25 66 | 96.40 3 | 97.60 5 | 91.93 1 | 99.62 1 | 93.18 19 | 99.02 3 | 98.67 5 |
|
XVS | | | 94.45 14 | 94.32 14 | 94.85 17 | 98.54 7 | 86.60 27 | 96.93 12 | 97.19 23 | 90.66 22 | 92.85 37 | 97.16 24 | 85.02 44 | 99.49 17 | 91.99 39 | 98.56 36 | 98.47 15 |
|
X-MVStestdata | | | 88.31 138 | 86.13 186 | 94.85 17 | 98.54 7 | 86.60 27 | 96.93 12 | 97.19 23 | 90.66 22 | 92.85 37 | 23.41 358 | 85.02 44 | 99.49 17 | 91.99 39 | 98.56 36 | 98.47 15 |
|
mPP-MVS | | | 93.99 29 | 93.78 31 | 94.63 30 | 98.50 9 | 85.90 48 | 96.87 16 | 96.91 42 | 88.70 54 | 91.83 65 | 97.17 23 | 83.96 53 | 99.55 8 | 91.44 53 | 98.64 32 | 98.43 21 |
|
HSP-MVS | | | 95.30 4 | 95.48 2 | 94.76 25 | 98.49 10 | 86.52 29 | 96.91 15 | 96.73 55 | 91.73 9 | 96.10 6 | 96.69 39 | 89.90 3 | 99.30 30 | 94.70 3 | 98.04 50 | 98.45 19 |
|
MP-MVS | | | 94.25 22 | 94.07 25 | 94.77 24 | 98.47 11 | 86.31 37 | 96.71 20 | 96.98 34 | 89.04 46 | 91.98 61 | 97.19 21 | 85.43 38 | 99.56 3 | 92.06 38 | 98.79 12 | 98.44 20 |
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 6 | 98.46 12 | 87.50 9 | 95.00 90 | 97.12 27 | 87.13 90 | 92.51 51 | 96.30 55 | 89.24 8 | 99.34 24 | 93.46 13 | 98.62 33 | 98.73 3 |
|
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 85 | 93.26 32 | 97.33 12 | 84.62 48 | 99.51 15 | 90.75 62 | 98.57 35 | 98.32 26 |
|
zzz-MVS | | | 94.47 13 | 94.30 15 | 95.00 10 | 98.42 14 | 86.95 13 | 95.06 86 | 96.97 35 | 91.07 14 | 93.14 35 | 97.56 7 | 84.30 50 | 99.56 3 | 93.43 14 | 98.75 17 | 98.47 15 |
|
MTAPA | | | 94.42 18 | 94.22 18 | 95.00 10 | 98.42 14 | 86.95 13 | 94.36 143 | 96.97 35 | 91.07 14 | 93.14 35 | 97.56 7 | 84.30 50 | 99.56 3 | 93.43 14 | 98.75 17 | 98.47 15 |
|
HPM-MVS | | | 94.02 28 | 93.88 28 | 94.43 38 | 98.39 16 | 85.78 50 | 97.25 5 | 97.07 31 | 86.90 101 | 92.62 48 | 96.80 36 | 84.85 47 | 99.17 36 | 92.43 27 | 98.65 31 | 98.33 25 |
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 20 | 88.53 60 | 92.73 44 | 97.23 17 | 85.20 41 | 99.32 28 | 92.15 35 | 98.83 11 | 98.25 35 |
|
HPM-MVS_fast | | | 93.40 42 | 93.22 40 | 93.94 49 | 98.36 18 | 84.83 58 | 97.15 7 | 96.80 50 | 85.77 119 | 92.47 52 | 97.13 25 | 82.38 62 | 99.07 45 | 90.51 64 | 98.40 40 | 97.92 59 |
|
DP-MVS Recon | | | 91.95 60 | 91.28 63 | 93.96 48 | 98.33 19 | 85.92 45 | 94.66 117 | 96.66 63 | 82.69 200 | 90.03 87 | 95.82 75 | 82.30 64 | 99.03 51 | 84.57 122 | 96.48 78 | 96.91 94 |
|
APDe-MVS | | | 95.46 1 | 95.64 1 | 94.91 13 | 98.26 20 | 86.29 39 | 97.46 2 | 97.40 9 | 89.03 47 | 96.20 5 | 98.10 1 | 89.39 7 | 99.34 24 | 95.88 1 | 99.03 2 | 99.10 1 |
|
TSAR-MVS + MP. | | | 94.85 9 | 94.94 8 | 94.58 32 | 98.25 21 | 86.33 35 | 96.11 33 | 96.62 66 | 88.14 70 | 96.10 6 | 96.96 29 | 89.09 9 | 98.94 66 | 94.48 5 | 98.68 25 | 98.48 14 |
|
HPM-MVS++ | | | 95.14 7 | 94.91 9 | 95.83 1 | 98.25 21 | 89.65 1 | 95.92 43 | 96.96 38 | 91.75 8 | 94.02 20 | 96.83 33 | 88.12 12 | 99.55 8 | 93.41 16 | 98.94 6 | 98.28 29 |
|
CPTT-MVS | | | 91.99 59 | 91.80 58 | 92.55 88 | 98.24 23 | 81.98 128 | 96.76 19 | 96.49 72 | 81.89 216 | 90.24 83 | 96.44 52 | 78.59 103 | 98.61 87 | 89.68 68 | 97.85 54 | 97.06 89 |
|
MP-MVS-pluss | | | 94.21 25 | 94.00 27 | 94.85 17 | 98.17 24 | 86.65 25 | 94.82 102 | 97.17 25 | 86.26 111 | 92.83 39 | 97.87 3 | 85.57 37 | 99.56 3 | 94.37 7 | 98.92 7 | 98.34 24 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
SMA-MVS | | | 95.20 5 | 95.10 7 | 95.51 3 | 98.14 25 | 88.26 4 | 96.26 28 | 97.31 17 | 86.04 116 | 97.82 1 | 98.10 1 | 88.43 11 | 99.56 3 | 94.66 4 | 99.13 1 | 98.71 4 |
|
CNVR-MVS | | | 95.40 2 | 95.37 4 | 95.50 4 | 98.11 26 | 88.51 3 | 95.29 67 | 96.96 38 | 92.09 3 | 95.32 10 | 97.08 26 | 89.49 6 | 99.33 27 | 95.10 2 | 98.85 9 | 98.66 7 |
|
114514_t | | | 89.51 106 | 88.50 114 | 92.54 89 | 98.11 26 | 81.99 127 | 95.16 80 | 96.36 80 | 70.19 326 | 85.81 152 | 95.25 89 | 76.70 120 | 98.63 85 | 82.07 158 | 96.86 69 | 97.00 91 |
|
ACMMP | | | 93.24 48 | 92.88 49 | 94.30 42 | 98.09 28 | 85.33 54 | 96.86 17 | 97.45 6 | 88.33 63 | 90.15 85 | 97.03 27 | 81.44 76 | 99.51 15 | 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 | | | 94.24 23 | 94.07 25 | 94.75 26 | 98.06 29 | 86.90 16 | 95.88 44 | 96.94 40 | 85.68 122 | 95.05 12 | 97.18 22 | 87.31 20 | 99.07 45 | 91.90 46 | 98.61 34 | 98.28 29 |
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 30 | 84.07 78 | 96.22 29 | 97.37 10 | 84.15 154 | 90.05 86 | 95.66 80 | 87.77 14 | 99.15 39 | 89.91 67 | 98.27 43 | 98.07 46 |
|
ACMMP_Plus | | | 94.74 11 | 94.56 12 | 95.28 5 | 98.02 31 | 87.70 5 | 95.68 52 | 97.34 11 | 88.28 65 | 95.30 11 | 97.67 4 | 85.90 34 | 99.54 11 | 93.91 10 | 98.95 5 | 98.60 9 |
|
APD-MVS_3200maxsize | | | 93.78 33 | 93.77 32 | 93.80 55 | 97.92 32 | 84.19 76 | 96.30 26 | 96.87 46 | 86.96 97 | 93.92 22 | 97.47 9 | 83.88 54 | 98.96 65 | 92.71 25 | 97.87 53 | 98.26 34 |
|
NCCC | | | 94.81 10 | 94.69 11 | 95.17 7 | 97.83 33 | 87.46 10 | 95.66 54 | 96.93 41 | 92.34 2 | 93.94 21 | 96.58 46 | 87.74 15 | 99.44 21 | 92.83 23 | 98.40 40 | 98.62 8 |
|
CDPH-MVS | | | 92.83 53 | 92.30 55 | 94.44 36 | 97.79 34 | 86.11 43 | 94.06 168 | 96.66 63 | 80.09 244 | 92.77 41 | 96.63 43 | 86.62 26 | 99.04 50 | 87.40 92 | 98.66 29 | 98.17 38 |
|
DP-MVS | | | 87.25 186 | 85.36 207 | 92.90 77 | 97.65 35 | 83.24 97 | 94.81 103 | 92.00 257 | 74.99 292 | 81.92 249 | 95.00 96 | 72.66 184 | 99.05 47 | 66.92 302 | 92.33 140 | 96.40 105 |
|
PAPM_NR | | | 91.22 73 | 90.78 74 | 92.52 90 | 97.60 36 | 81.46 137 | 94.37 139 | 96.24 86 | 86.39 109 | 87.41 124 | 94.80 104 | 82.06 71 | 98.48 93 | 82.80 146 | 95.37 91 | 97.61 69 |
|
TEST9 | | | | | | 97.53 37 | 86.49 30 | 94.07 165 | 96.78 51 | 81.61 229 | 92.77 41 | 96.20 60 | 87.71 16 | 99.12 42 | | | |
|
train_agg | | | 93.44 40 | 93.08 42 | 94.52 34 | 97.53 37 | 86.49 30 | 94.07 165 | 96.78 51 | 81.86 224 | 92.77 41 | 96.20 60 | 87.63 17 | 99.12 42 | 92.14 36 | 98.69 22 | 97.94 55 |
|
abl_6 | | | 93.18 50 | 93.05 43 | 93.57 59 | 97.52 39 | 84.27 75 | 95.53 60 | 96.67 62 | 87.85 76 | 93.20 34 | 97.22 18 | 80.35 83 | 99.18 35 | 91.91 43 | 97.21 63 | 97.26 78 |
|
test_8 | | | | | | 97.49 40 | 86.30 38 | 94.02 171 | 96.76 54 | 81.86 224 | 92.70 45 | 96.20 60 | 87.63 17 | 99.02 54 | | | |
|
agg_prior3 | | | 93.27 45 | 92.89 48 | 94.40 40 | 97.49 40 | 86.12 42 | 94.07 165 | 96.73 55 | 81.46 232 | 92.46 53 | 96.05 68 | 86.90 24 | 99.15 39 | 92.14 36 | 98.69 22 | 97.94 55 |
|
DeepC-MVS_fast | | 89.43 2 | 94.04 27 | 93.79 30 | 94.80 23 | 97.48 42 | 86.78 19 | 95.65 57 | 96.89 43 | 89.40 38 | 92.81 40 | 96.97 28 | 85.37 39 | 99.24 32 | 90.87 60 | 98.69 22 | 98.38 23 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
AdaColmap | | | 89.89 99 | 89.07 102 | 92.37 97 | 97.41 43 | 83.03 103 | 94.42 132 | 95.92 108 | 82.81 196 | 86.34 145 | 94.65 107 | 73.89 167 | 99.02 54 | 80.69 178 | 95.51 87 | 95.05 148 |
|
agg_prior1 | | | 93.29 44 | 92.97 46 | 94.26 43 | 97.38 44 | 85.92 45 | 93.92 176 | 96.72 57 | 81.96 211 | 92.16 57 | 96.23 58 | 87.85 13 | 98.97 62 | 91.95 42 | 98.55 38 | 97.90 60 |
|
agg_prior | | | | | | 97.38 44 | 85.92 45 | | 96.72 57 | | 92.16 57 | | | 98.97 62 | | | |
|
原ACMM1 | | | | | 92.01 108 | 97.34 46 | 81.05 149 | | 96.81 49 | 78.89 254 | 90.45 81 | 95.92 71 | 82.65 60 | 98.84 76 | 80.68 179 | 98.26 44 | 96.14 112 |
|
MSLP-MVS++ | | | 93.72 34 | 94.08 24 | 92.65 84 | 97.31 47 | 83.43 93 | 95.79 47 | 97.33 14 | 90.03 27 | 93.58 28 | 96.96 29 | 84.87 46 | 97.76 143 | 92.19 34 | 98.66 29 | 96.76 98 |
|
新几何1 | | | | | 93.10 67 | 97.30 48 | 84.35 74 | | 95.56 135 | 71.09 323 | 91.26 73 | 96.24 57 | 82.87 59 | 98.86 71 | 79.19 209 | 98.10 48 | 96.07 118 |
|
test_prior3 | | | 93.60 37 | 93.53 36 | 93.82 52 | 97.29 49 | 84.49 65 | 94.12 157 | 96.88 44 | 87.67 81 | 92.63 46 | 96.39 53 | 86.62 26 | 98.87 68 | 91.50 50 | 98.67 27 | 98.11 44 |
|
test_prior | | | | | 93.82 52 | 97.29 49 | 84.49 65 | | 96.88 44 | | | | | 98.87 68 | | | 98.11 44 |
|
1121 | | | 90.42 87 | 89.49 91 | 93.20 63 | 97.27 51 | 84.46 68 | 92.63 232 | 95.51 142 | 71.01 324 | 91.20 74 | 96.21 59 | 82.92 58 | 99.05 47 | 80.56 181 | 98.07 49 | 96.10 116 |
|
PLC | | 84.53 7 | 89.06 122 | 88.03 128 | 92.15 105 | 97.27 51 | 82.69 117 | 94.29 144 | 95.44 151 | 79.71 248 | 84.01 217 | 94.18 121 | 76.68 121 | 98.75 80 | 77.28 226 | 93.41 124 | 95.02 149 |
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 53 | 86.69 22 | 96.19 30 | 97.11 29 | 90.42 24 | 96.95 2 | 97.27 14 | 89.53 5 | 96.91 221 | 94.38 6 | 98.85 9 | 98.03 50 |
|
test12 | | | | | 94.34 41 | 97.13 54 | 86.15 41 | | 96.29 82 | | 91.04 76 | | 85.08 42 | 99.01 56 | | 98.13 47 | 97.86 61 |
|
MG-MVS | | | 91.77 62 | 91.70 59 | 92.00 110 | 97.08 55 | 80.03 175 | 93.60 197 | 95.18 171 | 87.85 76 | 90.89 77 | 96.47 51 | 82.06 71 | 98.36 97 | 85.07 114 | 97.04 66 | 97.62 68 |
|
SteuartSystems-ACMMP | | | 95.20 5 | 95.32 6 | 94.85 17 | 96.99 56 | 86.33 35 | 97.33 3 | 97.30 18 | 91.38 12 | 95.39 9 | 97.46 10 | 88.98 10 | 99.40 22 | 94.12 8 | 98.89 8 | 98.82 2 |
Skip Steuart: Steuart Systems R&D Blog. |
MVS_111021_HR | | | 93.45 39 | 93.31 38 | 93.84 51 | 96.99 56 | 84.84 57 | 93.24 213 | 97.24 20 | 88.76 53 | 91.60 69 | 95.85 74 | 86.07 32 | 98.66 82 | 91.91 43 | 98.16 46 | 98.03 50 |
|
CNLPA | | | 89.07 120 | 87.98 129 | 92.34 98 | 96.87 58 | 84.78 59 | 94.08 163 | 93.24 231 | 81.41 233 | 84.46 204 | 95.13 94 | 75.57 145 | 96.62 237 | 77.21 227 | 93.84 115 | 95.61 136 |
|
PHI-MVS | | | 93.89 32 | 93.65 34 | 94.62 31 | 96.84 59 | 86.43 32 | 96.69 21 | 97.49 4 | 85.15 133 | 93.56 30 | 96.28 56 | 85.60 36 | 99.31 29 | 92.45 26 | 98.79 12 | 98.12 43 |
|
旧先验1 | | | | | | 96.79 60 | 81.81 130 | | 95.67 127 | | | 96.81 34 | 86.69 25 | | | 97.66 57 | 96.97 92 |
|
LFMVS | | | 90.08 92 | 89.13 101 | 92.95 75 | 96.71 61 | 82.32 123 | 96.08 34 | 89.91 311 | 86.79 102 | 92.15 59 | 96.81 34 | 62.60 287 | 98.34 100 | 87.18 96 | 93.90 113 | 98.19 37 |
|
Anonymous202405211 | | | 87.68 161 | 86.13 186 | 92.31 99 | 96.66 62 | 80.74 159 | 94.87 99 | 91.49 275 | 80.47 240 | 89.46 92 | 95.44 83 | 54.72 323 | 98.23 104 | 82.19 156 | 89.89 170 | 97.97 53 |
|
TAPA-MVS | | 84.62 6 | 88.16 142 | 87.01 153 | 91.62 126 | 96.64 63 | 80.65 160 | 94.39 135 | 96.21 90 | 76.38 278 | 86.19 148 | 95.44 83 | 79.75 90 | 98.08 127 | 62.75 323 | 95.29 93 | 96.13 113 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MAR-MVS | | | 90.30 88 | 89.37 95 | 93.07 71 | 96.61 64 | 84.48 67 | 95.68 52 | 95.67 127 | 82.36 204 | 87.85 113 | 92.85 166 | 76.63 122 | 98.80 78 | 80.01 191 | 96.68 72 | 95.91 123 |
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 58 | 91.91 57 | 93.24 62 | 96.59 65 | 83.43 93 | 94.84 101 | 96.44 73 | 89.19 43 | 94.08 19 | 95.90 72 | 77.85 114 | 98.17 109 | 88.90 74 | 93.38 125 | 98.13 42 |
|
TSAR-MVS + GP. | | | 93.66 36 | 93.41 37 | 94.41 39 | 96.59 65 | 86.78 19 | 94.40 133 | 93.93 221 | 89.77 32 | 94.21 16 | 95.59 82 | 87.35 19 | 98.61 87 | 92.72 24 | 96.15 81 | 97.83 63 |
|
test222 | | | | | | 96.55 67 | 81.70 131 | 92.22 246 | 95.01 176 | 68.36 330 | 90.20 84 | 96.14 65 | 80.26 86 | | | 97.80 55 | 96.05 120 |
|
Anonymous20240529 | | | 88.09 145 | 86.59 176 | 92.58 87 | 96.53 68 | 81.92 129 | 95.99 37 | 95.84 116 | 74.11 300 | 89.06 97 | 95.21 91 | 61.44 295 | 98.81 77 | 83.67 136 | 87.47 210 | 97.01 90 |
|
DeepPCF-MVS | | 89.96 1 | 94.20 26 | 94.77 10 | 92.49 91 | 96.52 69 | 80.00 176 | 94.00 173 | 97.08 30 | 90.05 26 | 95.65 8 | 97.29 13 | 89.66 4 | 98.97 62 | 93.95 9 | 98.71 20 | 98.50 12 |
|
testdata | | | | | 90.49 165 | 96.40 70 | 77.89 247 | | 95.37 157 | 72.51 313 | 93.63 26 | 96.69 39 | 82.08 69 | 97.65 148 | 83.08 140 | 97.39 61 | 95.94 122 |
|
PVSNet_Blended_VisFu | | | 91.38 70 | 90.91 71 | 92.80 80 | 96.39 71 | 83.17 99 | 94.87 99 | 96.66 63 | 83.29 177 | 89.27 93 | 94.46 111 | 80.29 85 | 99.17 36 | 87.57 90 | 95.37 91 | 96.05 120 |
|
API-MVS | | | 90.66 81 | 90.07 83 | 92.45 93 | 96.36 72 | 84.57 63 | 96.06 35 | 95.22 170 | 82.39 202 | 89.13 94 | 94.27 119 | 80.32 84 | 98.46 94 | 80.16 190 | 96.71 71 | 94.33 192 |
|
F-COLMAP | | | 87.95 151 | 86.80 160 | 91.40 132 | 96.35 73 | 80.88 155 | 94.73 108 | 95.45 149 | 79.65 249 | 82.04 247 | 94.61 108 | 71.13 201 | 98.50 92 | 76.24 237 | 91.05 152 | 94.80 167 |
|
VDD-MVS | | | 90.74 79 | 89.92 87 | 93.20 63 | 96.27 74 | 83.02 104 | 95.73 49 | 93.86 222 | 88.42 62 | 92.53 49 | 96.84 32 | 62.09 290 | 98.64 84 | 90.95 59 | 92.62 138 | 97.93 58 |
|
OMC-MVS | | | 91.23 72 | 90.62 75 | 93.08 69 | 96.27 74 | 84.07 78 | 93.52 199 | 95.93 107 | 86.95 98 | 89.51 90 | 96.13 66 | 78.50 105 | 98.35 99 | 85.84 109 | 92.90 135 | 96.83 97 |
|
view600 | | | 87.62 170 | 86.65 169 | 90.53 157 | 96.19 76 | 78.52 226 | 95.29 67 | 91.09 282 | 87.08 92 | 87.84 114 | 93.03 160 | 68.86 238 | 98.11 115 | 69.44 283 | 91.02 154 | 94.96 154 |
|
view800 | | | 87.62 170 | 86.65 169 | 90.53 157 | 96.19 76 | 78.52 226 | 95.29 67 | 91.09 282 | 87.08 92 | 87.84 114 | 93.03 160 | 68.86 238 | 98.11 115 | 69.44 283 | 91.02 154 | 94.96 154 |
|
conf0.05thres1000 | | | 87.62 170 | 86.65 169 | 90.53 157 | 96.19 76 | 78.52 226 | 95.29 67 | 91.09 282 | 87.08 92 | 87.84 114 | 93.03 160 | 68.86 238 | 98.11 115 | 69.44 283 | 91.02 154 | 94.96 154 |
|
tfpn | | | 87.62 170 | 86.65 169 | 90.53 157 | 96.19 76 | 78.52 226 | 95.29 67 | 91.09 282 | 87.08 92 | 87.84 114 | 93.03 160 | 68.86 238 | 98.11 115 | 69.44 283 | 91.02 154 | 94.96 154 |
|
CHOSEN 1792x2688 | | | 88.84 127 | 87.69 133 | 92.30 100 | 96.14 80 | 81.42 139 | 90.01 278 | 95.86 115 | 74.52 297 | 87.41 124 | 93.94 130 | 75.46 147 | 98.36 97 | 80.36 185 | 95.53 86 | 97.12 87 |
|
tfpn111 | | | 87.63 167 | 86.68 167 | 90.47 167 | 96.12 81 | 78.55 222 | 95.03 87 | 91.58 268 | 87.15 87 | 88.06 108 | 92.29 186 | 68.91 234 | 98.15 112 | 69.88 281 | 91.10 146 | 94.71 169 |
|
conf200view11 | | | 87.65 163 | 86.71 164 | 90.46 169 | 96.12 81 | 78.55 222 | 95.03 87 | 91.58 268 | 87.15 87 | 88.06 108 | 92.29 186 | 68.91 234 | 98.10 119 | 70.13 276 | 91.10 146 | 94.71 169 |
|
thres100view900 | | | 87.63 167 | 86.71 164 | 90.38 173 | 96.12 81 | 78.55 222 | 95.03 87 | 91.58 268 | 87.15 87 | 88.06 108 | 92.29 186 | 68.91 234 | 98.10 119 | 70.13 276 | 91.10 146 | 94.48 188 |
|
PVSNet_BlendedMVS | | | 89.98 94 | 89.70 88 | 90.82 151 | 96.12 81 | 81.25 142 | 93.92 176 | 96.83 47 | 83.49 171 | 89.10 95 | 92.26 189 | 81.04 80 | 98.85 74 | 86.72 105 | 87.86 208 | 92.35 279 |
|
PVSNet_Blended | | | 90.73 80 | 90.32 78 | 91.98 111 | 96.12 81 | 81.25 142 | 92.55 236 | 96.83 47 | 82.04 210 | 89.10 95 | 92.56 176 | 81.04 80 | 98.85 74 | 86.72 105 | 95.91 82 | 95.84 127 |
|
UA-Net | | | 92.83 53 | 92.54 53 | 93.68 57 | 96.10 86 | 84.71 60 | 95.66 54 | 96.39 78 | 91.92 4 | 93.22 33 | 96.49 50 | 83.16 56 | 98.87 68 | 84.47 123 | 95.47 89 | 97.45 75 |
|
thres600view7 | | | 87.65 163 | 86.67 168 | 90.59 154 | 96.08 87 | 78.72 218 | 94.88 98 | 91.58 268 | 87.06 96 | 88.08 107 | 92.30 185 | 68.91 234 | 98.10 119 | 70.05 280 | 91.10 146 | 94.96 154 |
|
DeepC-MVS | | 88.79 3 | 93.31 43 | 92.99 45 | 94.26 43 | 96.07 88 | 85.83 49 | 94.89 96 | 96.99 33 | 89.02 48 | 89.56 89 | 97.37 11 | 82.51 61 | 99.38 23 | 92.20 33 | 98.30 42 | 97.57 71 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
LS3D | | | 87.89 152 | 86.32 182 | 92.59 86 | 96.07 88 | 82.92 108 | 95.23 75 | 94.92 184 | 75.66 285 | 82.89 235 | 95.98 69 | 72.48 188 | 99.21 33 | 68.43 293 | 95.23 95 | 95.64 135 |
|
HyFIR lowres test | | | 88.09 145 | 86.81 159 | 91.93 114 | 96.00 90 | 80.63 161 | 90.01 278 | 95.79 120 | 73.42 304 | 87.68 122 | 92.10 195 | 73.86 168 | 97.96 134 | 80.75 177 | 91.70 142 | 97.19 83 |
|
tfpn200view9 | | | 87.58 176 | 86.64 173 | 90.41 170 | 95.99 91 | 78.64 220 | 94.58 120 | 91.98 259 | 86.94 99 | 88.09 105 | 91.77 206 | 69.18 231 | 98.10 119 | 70.13 276 | 91.10 146 | 94.48 188 |
|
thres400 | | | 87.62 170 | 86.64 173 | 90.57 155 | 95.99 91 | 78.64 220 | 94.58 120 | 91.98 259 | 86.94 99 | 88.09 105 | 91.77 206 | 69.18 231 | 98.10 119 | 70.13 276 | 91.10 146 | 94.96 154 |
|
MVS_111021_LR | | | 92.47 56 | 92.29 56 | 92.98 74 | 95.99 91 | 84.43 72 | 93.08 218 | 96.09 95 | 88.20 69 | 91.12 75 | 95.72 79 | 81.33 78 | 97.76 143 | 91.74 47 | 97.37 62 | 96.75 99 |
|
PatchMatch-RL | | | 86.77 200 | 85.54 200 | 90.47 167 | 95.88 94 | 82.71 116 | 90.54 271 | 92.31 247 | 79.82 247 | 84.32 211 | 91.57 216 | 68.77 242 | 96.39 250 | 73.16 260 | 93.48 123 | 92.32 280 |
|
EPP-MVSNet | | | 91.70 66 | 91.56 60 | 92.13 107 | 95.88 94 | 80.50 166 | 97.33 3 | 95.25 164 | 86.15 113 | 89.76 88 | 95.60 81 | 83.42 55 | 98.32 102 | 87.37 94 | 93.25 128 | 97.56 72 |
|
IS-MVSNet | | | 91.43 69 | 91.09 68 | 92.46 92 | 95.87 96 | 81.38 140 | 96.95 9 | 93.69 226 | 89.72 34 | 89.50 91 | 95.98 69 | 78.57 104 | 97.77 142 | 83.02 142 | 96.50 77 | 98.22 36 |
|
PAPR | | | 90.02 93 | 89.27 99 | 92.29 101 | 95.78 97 | 80.95 153 | 92.68 231 | 96.22 87 | 81.91 214 | 86.66 138 | 93.75 141 | 82.23 65 | 98.44 96 | 79.40 208 | 94.79 97 | 97.48 74 |
|
Vis-MVSNet (Re-imp) | | | 89.59 104 | 89.44 93 | 90.03 195 | 95.74 98 | 75.85 276 | 95.61 58 | 90.80 295 | 87.66 83 | 87.83 118 | 95.40 86 | 76.79 119 | 96.46 247 | 78.37 214 | 96.73 70 | 97.80 64 |
|
MVS_0304 | | | 93.25 47 | 92.62 51 | 95.14 9 | 95.72 99 | 87.58 8 | 94.71 113 | 96.59 68 | 91.78 7 | 91.46 70 | 96.18 64 | 75.45 148 | 99.55 8 | 93.53 11 | 98.19 45 | 98.28 29 |
|
canonicalmvs | | | 93.27 45 | 92.75 50 | 94.85 17 | 95.70 100 | 87.66 6 | 96.33 25 | 96.41 76 | 90.00 28 | 94.09 18 | 94.60 109 | 82.33 63 | 98.62 86 | 92.40 29 | 92.86 136 | 98.27 32 |
|
CANet | | | 93.54 38 | 93.20 41 | 94.55 33 | 95.65 101 | 85.73 51 | 94.94 93 | 96.69 61 | 91.89 5 | 90.69 79 | 95.88 73 | 81.99 73 | 99.54 11 | 93.14 21 | 97.95 52 | 98.39 22 |
|
3Dnovator+ | | 87.14 4 | 92.42 57 | 91.37 61 | 95.55 2 | 95.63 102 | 88.73 2 | 97.07 8 | 96.77 53 | 90.84 17 | 84.02 216 | 96.62 44 | 75.95 137 | 99.34 24 | 87.77 87 | 97.68 56 | 98.59 10 |
|
alignmvs | | | 93.08 51 | 92.50 54 | 94.81 22 | 95.62 103 | 87.61 7 | 95.99 37 | 96.07 97 | 89.77 32 | 94.12 17 | 94.87 99 | 80.56 82 | 98.66 82 | 92.42 28 | 93.10 131 | 98.15 40 |
|
tfpn1000 | | | 86.06 212 | 84.92 216 | 89.49 219 | 95.54 104 | 77.79 250 | 94.72 111 | 89.07 326 | 82.05 208 | 85.36 184 | 91.94 202 | 68.32 257 | 96.65 235 | 67.04 299 | 90.24 164 | 94.02 206 |
|
Regformer-1 | | | 94.22 24 | 94.13 23 | 94.51 35 | 95.54 104 | 86.36 34 | 94.57 122 | 96.44 73 | 91.69 10 | 94.32 15 | 96.56 48 | 87.05 23 | 99.03 51 | 93.35 17 | 97.65 58 | 98.15 40 |
|
Regformer-2 | | | 94.33 20 | 94.22 18 | 94.68 28 | 95.54 104 | 86.75 21 | 94.57 122 | 96.70 59 | 91.84 6 | 94.41 13 | 96.56 48 | 87.19 21 | 99.13 41 | 93.50 12 | 97.65 58 | 98.16 39 |
|
tfpn_ndepth | | | 86.10 211 | 84.98 212 | 89.43 221 | 95.52 107 | 78.29 237 | 94.62 118 | 89.60 317 | 81.88 223 | 85.43 176 | 90.54 252 | 68.47 248 | 96.85 225 | 68.46 292 | 90.34 163 | 93.15 255 |
|
WTY-MVS | | | 89.60 103 | 88.92 106 | 91.67 125 | 95.47 108 | 81.15 147 | 92.38 241 | 94.78 191 | 83.11 180 | 89.06 97 | 94.32 114 | 78.67 102 | 96.61 239 | 81.57 167 | 90.89 158 | 97.24 79 |
|
DELS-MVS | | | 93.43 41 | 93.25 39 | 93.97 47 | 95.42 109 | 85.04 56 | 93.06 220 | 97.13 26 | 90.74 20 | 91.84 63 | 95.09 95 | 86.32 29 | 99.21 33 | 91.22 54 | 98.45 39 | 97.65 67 |
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 | 95.36 110 | 84.61 61 | 94.68 114 | 95.83 117 | 91.27 13 | 93.60 27 | 96.71 37 | 85.75 35 | 98.86 71 | 92.87 22 | 96.65 73 | 97.96 54 |
|
Regformer-4 | | | 93.91 31 | 93.81 29 | 94.19 45 | 95.36 110 | 85.47 52 | 94.68 114 | 96.41 76 | 91.60 11 | 93.75 23 | 96.71 37 | 85.95 33 | 99.10 44 | 93.21 18 | 96.65 73 | 98.01 52 |
|
thres200 | | | 87.21 189 | 86.24 185 | 90.12 186 | 95.36 110 | 78.53 225 | 93.26 211 | 92.10 252 | 86.42 108 | 88.00 111 | 91.11 241 | 69.24 230 | 98.00 132 | 69.58 282 | 91.04 153 | 93.83 217 |
|
Vis-MVSNet | | | 91.75 63 | 91.23 65 | 93.29 60 | 95.32 113 | 83.78 83 | 96.14 32 | 95.98 104 | 89.89 29 | 90.45 81 | 96.58 46 | 75.09 152 | 98.31 103 | 84.75 120 | 96.90 67 | 97.78 66 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
BH-RMVSNet | | | 88.37 136 | 87.48 136 | 91.02 146 | 95.28 114 | 79.45 192 | 92.89 226 | 93.07 234 | 85.45 126 | 86.91 133 | 94.84 103 | 70.35 215 | 97.76 143 | 73.97 255 | 94.59 102 | 95.85 126 |
|
COLMAP_ROB | | 80.39 16 | 83.96 259 | 82.04 265 | 89.74 206 | 95.28 114 | 79.75 182 | 94.25 146 | 92.28 248 | 75.17 290 | 78.02 285 | 93.77 139 | 58.60 311 | 97.84 140 | 65.06 316 | 85.92 222 | 91.63 292 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
conf0.01 | | | 85.83 219 | 84.54 225 | 89.71 208 | 95.26 116 | 77.63 256 | 94.21 149 | 89.33 319 | 81.89 216 | 84.94 191 | 91.51 220 | 68.43 250 | 96.80 226 | 66.05 305 | 89.23 182 | 94.71 169 |
|
conf0.002 | | | 85.83 219 | 84.54 225 | 89.71 208 | 95.26 116 | 77.63 256 | 94.21 149 | 89.33 319 | 81.89 216 | 84.94 191 | 91.51 220 | 68.43 250 | 96.80 226 | 66.05 305 | 89.23 182 | 94.71 169 |
|
thresconf0.02 | | | 85.75 223 | 84.54 225 | 89.38 224 | 95.26 116 | 77.63 256 | 94.21 149 | 89.33 319 | 81.89 216 | 84.94 191 | 91.51 220 | 68.43 250 | 96.80 226 | 66.05 305 | 89.23 182 | 93.70 227 |
|
tfpn_n400 | | | 85.75 223 | 84.54 225 | 89.38 224 | 95.26 116 | 77.63 256 | 94.21 149 | 89.33 319 | 81.89 216 | 84.94 191 | 91.51 220 | 68.43 250 | 96.80 226 | 66.05 305 | 89.23 182 | 93.70 227 |
|
tfpnconf | | | 85.75 223 | 84.54 225 | 89.38 224 | 95.26 116 | 77.63 256 | 94.21 149 | 89.33 319 | 81.89 216 | 84.94 191 | 91.51 220 | 68.43 250 | 96.80 226 | 66.05 305 | 89.23 182 | 93.70 227 |
|
tfpnview11 | | | 85.75 223 | 84.54 225 | 89.38 224 | 95.26 116 | 77.63 256 | 94.21 149 | 89.33 319 | 81.89 216 | 84.94 191 | 91.51 220 | 68.43 250 | 96.80 226 | 66.05 305 | 89.23 182 | 93.70 227 |
|
PS-MVSNAJ | | | 91.18 74 | 90.92 70 | 91.96 112 | 95.26 116 | 82.60 120 | 92.09 251 | 95.70 126 | 86.27 110 | 91.84 63 | 92.46 177 | 79.70 92 | 98.99 60 | 89.08 72 | 95.86 83 | 94.29 193 |
|
BH-untuned | | | 88.60 132 | 88.13 127 | 90.01 197 | 95.24 123 | 78.50 231 | 93.29 209 | 94.15 208 | 84.75 141 | 84.46 204 | 93.40 143 | 75.76 142 | 97.40 180 | 77.59 223 | 94.52 104 | 94.12 199 |
|
ab-mvs | | | 89.41 112 | 88.35 118 | 92.60 85 | 95.15 124 | 82.65 118 | 92.20 247 | 95.60 133 | 83.97 157 | 88.55 101 | 93.70 142 | 74.16 164 | 98.21 107 | 82.46 152 | 89.37 178 | 96.94 93 |
|
VDDNet | | | 89.56 105 | 88.49 116 | 92.76 82 | 95.07 125 | 82.09 125 | 96.30 26 | 93.19 232 | 81.05 237 | 91.88 62 | 96.86 31 | 61.16 300 | 98.33 101 | 88.43 79 | 92.49 139 | 97.84 62 |
|
AllTest | | | 83.42 264 | 81.39 268 | 89.52 216 | 95.01 126 | 77.79 250 | 93.12 215 | 90.89 293 | 77.41 270 | 76.12 302 | 93.34 144 | 54.08 326 | 97.51 156 | 68.31 294 | 84.27 237 | 93.26 249 |
|
TestCases | | | | | 89.52 216 | 95.01 126 | 77.79 250 | | 90.89 293 | 77.41 270 | 76.12 302 | 93.34 144 | 54.08 326 | 97.51 156 | 68.31 294 | 84.27 237 | 93.26 249 |
|
EI-MVSNet-Vis-set | | | 93.01 52 | 92.92 47 | 93.29 60 | 95.01 126 | 83.51 92 | 94.48 125 | 95.77 121 | 90.87 16 | 92.52 50 | 96.67 41 | 84.50 49 | 99.00 59 | 91.99 39 | 94.44 108 | 97.36 76 |
|
xiu_mvs_v2_base | | | 91.13 75 | 90.89 72 | 91.86 117 | 94.97 129 | 82.42 121 | 92.24 245 | 95.64 132 | 86.11 115 | 91.74 68 | 93.14 155 | 79.67 95 | 98.89 67 | 89.06 73 | 95.46 90 | 94.28 194 |
|
Test_1112_low_res | | | 87.65 163 | 86.51 178 | 91.08 142 | 94.94 130 | 79.28 206 | 91.77 254 | 94.30 204 | 76.04 283 | 83.51 228 | 92.37 182 | 77.86 113 | 97.73 147 | 78.69 213 | 89.13 190 | 96.22 110 |
|
1112_ss | | | 88.42 134 | 87.33 140 | 91.72 123 | 94.92 131 | 80.98 151 | 92.97 224 | 94.54 196 | 78.16 267 | 83.82 220 | 93.88 135 | 78.78 100 | 97.91 138 | 79.45 204 | 89.41 177 | 96.26 109 |
|
QAPM | | | 89.51 106 | 88.15 126 | 93.59 58 | 94.92 131 | 84.58 62 | 96.82 18 | 96.70 59 | 78.43 262 | 83.41 230 | 96.19 63 | 73.18 178 | 99.30 30 | 77.11 229 | 96.54 76 | 96.89 96 |
|
BH-w/o | | | 87.57 177 | 87.05 152 | 89.12 233 | 94.90 133 | 77.90 246 | 92.41 239 | 93.51 228 | 82.89 195 | 83.70 223 | 91.34 227 | 75.75 143 | 97.07 208 | 75.49 241 | 93.49 121 | 92.39 277 |
|
EI-MVSNet-UG-set | | | 92.74 55 | 92.62 51 | 93.12 66 | 94.86 134 | 83.20 98 | 94.40 133 | 95.74 124 | 90.71 21 | 92.05 60 | 96.60 45 | 84.00 52 | 98.99 60 | 91.55 49 | 93.63 118 | 97.17 84 |
|
HY-MVS | | 83.01 12 | 89.03 123 | 87.94 131 | 92.29 101 | 94.86 134 | 82.77 110 | 92.08 252 | 94.49 197 | 81.52 231 | 86.93 132 | 92.79 172 | 78.32 108 | 98.23 104 | 79.93 194 | 90.55 159 | 95.88 125 |
|
Fast-Effi-MVS+ | | | 89.41 112 | 88.64 111 | 91.71 124 | 94.74 136 | 80.81 157 | 93.54 198 | 95.10 173 | 83.11 180 | 86.82 136 | 90.67 248 | 79.74 91 | 97.75 146 | 80.51 183 | 93.55 119 | 96.57 103 |
|
ACMP | | 84.23 8 | 89.01 125 | 88.35 118 | 90.99 148 | 94.73 137 | 81.27 141 | 95.07 84 | 95.89 113 | 86.48 106 | 83.67 224 | 94.30 115 | 69.33 226 | 97.99 133 | 87.10 101 | 88.55 195 | 93.72 226 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PVSNet | | 78.82 18 | 85.55 229 | 84.65 223 | 88.23 262 | 94.72 138 | 71.93 305 | 87.12 312 | 92.75 240 | 78.80 257 | 84.95 190 | 90.53 254 | 64.43 282 | 96.71 234 | 74.74 249 | 93.86 114 | 96.06 119 |
|
LCM-MVSNet-Re | | | 88.30 139 | 88.32 121 | 88.27 259 | 94.71 139 | 72.41 304 | 93.15 214 | 90.98 290 | 87.77 78 | 79.25 279 | 91.96 201 | 78.35 107 | 95.75 275 | 83.04 141 | 95.62 85 | 96.65 101 |
|
HQP_MVS | | | 90.60 85 | 90.19 80 | 91.82 120 | 94.70 140 | 82.73 114 | 95.85 45 | 96.22 87 | 90.81 18 | 86.91 133 | 94.86 100 | 74.23 160 | 98.12 113 | 88.15 81 | 89.99 167 | 94.63 174 |
|
plane_prior7 | | | | | | 94.70 140 | 82.74 113 | | | | | | | | | | |
|
casdiffmvs | | | 91.72 65 | 91.26 64 | 93.10 67 | 94.66 142 | 83.75 84 | 94.77 106 | 96.00 103 | 83.98 156 | 90.74 78 | 93.96 129 | 82.08 69 | 98.19 108 | 91.47 52 | 93.68 116 | 97.36 76 |
|
ACMH+ | | 81.04 14 | 85.05 238 | 83.46 250 | 89.82 202 | 94.66 142 | 79.37 200 | 94.44 130 | 94.12 210 | 82.19 206 | 78.04 284 | 92.82 169 | 58.23 312 | 97.54 154 | 73.77 257 | 82.90 251 | 92.54 271 |
|
ACMM | | 84.12 9 | 89.14 118 | 88.48 117 | 91.12 139 | 94.65 144 | 81.22 144 | 95.31 63 | 96.12 94 | 85.31 129 | 85.92 151 | 94.34 112 | 70.19 218 | 98.06 129 | 85.65 110 | 88.86 193 | 94.08 203 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
plane_prior1 | | | | | | 94.59 145 | | | | | | | | | | | |
|
3Dnovator | | 86.66 5 | 91.73 64 | 90.82 73 | 94.44 36 | 94.59 145 | 86.37 33 | 97.18 6 | 97.02 32 | 89.20 42 | 84.31 212 | 96.66 42 | 73.74 171 | 99.17 36 | 86.74 102 | 97.96 51 | 97.79 65 |
|
plane_prior6 | | | | | | 94.52 147 | 82.75 111 | | | | | | 74.23 160 | | | | |
|
UGNet | | | 89.95 96 | 88.95 105 | 92.95 75 | 94.51 148 | 83.31 96 | 95.70 51 | 95.23 168 | 89.37 39 | 87.58 123 | 93.94 130 | 64.00 283 | 98.78 79 | 83.92 133 | 96.31 80 | 96.74 100 |
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 | | | 89.45 109 | 88.90 107 | 91.12 139 | 94.47 149 | 81.49 135 | 95.30 65 | 96.14 91 | 86.73 103 | 85.45 173 | 95.16 92 | 69.89 219 | 98.10 119 | 87.70 88 | 89.23 182 | 93.77 222 |
|
LGP-MVS_train | | | | | 91.12 139 | 94.47 149 | 81.49 135 | | 96.14 91 | 86.73 103 | 85.45 173 | 95.16 92 | 69.89 219 | 98.10 119 | 87.70 88 | 89.23 182 | 93.77 222 |
|
ACMH | | 80.38 17 | 85.36 231 | 83.68 243 | 90.39 171 | 94.45 151 | 80.63 161 | 94.73 108 | 94.85 187 | 82.09 207 | 77.24 291 | 92.65 174 | 60.01 306 | 97.58 151 | 72.25 264 | 84.87 232 | 92.96 260 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
LTVRE_ROB | | 82.13 13 | 86.26 209 | 84.90 217 | 90.34 176 | 94.44 152 | 81.50 134 | 92.31 243 | 94.89 185 | 83.03 187 | 79.63 276 | 92.67 173 | 69.69 222 | 97.79 141 | 71.20 268 | 86.26 221 | 91.72 290 |
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 71 | 91.11 66 | 91.93 114 | 94.37 153 | 80.14 170 | 93.46 202 | 95.80 119 | 86.46 107 | 91.35 72 | 93.77 139 | 82.21 66 | 98.09 126 | 87.57 90 | 94.95 96 | 97.55 73 |
|
NP-MVS | | | | | | 94.37 153 | 82.42 121 | | | | | 93.98 127 | | | | | |
|
TR-MVS | | | 86.78 198 | 85.76 198 | 89.82 202 | 94.37 153 | 78.41 233 | 92.47 238 | 92.83 237 | 81.11 236 | 86.36 144 | 92.40 180 | 68.73 243 | 97.48 158 | 73.75 258 | 89.85 172 | 93.57 240 |
|
Effi-MVS+ | | | 91.59 68 | 91.11 66 | 93.01 73 | 94.35 156 | 83.39 95 | 94.60 119 | 95.10 173 | 87.10 91 | 90.57 80 | 93.10 157 | 81.43 77 | 98.07 128 | 89.29 71 | 94.48 105 | 97.59 70 |
|
CLD-MVS | | | 89.47 108 | 88.90 107 | 91.18 138 | 94.22 157 | 82.07 126 | 92.13 249 | 96.09 95 | 87.90 74 | 85.37 183 | 92.45 178 | 74.38 158 | 97.56 153 | 87.15 97 | 90.43 160 | 93.93 208 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
HQP-NCC | | | | | | 94.17 158 | | 94.39 135 | | 88.81 50 | 85.43 176 | | | | | | |
|
ACMP_Plane | | | | | | 94.17 158 | | 94.39 135 | | 88.81 50 | 85.43 176 | | | | | | |
|
HQP-MVS | | | 89.80 100 | 89.28 98 | 91.34 133 | 94.17 158 | 81.56 132 | 94.39 135 | 96.04 101 | 88.81 50 | 85.43 176 | 93.97 128 | 73.83 169 | 97.96 134 | 87.11 99 | 89.77 173 | 94.50 185 |
|
XVG-OURS | | | 89.40 114 | 88.70 110 | 91.52 128 | 94.06 161 | 81.46 137 | 91.27 266 | 96.07 97 | 86.14 114 | 88.89 99 | 95.77 77 | 68.73 243 | 97.26 193 | 87.39 93 | 89.96 169 | 95.83 128 |
|
sss | | | 88.93 126 | 88.26 125 | 90.94 150 | 94.05 162 | 80.78 158 | 91.71 257 | 95.38 155 | 81.55 230 | 88.63 100 | 93.91 134 | 75.04 153 | 95.47 286 | 82.47 151 | 91.61 143 | 96.57 103 |
|
PCF-MVS | | 84.11 10 | 87.74 160 | 86.08 190 | 92.70 83 | 94.02 163 | 84.43 72 | 89.27 289 | 95.87 114 | 73.62 303 | 84.43 206 | 94.33 113 | 78.48 106 | 98.86 71 | 70.27 272 | 94.45 107 | 94.81 166 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
GBi-Net | | | 87.26 184 | 85.98 192 | 91.08 142 | 94.01 164 | 83.10 100 | 95.14 81 | 94.94 180 | 83.57 167 | 84.37 207 | 91.64 209 | 66.59 266 | 96.34 253 | 78.23 217 | 85.36 227 | 93.79 218 |
|
test1 | | | 87.26 184 | 85.98 192 | 91.08 142 | 94.01 164 | 83.10 100 | 95.14 81 | 94.94 180 | 83.57 167 | 84.37 207 | 91.64 209 | 66.59 266 | 96.34 253 | 78.23 217 | 85.36 227 | 93.79 218 |
|
FMVSNet2 | | | 87.19 190 | 85.82 197 | 91.30 135 | 94.01 164 | 83.67 87 | 94.79 104 | 94.94 180 | 83.57 167 | 83.88 218 | 92.05 199 | 66.59 266 | 96.51 243 | 77.56 224 | 85.01 231 | 93.73 225 |
|
XVG-OURS-SEG-HR | | | 89.95 96 | 89.45 92 | 91.47 130 | 94.00 167 | 81.21 145 | 91.87 253 | 96.06 99 | 85.78 118 | 88.55 101 | 95.73 78 | 74.67 156 | 97.27 191 | 88.71 76 | 89.64 175 | 95.91 123 |
|
FIs | | | 90.51 86 | 90.35 77 | 90.99 148 | 93.99 168 | 80.98 151 | 95.73 49 | 97.54 3 | 89.15 44 | 86.72 137 | 94.68 105 | 81.83 75 | 97.24 195 | 85.18 113 | 88.31 203 | 94.76 168 |
|
xiu_mvs_v1_base_debu | | | 90.64 82 | 90.05 84 | 92.40 94 | 93.97 169 | 84.46 68 | 93.32 204 | 95.46 145 | 85.17 130 | 92.25 54 | 94.03 122 | 70.59 210 | 98.57 89 | 90.97 56 | 94.67 98 | 94.18 195 |
|
xiu_mvs_v1_base | | | 90.64 82 | 90.05 84 | 92.40 94 | 93.97 169 | 84.46 68 | 93.32 204 | 95.46 145 | 85.17 130 | 92.25 54 | 94.03 122 | 70.59 210 | 98.57 89 | 90.97 56 | 94.67 98 | 94.18 195 |
|
xiu_mvs_v1_base_debi | | | 90.64 82 | 90.05 84 | 92.40 94 | 93.97 169 | 84.46 68 | 93.32 204 | 95.46 145 | 85.17 130 | 92.25 54 | 94.03 122 | 70.59 210 | 98.57 89 | 90.97 56 | 94.67 98 | 94.18 195 |
|
VPA-MVSNet | | | 89.62 102 | 88.96 104 | 91.60 127 | 93.86 172 | 82.89 109 | 95.46 61 | 97.33 14 | 87.91 73 | 88.43 103 | 93.31 147 | 74.17 163 | 97.40 180 | 87.32 95 | 82.86 252 | 94.52 183 |
|
MVSFormer | | | 91.68 67 | 91.30 62 | 92.80 80 | 93.86 172 | 83.88 81 | 95.96 41 | 95.90 111 | 84.66 143 | 91.76 66 | 94.91 97 | 77.92 111 | 97.30 187 | 89.64 69 | 97.11 64 | 97.24 79 |
|
lupinMVS | | | 90.92 77 | 90.21 79 | 93.03 72 | 93.86 172 | 83.88 81 | 92.81 227 | 93.86 222 | 79.84 246 | 91.76 66 | 94.29 116 | 77.92 111 | 98.04 130 | 90.48 65 | 97.11 64 | 97.17 84 |
|
IterMVS-LS | | | 88.36 137 | 87.91 132 | 89.70 210 | 93.80 175 | 78.29 237 | 93.73 188 | 95.08 175 | 85.73 120 | 84.75 198 | 91.90 204 | 79.88 88 | 96.92 220 | 83.83 134 | 82.51 254 | 93.89 210 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MSDG | | | 84.86 245 | 83.09 256 | 90.14 185 | 93.80 175 | 80.05 173 | 89.18 292 | 93.09 233 | 78.89 254 | 78.19 282 | 91.91 203 | 65.86 276 | 97.27 191 | 68.47 291 | 88.45 199 | 93.11 256 |
|
FMVSNet3 | | | 87.40 182 | 86.11 188 | 91.30 135 | 93.79 177 | 83.64 88 | 94.20 155 | 94.81 190 | 83.89 158 | 84.37 207 | 91.87 205 | 68.45 249 | 96.56 240 | 78.23 217 | 85.36 227 | 93.70 227 |
|
FC-MVSNet-test | | | 90.27 89 | 90.18 81 | 90.53 157 | 93.71 178 | 79.85 180 | 95.77 48 | 97.59 2 | 89.31 40 | 86.27 146 | 94.67 106 | 81.93 74 | 97.01 212 | 84.26 128 | 88.09 206 | 94.71 169 |
|
TAMVS | | | 89.21 117 | 88.29 123 | 91.96 112 | 93.71 178 | 82.62 119 | 93.30 208 | 94.19 206 | 82.22 205 | 87.78 120 | 93.94 130 | 78.83 99 | 96.95 218 | 77.70 222 | 92.98 133 | 96.32 107 |
|
CDS-MVSNet | | | 89.45 109 | 88.51 113 | 92.29 101 | 93.62 180 | 83.61 90 | 93.01 221 | 94.68 193 | 81.95 212 | 87.82 119 | 93.24 151 | 78.69 101 | 96.99 213 | 80.34 186 | 93.23 129 | 96.28 108 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
UniMVSNet (Re) | | | 89.80 100 | 89.07 102 | 92.01 108 | 93.60 181 | 84.52 64 | 94.78 105 | 97.47 5 | 89.26 41 | 86.44 143 | 92.32 184 | 82.10 68 | 97.39 183 | 84.81 119 | 80.84 283 | 94.12 199 |
|
VPNet | | | 88.20 141 | 87.47 137 | 90.39 171 | 93.56 182 | 79.46 190 | 94.04 169 | 95.54 138 | 88.67 55 | 86.96 131 | 94.58 110 | 69.33 226 | 97.15 201 | 84.05 132 | 80.53 288 | 94.56 181 |
|
mvs_anonymous | | | 89.37 115 | 89.32 96 | 89.51 218 | 93.47 183 | 74.22 283 | 91.65 260 | 94.83 189 | 82.91 194 | 85.45 173 | 93.79 138 | 81.23 79 | 96.36 252 | 86.47 108 | 94.09 111 | 97.94 55 |
|
CANet_DTU | | | 90.26 90 | 89.41 94 | 92.81 79 | 93.46 184 | 83.01 105 | 93.48 200 | 94.47 198 | 89.43 37 | 87.76 121 | 94.23 120 | 70.54 214 | 99.03 51 | 84.97 115 | 96.39 79 | 96.38 106 |
|
UniMVSNet_NR-MVSNet | | | 89.92 98 | 89.29 97 | 91.81 122 | 93.39 185 | 83.72 85 | 94.43 131 | 97.12 27 | 89.80 31 | 86.46 140 | 93.32 146 | 83.16 56 | 97.23 197 | 84.92 116 | 81.02 279 | 94.49 187 |
|
Effi-MVS+-dtu | | | 88.65 131 | 88.35 118 | 89.54 215 | 93.33 186 | 76.39 271 | 94.47 128 | 94.36 201 | 87.70 79 | 85.43 176 | 89.56 270 | 73.45 174 | 97.26 193 | 85.57 111 | 91.28 145 | 94.97 151 |
|
mvs-test1 | | | 89.45 109 | 89.14 100 | 90.38 173 | 93.33 186 | 77.63 256 | 94.95 92 | 94.36 201 | 87.70 79 | 87.10 130 | 92.81 170 | 73.45 174 | 98.03 131 | 85.57 111 | 93.04 132 | 95.48 138 |
|
WR-MVS | | | 88.38 135 | 87.67 134 | 90.52 163 | 93.30 188 | 80.18 168 | 93.26 211 | 95.96 106 | 88.57 59 | 85.47 172 | 92.81 170 | 76.12 126 | 96.91 221 | 81.24 169 | 82.29 256 | 94.47 190 |
|
diffmvs | | | 89.07 120 | 88.32 121 | 91.34 133 | 93.24 189 | 79.79 181 | 92.29 244 | 94.98 179 | 80.24 241 | 87.38 127 | 92.45 178 | 78.02 109 | 97.33 185 | 83.29 139 | 92.93 134 | 96.91 94 |
|
WR-MVS_H | | | 87.80 158 | 87.37 139 | 89.10 235 | 93.23 190 | 78.12 241 | 95.61 58 | 97.30 18 | 87.90 74 | 83.72 222 | 92.01 200 | 79.65 96 | 96.01 264 | 76.36 234 | 80.54 287 | 93.16 253 |
|
test_0402 | | | 81.30 287 | 79.17 291 | 87.67 271 | 93.19 191 | 78.17 240 | 92.98 223 | 91.71 264 | 75.25 289 | 76.02 305 | 90.31 258 | 59.23 309 | 96.37 251 | 50.22 341 | 83.63 244 | 88.47 335 |
|
OPM-MVS | | | 90.12 91 | 89.56 90 | 91.82 120 | 93.14 192 | 83.90 80 | 94.16 156 | 95.74 124 | 88.96 49 | 87.86 112 | 95.43 85 | 72.48 188 | 97.91 138 | 88.10 84 | 90.18 166 | 93.65 232 |
|
CP-MVSNet | | | 87.63 167 | 87.26 143 | 88.74 240 | 93.12 193 | 76.59 270 | 95.29 67 | 96.58 70 | 88.43 61 | 83.49 229 | 92.98 164 | 75.28 149 | 95.83 271 | 78.97 210 | 81.15 276 | 93.79 218 |
|
nrg030 | | | 91.08 76 | 90.39 76 | 93.17 65 | 93.07 194 | 86.91 15 | 96.41 24 | 96.26 83 | 88.30 64 | 88.37 104 | 94.85 102 | 82.19 67 | 97.64 150 | 91.09 55 | 82.95 250 | 94.96 154 |
|
PAPM | | | 86.68 201 | 85.39 206 | 90.53 157 | 93.05 195 | 79.33 205 | 89.79 282 | 94.77 192 | 78.82 256 | 81.95 248 | 93.24 151 | 76.81 118 | 97.30 187 | 66.94 300 | 93.16 130 | 94.95 161 |
|
DU-MVS | | | 89.34 116 | 88.50 114 | 91.85 118 | 93.04 196 | 83.72 85 | 94.47 128 | 96.59 68 | 89.50 36 | 86.46 140 | 93.29 149 | 77.25 115 | 97.23 197 | 84.92 116 | 81.02 279 | 94.59 178 |
|
NR-MVSNet | | | 88.58 133 | 87.47 137 | 91.93 114 | 93.04 196 | 84.16 77 | 94.77 106 | 96.25 85 | 89.05 45 | 80.04 273 | 93.29 149 | 79.02 98 | 97.05 210 | 81.71 166 | 80.05 293 | 94.59 178 |
|
jason | | | 90.80 78 | 90.10 82 | 92.90 77 | 93.04 196 | 83.53 91 | 93.08 218 | 94.15 208 | 80.22 242 | 91.41 71 | 94.91 97 | 76.87 117 | 97.93 137 | 90.28 66 | 96.90 67 | 97.24 79 |
jason: jason. |
PS-CasMVS | | | 87.32 183 | 86.88 155 | 88.63 243 | 92.99 199 | 76.33 273 | 95.33 62 | 96.61 67 | 88.22 68 | 83.30 232 | 93.07 158 | 73.03 180 | 95.79 274 | 78.36 215 | 81.00 281 | 93.75 224 |
|
MVSTER | | | 88.84 127 | 88.29 123 | 90.51 164 | 92.95 200 | 80.44 167 | 93.73 188 | 95.01 176 | 84.66 143 | 87.15 128 | 93.12 156 | 72.79 182 | 97.21 199 | 87.86 86 | 87.36 213 | 93.87 213 |
|
RPSCF | | | 85.07 237 | 84.27 231 | 87.48 277 | 92.91 201 | 70.62 317 | 91.69 259 | 92.46 245 | 76.20 282 | 82.67 238 | 95.22 90 | 63.94 284 | 97.29 190 | 77.51 225 | 85.80 224 | 94.53 182 |
|
Anonymous20240521 | | | 86.87 195 | 85.95 194 | 89.64 212 | 92.89 202 | 78.88 217 | 95.66 54 | 96.05 100 | 84.77 140 | 81.92 249 | 92.39 181 | 71.54 196 | 96.96 216 | 76.46 233 | 81.87 266 | 93.08 258 |
|
FMVSNet1 | | | 85.85 217 | 84.11 233 | 91.08 142 | 92.81 203 | 83.10 100 | 95.14 81 | 94.94 180 | 81.64 227 | 82.68 237 | 91.64 209 | 59.01 310 | 96.34 253 | 75.37 243 | 83.78 240 | 93.79 218 |
|
tfpnnormal | | | 84.72 251 | 83.23 255 | 89.20 232 | 92.79 204 | 80.05 173 | 94.48 125 | 95.81 118 | 82.38 203 | 81.08 259 | 91.21 235 | 69.01 233 | 96.95 218 | 61.69 325 | 80.59 286 | 90.58 317 |
|
OpenMVS | | 83.78 11 | 88.74 130 | 87.29 141 | 93.08 69 | 92.70 205 | 85.39 53 | 96.57 22 | 96.43 75 | 78.74 259 | 80.85 261 | 96.07 67 | 69.64 223 | 99.01 56 | 78.01 220 | 96.65 73 | 94.83 165 |
|
TranMVSNet+NR-MVSNet | | | 88.84 127 | 87.95 130 | 91.49 129 | 92.68 206 | 83.01 105 | 94.92 95 | 96.31 81 | 89.88 30 | 85.53 167 | 93.85 137 | 76.63 122 | 96.96 216 | 81.91 162 | 79.87 298 | 94.50 185 |
|
MVS | | | 87.44 180 | 86.10 189 | 91.44 131 | 92.61 207 | 83.62 89 | 92.63 232 | 95.66 129 | 67.26 334 | 81.47 253 | 92.15 191 | 77.95 110 | 98.22 106 | 79.71 200 | 95.48 88 | 92.47 274 |
|
CHOSEN 280x420 | | | 85.15 236 | 83.99 235 | 88.65 242 | 92.47 208 | 78.40 234 | 79.68 344 | 92.76 239 | 74.90 294 | 81.41 255 | 89.59 268 | 69.85 221 | 95.51 282 | 79.92 195 | 95.29 93 | 92.03 284 |
|
1314 | | | 87.51 178 | 86.57 177 | 90.34 176 | 92.42 209 | 79.74 183 | 92.63 232 | 95.35 159 | 78.35 263 | 80.14 271 | 91.62 213 | 74.05 165 | 97.15 201 | 81.05 170 | 93.53 120 | 94.12 199 |
|
pcd1.5k->3k | | | 37.02 334 | 38.84 335 | 31.53 347 | 92.33 210 | 0.00 367 | 0.00 359 | 96.13 93 | 0.00 362 | 0.00 363 | 0.00 364 | 72.70 183 | 0.00 365 | 0.00 362 | 88.43 200 | 94.60 177 |
|
PEN-MVS | | | 86.80 197 | 86.27 184 | 88.40 256 | 92.32 211 | 75.71 277 | 95.18 78 | 96.38 79 | 87.97 71 | 82.82 236 | 93.15 154 | 73.39 176 | 95.92 267 | 76.15 238 | 79.03 302 | 93.59 239 |
|
Patchmatch-test1 | | | 85.81 221 | 84.71 221 | 89.12 233 | 92.15 212 | 76.60 269 | 91.12 269 | 91.69 266 | 83.53 170 | 85.50 170 | 88.56 283 | 66.79 264 | 95.00 303 | 72.69 262 | 90.35 162 | 95.76 131 |
|
XXY-MVS | | | 87.65 163 | 86.85 157 | 90.03 195 | 92.14 213 | 80.60 163 | 93.76 185 | 95.23 168 | 82.94 192 | 84.60 200 | 94.02 125 | 74.27 159 | 95.49 285 | 81.04 171 | 83.68 243 | 94.01 207 |
|
IB-MVS | | 80.51 15 | 85.24 235 | 83.26 254 | 91.19 137 | 92.13 214 | 79.86 179 | 91.75 255 | 91.29 280 | 83.28 178 | 80.66 264 | 88.49 284 | 61.28 296 | 98.46 94 | 80.99 174 | 79.46 300 | 95.25 145 |
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 | | | 86.43 207 | 84.98 212 | 90.80 152 | 92.10 215 | 80.92 154 | 90.24 274 | 95.91 110 | 73.10 307 | 83.57 227 | 88.39 285 | 65.15 278 | 97.46 160 | 84.90 118 | 91.43 144 | 94.03 205 |
|
Fast-Effi-MVS+-dtu | | | 87.44 180 | 86.72 163 | 89.63 213 | 92.04 216 | 77.68 255 | 94.03 170 | 93.94 220 | 85.81 117 | 82.42 239 | 91.32 230 | 70.33 216 | 97.06 209 | 80.33 187 | 90.23 165 | 94.14 198 |
|
PS-MVSNAJss | | | 89.97 95 | 89.62 89 | 91.02 146 | 91.90 217 | 80.85 156 | 95.26 74 | 95.98 104 | 86.26 111 | 86.21 147 | 94.29 116 | 79.70 92 | 97.65 148 | 88.87 75 | 88.10 204 | 94.57 180 |
|
ITE_SJBPF | | | | | 88.24 261 | 91.88 218 | 77.05 266 | | 92.92 235 | 85.54 124 | 80.13 272 | 93.30 148 | 57.29 315 | 96.20 257 | 72.46 263 | 84.71 233 | 91.49 294 |
|
EI-MVSNet | | | 89.10 119 | 88.86 109 | 89.80 205 | 91.84 219 | 78.30 236 | 93.70 192 | 95.01 176 | 85.73 120 | 87.15 128 | 95.28 87 | 79.87 89 | 97.21 199 | 83.81 135 | 87.36 213 | 93.88 212 |
|
CVMVSNet | | | 84.69 253 | 84.79 220 | 84.37 311 | 91.84 219 | 64.92 335 | 93.70 192 | 91.47 276 | 66.19 336 | 86.16 149 | 95.28 87 | 67.18 262 | 93.33 319 | 80.89 176 | 90.42 161 | 94.88 163 |
|
MVS-HIRNet | | | 73.70 311 | 72.20 311 | 78.18 325 | 91.81 221 | 56.42 347 | 82.94 338 | 82.58 347 | 55.24 346 | 68.88 330 | 66.48 347 | 55.32 321 | 95.13 299 | 58.12 332 | 88.42 201 | 83.01 342 |
|
PatchmatchNet | | | 85.85 217 | 84.70 222 | 89.29 229 | 91.76 222 | 75.54 278 | 88.49 300 | 91.30 279 | 81.63 228 | 85.05 188 | 88.70 280 | 71.71 192 | 96.24 256 | 74.61 251 | 89.05 191 | 96.08 117 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
TransMVSNet (Re) | | | 84.43 256 | 83.06 257 | 88.54 252 | 91.72 223 | 78.44 232 | 95.18 78 | 92.82 238 | 82.73 198 | 79.67 275 | 92.12 192 | 73.49 173 | 95.96 266 | 71.10 271 | 68.73 337 | 91.21 299 |
|
semantic-postprocess | | | | | 88.18 263 | 91.71 224 | 76.87 268 | | 92.65 243 | 85.40 127 | 81.44 254 | 90.54 252 | 66.21 270 | 95.00 303 | 81.04 171 | 81.05 277 | 92.66 269 |
|
TinyColmap | | | 79.76 297 | 77.69 297 | 85.97 299 | 91.71 224 | 73.12 293 | 89.55 283 | 90.36 301 | 75.03 291 | 72.03 325 | 90.19 259 | 46.22 340 | 96.19 258 | 63.11 321 | 81.03 278 | 88.59 331 |
|
MDTV_nov1_ep13 | | | | 83.56 246 | | 91.69 226 | 69.93 321 | 87.75 308 | 91.54 273 | 78.60 260 | 84.86 197 | 88.90 276 | 69.54 224 | 96.03 262 | 70.25 273 | 88.93 192 | |
|
DTE-MVSNet | | | 86.11 210 | 85.48 204 | 87.98 266 | 91.65 227 | 74.92 280 | 94.93 94 | 95.75 123 | 87.36 86 | 82.26 241 | 93.04 159 | 72.85 181 | 95.82 272 | 74.04 254 | 77.46 308 | 93.20 251 |
|
PatchFormer-LS_test | | | 86.02 213 | 85.13 210 | 88.70 241 | 91.52 228 | 74.12 286 | 91.19 268 | 92.09 253 | 82.71 199 | 84.30 213 | 87.24 301 | 70.87 205 | 96.98 214 | 81.04 171 | 85.17 230 | 95.00 150 |
|
MIMVSNet | | | 82.59 272 | 80.53 275 | 88.76 239 | 91.51 229 | 78.32 235 | 86.57 315 | 90.13 305 | 79.32 250 | 80.70 263 | 88.69 281 | 52.98 328 | 93.07 324 | 66.03 311 | 88.86 193 | 94.90 162 |
|
tpmp4_e23 | | | 83.87 262 | 82.33 263 | 88.48 253 | 91.46 230 | 72.82 296 | 89.82 281 | 91.57 272 | 73.02 309 | 81.86 251 | 89.05 273 | 66.20 271 | 96.97 215 | 71.57 266 | 86.39 220 | 95.66 134 |
|
pm-mvs1 | | | 86.61 202 | 85.54 200 | 89.82 202 | 91.44 231 | 80.18 168 | 95.28 73 | 94.85 187 | 83.84 159 | 81.66 252 | 92.62 175 | 72.45 190 | 96.48 245 | 79.67 201 | 78.06 304 | 92.82 266 |
|
Baseline_NR-MVSNet | | | 87.07 192 | 86.63 175 | 88.40 256 | 91.44 231 | 77.87 248 | 94.23 148 | 92.57 244 | 84.12 155 | 85.74 158 | 92.08 196 | 77.25 115 | 96.04 261 | 82.29 155 | 79.94 296 | 91.30 298 |
|
IterMVS | | | 84.88 244 | 83.98 236 | 87.60 272 | 91.44 231 | 76.03 275 | 90.18 276 | 92.41 246 | 83.24 179 | 81.06 260 | 90.42 257 | 66.60 265 | 94.28 309 | 79.46 203 | 80.98 282 | 92.48 273 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DWT-MVSNet_test | | | 84.95 242 | 83.68 243 | 88.77 238 | 91.43 234 | 73.75 289 | 91.74 256 | 90.98 290 | 80.66 239 | 83.84 219 | 87.36 299 | 62.44 288 | 97.11 205 | 78.84 212 | 85.81 223 | 95.46 139 |
|
MS-PatchMatch | | | 85.05 238 | 84.16 232 | 87.73 270 | 91.42 235 | 78.51 230 | 91.25 267 | 93.53 227 | 77.50 269 | 80.15 270 | 91.58 214 | 61.99 291 | 95.51 282 | 75.69 240 | 94.35 110 | 89.16 325 |
|
v16 | | | 84.96 241 | 83.74 240 | 88.62 244 | 91.40 236 | 79.48 188 | 93.83 179 | 94.04 211 | 83.03 187 | 76.54 296 | 86.59 305 | 76.11 129 | 95.42 288 | 80.33 187 | 71.80 321 | 90.95 306 |
|
tpm2 | | | 84.08 258 | 82.94 258 | 87.48 277 | 91.39 237 | 71.27 309 | 89.23 291 | 90.37 300 | 71.95 317 | 84.64 199 | 89.33 271 | 67.30 259 | 96.55 242 | 75.17 245 | 87.09 217 | 94.63 174 |
|
v1neww | | | 87.98 148 | 87.25 144 | 90.16 180 | 91.38 238 | 79.41 194 | 94.37 139 | 95.28 160 | 84.48 146 | 85.77 154 | 91.53 218 | 76.12 126 | 97.45 162 | 84.45 125 | 81.89 263 | 93.61 237 |
|
v7new | | | 87.98 148 | 87.25 144 | 90.16 180 | 91.38 238 | 79.41 194 | 94.37 139 | 95.28 160 | 84.48 146 | 85.77 154 | 91.53 218 | 76.12 126 | 97.45 162 | 84.45 125 | 81.89 263 | 93.61 237 |
|
v8 | | | 87.50 179 | 86.71 164 | 89.89 200 | 91.37 240 | 79.40 198 | 94.50 124 | 95.38 155 | 84.81 139 | 83.60 226 | 91.33 228 | 76.05 130 | 97.42 173 | 82.84 145 | 80.51 290 | 92.84 264 |
|
v18 | | | 84.97 240 | 83.76 238 | 88.60 246 | 91.36 241 | 79.41 194 | 93.82 181 | 94.04 211 | 83.00 190 | 76.61 295 | 86.60 304 | 76.19 124 | 95.43 287 | 80.39 184 | 71.79 322 | 90.96 304 |
|
v17 | | | 84.93 243 | 83.70 242 | 88.62 244 | 91.36 241 | 79.48 188 | 93.83 179 | 94.03 213 | 83.04 186 | 76.51 297 | 86.57 306 | 76.05 130 | 95.42 288 | 80.31 189 | 71.65 323 | 90.96 304 |
|
v6 | | | 87.98 148 | 87.25 144 | 90.16 180 | 91.36 241 | 79.39 199 | 94.37 139 | 95.27 163 | 84.48 146 | 85.78 153 | 91.51 220 | 76.15 125 | 97.46 160 | 84.46 124 | 81.88 265 | 93.62 236 |
|
ADS-MVSNet2 | | | 81.66 280 | 79.71 286 | 87.50 275 | 91.35 244 | 74.19 284 | 83.33 335 | 88.48 330 | 72.90 310 | 82.24 242 | 85.77 316 | 64.98 279 | 93.20 321 | 64.57 317 | 83.74 241 | 95.12 146 |
|
ADS-MVSNet | | | 81.56 282 | 79.78 284 | 86.90 290 | 91.35 244 | 71.82 306 | 83.33 335 | 89.16 325 | 72.90 310 | 82.24 242 | 85.77 316 | 64.98 279 | 93.76 313 | 64.57 317 | 83.74 241 | 95.12 146 |
|
V9 | | | 84.77 248 | 83.50 249 | 88.58 247 | 91.33 246 | 79.46 190 | 93.75 186 | 94.00 217 | 83.07 182 | 76.07 304 | 86.43 307 | 75.97 135 | 95.37 291 | 79.91 196 | 70.93 329 | 90.91 308 |
|
GA-MVS | | | 86.61 202 | 85.27 209 | 90.66 153 | 91.33 246 | 78.71 219 | 90.40 272 | 93.81 225 | 85.34 128 | 85.12 187 | 89.57 269 | 61.25 297 | 97.11 205 | 80.99 174 | 89.59 176 | 96.15 111 |
|
v12 | | | 84.74 249 | 83.46 250 | 88.58 247 | 91.32 248 | 79.50 185 | 93.75 186 | 94.01 214 | 83.06 183 | 75.98 306 | 86.41 311 | 75.82 141 | 95.36 294 | 79.87 197 | 70.89 330 | 90.89 310 |
|
v11 | | | 84.67 254 | 83.41 253 | 88.44 255 | 91.32 248 | 79.13 213 | 93.69 195 | 93.99 219 | 82.81 196 | 76.20 300 | 86.24 314 | 75.48 146 | 95.35 295 | 79.53 202 | 71.48 325 | 90.85 312 |
|
V14 | | | 84.79 246 | 83.52 248 | 88.57 250 | 91.32 248 | 79.43 193 | 93.72 190 | 94.01 214 | 83.06 183 | 76.22 299 | 86.43 307 | 76.01 134 | 95.37 291 | 79.96 193 | 70.99 327 | 90.91 308 |
|
v13 | | | 84.72 251 | 83.44 252 | 88.58 247 | 91.31 251 | 79.52 184 | 93.77 184 | 94.00 217 | 83.03 187 | 75.85 307 | 86.38 312 | 75.84 140 | 95.35 295 | 79.83 198 | 70.95 328 | 90.87 311 |
|
v15 | | | 84.79 246 | 83.53 247 | 88.57 250 | 91.30 252 | 79.41 194 | 93.70 192 | 94.01 214 | 83.06 183 | 76.27 298 | 86.42 310 | 76.03 133 | 95.38 290 | 80.01 191 | 71.00 326 | 90.92 307 |
|
v1141 | | | 87.84 154 | 87.09 148 | 90.11 191 | 91.23 253 | 79.25 208 | 94.08 163 | 95.24 165 | 84.44 150 | 85.69 161 | 91.31 231 | 75.91 138 | 97.44 169 | 84.17 130 | 81.74 270 | 93.63 235 |
|
divwei89l23v2f112 | | | 87.84 154 | 87.09 148 | 90.10 193 | 91.23 253 | 79.24 210 | 94.09 161 | 95.24 165 | 84.44 150 | 85.70 159 | 91.31 231 | 75.91 138 | 97.44 169 | 84.17 130 | 81.73 271 | 93.64 233 |
|
v1 | | | 87.85 153 | 87.10 147 | 90.11 191 | 91.21 255 | 79.24 210 | 94.09 161 | 95.24 165 | 84.44 150 | 85.70 159 | 91.31 231 | 75.96 136 | 97.45 162 | 84.18 129 | 81.73 271 | 93.64 233 |
|
v7 | | | 87.75 159 | 86.96 154 | 90.12 186 | 91.20 256 | 79.50 185 | 94.28 145 | 95.46 145 | 83.45 172 | 85.75 156 | 91.56 217 | 75.13 150 | 97.43 171 | 83.60 137 | 82.18 258 | 93.42 246 |
|
XVG-ACMP-BASELINE | | | 86.00 214 | 84.84 219 | 89.45 220 | 91.20 256 | 78.00 243 | 91.70 258 | 95.55 136 | 85.05 135 | 82.97 234 | 92.25 190 | 54.49 324 | 97.48 158 | 82.93 143 | 87.45 212 | 92.89 262 |
|
v10 | | | 87.25 186 | 86.38 179 | 89.85 201 | 91.19 258 | 79.50 185 | 94.48 125 | 95.45 149 | 83.79 162 | 83.62 225 | 91.19 236 | 75.13 150 | 97.42 173 | 81.94 161 | 80.60 285 | 92.63 270 |
|
FMVSNet5 | | | 81.52 283 | 79.60 287 | 87.27 279 | 91.17 259 | 77.95 244 | 91.49 262 | 92.26 249 | 76.87 275 | 76.16 301 | 87.91 294 | 51.67 329 | 92.34 326 | 67.74 298 | 81.16 274 | 91.52 293 |
|
USDC | | | 82.76 269 | 81.26 270 | 87.26 280 | 91.17 259 | 74.55 281 | 89.27 289 | 93.39 230 | 78.26 265 | 75.30 309 | 92.08 196 | 54.43 325 | 96.63 236 | 71.64 265 | 85.79 225 | 90.61 314 |
|
CostFormer | | | 85.77 222 | 84.94 215 | 88.26 260 | 91.16 261 | 72.58 303 | 89.47 287 | 91.04 289 | 76.26 281 | 86.45 142 | 89.97 263 | 70.74 208 | 96.86 224 | 82.35 153 | 87.07 218 | 95.34 144 |
|
tpm cat1 | | | 81.96 275 | 80.27 278 | 87.01 287 | 91.09 262 | 71.02 313 | 87.38 311 | 91.53 274 | 66.25 335 | 80.17 269 | 86.35 313 | 68.22 258 | 96.15 259 | 69.16 287 | 82.29 256 | 93.86 215 |
|
tpmvs | | | 83.35 267 | 82.07 264 | 87.20 285 | 91.07 263 | 71.00 314 | 88.31 303 | 91.70 265 | 78.91 253 | 80.49 267 | 87.18 302 | 69.30 229 | 97.08 207 | 68.12 297 | 83.56 245 | 93.51 244 |
|
v1144 | | | 87.61 175 | 86.79 161 | 90.06 194 | 91.01 264 | 79.34 202 | 93.95 175 | 95.42 154 | 83.36 176 | 85.66 163 | 91.31 231 | 74.98 154 | 97.42 173 | 83.37 138 | 82.06 259 | 93.42 246 |
|
v2v482 | | | 87.84 154 | 87.06 151 | 90.17 179 | 90.99 265 | 79.23 212 | 94.00 173 | 95.13 172 | 84.87 137 | 85.53 167 | 92.07 198 | 74.45 157 | 97.45 162 | 84.71 121 | 81.75 269 | 93.85 216 |
|
SixPastTwentyTwo | | | 83.91 260 | 82.90 259 | 86.92 289 | 90.99 265 | 70.67 316 | 93.48 200 | 91.99 258 | 85.54 124 | 77.62 289 | 92.11 194 | 60.59 302 | 96.87 223 | 76.05 239 | 77.75 305 | 93.20 251 |
|
test-LLR | | | 85.87 216 | 85.41 205 | 87.25 281 | 90.95 267 | 71.67 307 | 89.55 283 | 89.88 312 | 83.41 173 | 84.54 202 | 87.95 292 | 67.25 260 | 95.11 300 | 81.82 163 | 93.37 126 | 94.97 151 |
|
test-mter | | | 84.54 255 | 83.64 245 | 87.25 281 | 90.95 267 | 71.67 307 | 89.55 283 | 89.88 312 | 79.17 251 | 84.54 202 | 87.95 292 | 55.56 319 | 95.11 300 | 81.82 163 | 93.37 126 | 94.97 151 |
|
v148 | | | 87.04 193 | 86.32 182 | 89.21 231 | 90.94 269 | 77.26 264 | 93.71 191 | 94.43 199 | 84.84 138 | 84.36 210 | 90.80 246 | 76.04 132 | 97.05 210 | 82.12 157 | 79.60 299 | 93.31 248 |
|
mvs_tets | | | 88.06 147 | 87.28 142 | 90.38 173 | 90.94 269 | 79.88 178 | 95.22 76 | 95.66 129 | 85.10 134 | 84.21 215 | 93.94 130 | 63.53 285 | 97.40 180 | 88.50 78 | 88.40 202 | 93.87 213 |
|
MVP-Stereo | | | 85.97 215 | 84.86 218 | 89.32 228 | 90.92 271 | 82.19 124 | 92.11 250 | 94.19 206 | 78.76 258 | 78.77 281 | 91.63 212 | 68.38 256 | 96.56 240 | 75.01 248 | 93.95 112 | 89.20 324 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
Patchmatch-test | | | 81.37 285 | 79.30 289 | 87.58 273 | 90.92 271 | 74.16 285 | 80.99 341 | 87.68 336 | 70.52 325 | 76.63 294 | 88.81 277 | 71.21 200 | 92.76 325 | 60.01 331 | 86.93 219 | 95.83 128 |
|
jajsoiax | | | 88.24 140 | 87.50 135 | 90.48 166 | 90.89 273 | 80.14 170 | 95.31 63 | 95.65 131 | 84.97 136 | 84.24 214 | 94.02 125 | 65.31 277 | 97.42 173 | 88.56 77 | 88.52 197 | 93.89 210 |
|
tpmrst | | | 85.35 232 | 84.99 211 | 86.43 295 | 90.88 274 | 67.88 327 | 88.71 297 | 91.43 277 | 80.13 243 | 86.08 150 | 88.80 278 | 73.05 179 | 96.02 263 | 82.48 150 | 83.40 249 | 95.40 141 |
|
gg-mvs-nofinetune | | | 81.77 278 | 79.37 288 | 88.99 236 | 90.85 275 | 77.73 254 | 86.29 316 | 79.63 353 | 74.88 295 | 83.19 233 | 69.05 346 | 60.34 303 | 96.11 260 | 75.46 242 | 94.64 101 | 93.11 256 |
|
OurMVSNet-221017-0 | | | 85.35 232 | 84.64 224 | 87.49 276 | 90.77 276 | 72.59 302 | 94.01 172 | 94.40 200 | 84.72 142 | 79.62 277 | 93.17 153 | 61.91 292 | 96.72 232 | 81.99 160 | 81.16 274 | 93.16 253 |
|
v1192 | | | 87.25 186 | 86.33 181 | 90.00 198 | 90.76 277 | 79.04 214 | 93.80 182 | 95.48 144 | 82.57 201 | 85.48 171 | 91.18 237 | 73.38 177 | 97.42 173 | 82.30 154 | 82.06 259 | 93.53 241 |
|
test_djsdf | | | 89.03 123 | 88.64 111 | 90.21 178 | 90.74 278 | 79.28 206 | 95.96 41 | 95.90 111 | 84.66 143 | 85.33 185 | 92.94 165 | 74.02 166 | 97.30 187 | 89.64 69 | 88.53 196 | 94.05 204 |
|
v7n | | | 86.81 196 | 85.76 198 | 89.95 199 | 90.72 279 | 79.25 208 | 95.07 84 | 95.92 108 | 84.45 149 | 82.29 240 | 90.86 245 | 72.60 186 | 97.53 155 | 79.42 207 | 80.52 289 | 93.08 258 |
|
PVSNet_0 | | 73.20 20 | 77.22 304 | 74.83 307 | 84.37 311 | 90.70 280 | 71.10 312 | 83.09 337 | 89.67 315 | 72.81 312 | 73.93 316 | 83.13 327 | 60.79 301 | 93.70 314 | 68.54 290 | 50.84 349 | 88.30 336 |
|
DI_MVS_plusplus_test | | | 88.15 143 | 86.82 158 | 92.14 106 | 90.67 281 | 81.07 148 | 93.01 221 | 94.59 195 | 83.83 161 | 77.78 286 | 90.63 249 | 68.51 246 | 98.16 110 | 88.02 85 | 94.37 109 | 97.17 84 |
|
v144192 | | | 87.19 190 | 86.35 180 | 89.74 206 | 90.64 282 | 78.24 239 | 93.92 176 | 95.43 152 | 81.93 213 | 85.51 169 | 91.05 243 | 74.21 162 | 97.45 162 | 82.86 144 | 81.56 273 | 93.53 241 |
|
V42 | | | 87.68 161 | 86.86 156 | 90.15 184 | 90.58 283 | 80.14 170 | 94.24 147 | 95.28 160 | 83.66 164 | 85.67 162 | 91.33 228 | 74.73 155 | 97.41 178 | 84.43 127 | 81.83 267 | 92.89 262 |
|
CR-MVSNet | | | 85.35 232 | 83.76 238 | 90.12 186 | 90.58 283 | 79.34 202 | 85.24 324 | 91.96 261 | 78.27 264 | 85.55 165 | 87.87 295 | 71.03 203 | 95.61 277 | 73.96 256 | 89.36 179 | 95.40 141 |
|
RPMNet | | | 83.18 268 | 80.87 274 | 90.12 186 | 90.58 283 | 79.34 202 | 85.24 324 | 90.78 296 | 71.44 319 | 85.55 165 | 82.97 328 | 70.87 205 | 95.61 277 | 61.01 327 | 89.36 179 | 95.40 141 |
|
test_normal | | | 88.13 144 | 86.78 162 | 92.18 104 | 90.55 286 | 81.19 146 | 92.74 229 | 94.64 194 | 83.84 159 | 77.49 290 | 90.51 255 | 68.49 247 | 98.16 110 | 88.22 80 | 94.55 103 | 97.21 82 |
|
v1921920 | | | 86.97 194 | 86.06 191 | 89.69 211 | 90.53 287 | 78.11 242 | 93.80 182 | 95.43 152 | 81.90 215 | 85.33 185 | 91.05 243 | 72.66 184 | 97.41 178 | 82.05 159 | 81.80 268 | 93.53 241 |
|
v1240 | | | 86.78 198 | 85.85 196 | 89.56 214 | 90.45 288 | 77.79 250 | 93.61 196 | 95.37 157 | 81.65 226 | 85.43 176 | 91.15 239 | 71.50 198 | 97.43 171 | 81.47 168 | 82.05 261 | 93.47 245 |
|
tpm | | | 84.73 250 | 84.02 234 | 86.87 292 | 90.33 289 | 68.90 324 | 89.06 293 | 89.94 310 | 80.85 238 | 85.75 156 | 89.86 265 | 68.54 245 | 95.97 265 | 77.76 221 | 84.05 239 | 95.75 132 |
|
EG-PatchMatch MVS | | | 82.37 274 | 80.34 277 | 88.46 254 | 90.27 290 | 79.35 201 | 92.80 228 | 94.33 203 | 77.14 274 | 73.26 320 | 90.18 260 | 47.47 338 | 96.72 232 | 70.25 273 | 87.32 215 | 89.30 322 |
|
v748 | | | 86.27 208 | 85.28 208 | 89.25 230 | 90.26 291 | 77.58 263 | 94.89 96 | 95.50 143 | 84.28 153 | 81.41 255 | 90.46 256 | 72.57 187 | 97.32 186 | 79.81 199 | 78.36 303 | 92.84 264 |
|
EPNet_dtu | | | 86.49 206 | 85.94 195 | 88.14 264 | 90.24 292 | 72.82 296 | 94.11 159 | 92.20 250 | 86.66 105 | 79.42 278 | 92.36 183 | 73.52 172 | 95.81 273 | 71.26 267 | 93.66 117 | 95.80 130 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPMVS | | | 83.90 261 | 82.70 262 | 87.51 274 | 90.23 293 | 72.67 299 | 88.62 299 | 81.96 349 | 81.37 234 | 85.01 189 | 88.34 286 | 66.31 269 | 94.45 306 | 75.30 244 | 87.12 216 | 95.43 140 |
|
EPNet | | | 91.79 61 | 91.02 69 | 94.10 46 | 90.10 294 | 85.25 55 | 96.03 36 | 92.05 255 | 92.83 1 | 87.39 126 | 95.78 76 | 79.39 97 | 99.01 56 | 88.13 83 | 97.48 60 | 98.05 48 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PatchT | | | 82.68 271 | 81.27 269 | 86.89 291 | 90.09 295 | 70.94 315 | 84.06 331 | 90.15 304 | 74.91 293 | 85.63 164 | 83.57 324 | 69.37 225 | 94.87 305 | 65.19 313 | 88.50 198 | 94.84 164 |
|
Patchmtry | | | 82.71 270 | 80.93 273 | 88.06 265 | 90.05 296 | 76.37 272 | 84.74 326 | 91.96 261 | 72.28 315 | 81.32 257 | 87.87 295 | 71.03 203 | 95.50 284 | 68.97 288 | 80.15 292 | 92.32 280 |
|
pmmvs4 | | | 85.43 230 | 83.86 237 | 90.16 180 | 90.02 297 | 82.97 107 | 90.27 273 | 92.67 242 | 75.93 284 | 80.73 262 | 91.74 208 | 71.05 202 | 95.73 276 | 78.85 211 | 83.46 247 | 91.78 287 |
|
TESTMET0.1,1 | | | 83.74 263 | 82.85 260 | 86.42 296 | 89.96 298 | 71.21 311 | 89.55 283 | 87.88 333 | 77.41 270 | 83.37 231 | 87.31 300 | 56.71 316 | 93.65 315 | 80.62 180 | 92.85 137 | 94.40 191 |
|
dp | | | 81.47 284 | 80.23 279 | 85.17 306 | 89.92 299 | 65.49 334 | 86.74 313 | 90.10 306 | 76.30 280 | 81.10 258 | 87.12 303 | 62.81 286 | 95.92 267 | 68.13 296 | 79.88 297 | 94.09 202 |
|
K. test v3 | | | 81.59 281 | 80.15 281 | 85.91 300 | 89.89 300 | 69.42 323 | 92.57 235 | 87.71 335 | 85.56 123 | 73.44 318 | 89.71 267 | 55.58 318 | 95.52 281 | 77.17 228 | 69.76 333 | 92.78 267 |
|
MDA-MVSNet-bldmvs | | | 78.85 302 | 76.31 303 | 86.46 294 | 89.76 301 | 73.88 288 | 88.79 296 | 90.42 299 | 79.16 252 | 59.18 343 | 88.33 287 | 60.20 304 | 94.04 311 | 62.00 324 | 68.96 335 | 91.48 295 |
|
GG-mvs-BLEND | | | | | 87.94 268 | 89.73 302 | 77.91 245 | 87.80 306 | 78.23 355 | | 80.58 265 | 83.86 322 | 59.88 307 | 95.33 297 | 71.20 268 | 92.22 141 | 90.60 316 |
|
gm-plane-assit | | | | | | 89.60 303 | 68.00 326 | | | 77.28 273 | | 88.99 274 | | 97.57 152 | 79.44 205 | | |
|
v52 | | | 86.50 204 | 85.53 203 | 89.39 222 | 89.17 304 | 78.99 215 | 94.72 111 | 95.54 138 | 83.59 165 | 82.10 244 | 90.60 251 | 71.59 195 | 97.45 162 | 82.52 148 | 79.99 295 | 91.73 289 |
|
anonymousdsp | | | 87.84 154 | 87.09 148 | 90.12 186 | 89.13 305 | 80.54 164 | 94.67 116 | 95.55 136 | 82.05 208 | 83.82 220 | 92.12 192 | 71.47 199 | 97.15 201 | 87.15 97 | 87.80 209 | 92.67 268 |
|
V4 | | | 86.50 204 | 85.54 200 | 89.39 222 | 89.13 305 | 78.99 215 | 94.73 108 | 95.54 138 | 83.59 165 | 82.10 244 | 90.61 250 | 71.60 194 | 97.45 162 | 82.52 148 | 80.01 294 | 91.74 288 |
|
N_pmnet | | | 68.89 318 | 68.44 319 | 70.23 334 | 89.07 307 | 28.79 363 | 88.06 304 | 19.50 364 | 69.47 328 | 71.86 326 | 84.93 319 | 61.24 298 | 91.75 331 | 54.70 335 | 77.15 309 | 90.15 318 |
|
pmmvs5 | | | 84.21 257 | 82.84 261 | 88.34 258 | 88.95 308 | 76.94 267 | 92.41 239 | 91.91 263 | 75.63 286 | 80.28 268 | 91.18 237 | 64.59 281 | 95.57 279 | 77.09 230 | 83.47 246 | 92.53 272 |
|
PMMVS | | | 85.71 228 | 84.96 214 | 87.95 267 | 88.90 309 | 77.09 265 | 88.68 298 | 90.06 307 | 72.32 314 | 86.47 139 | 90.76 247 | 72.15 191 | 94.40 307 | 81.78 165 | 93.49 121 | 92.36 278 |
|
JIA-IIPM | | | 81.04 288 | 78.98 294 | 87.25 281 | 88.64 310 | 73.48 291 | 81.75 340 | 89.61 316 | 73.19 306 | 82.05 246 | 73.71 343 | 66.07 275 | 95.87 270 | 71.18 270 | 84.60 234 | 92.41 276 |
|
Gipuma | | | 57.99 326 | 54.91 327 | 67.24 339 | 88.51 311 | 65.59 333 | 52.21 357 | 90.33 302 | 43.58 352 | 42.84 351 | 51.18 354 | 20.29 359 | 85.07 349 | 34.77 354 | 70.45 331 | 51.05 355 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
EU-MVSNet | | | 81.32 286 | 80.95 272 | 82.42 319 | 88.50 312 | 63.67 336 | 93.32 204 | 91.33 278 | 64.02 340 | 80.57 266 | 92.83 168 | 61.21 299 | 92.27 327 | 76.34 235 | 80.38 291 | 91.32 297 |
|
our_test_3 | | | 81.93 276 | 80.46 276 | 86.33 297 | 88.46 313 | 73.48 291 | 88.46 301 | 91.11 281 | 76.46 276 | 76.69 293 | 88.25 288 | 66.89 263 | 94.36 308 | 68.75 289 | 79.08 301 | 91.14 301 |
|
ppachtmachnet_test | | | 81.84 277 | 80.07 282 | 87.15 286 | 88.46 313 | 74.43 282 | 89.04 294 | 92.16 251 | 75.33 288 | 77.75 287 | 88.99 274 | 66.20 271 | 95.37 291 | 65.12 315 | 77.60 306 | 91.65 291 |
|
lessismore_v0 | | | | | 86.04 298 | 88.46 313 | 68.78 325 | | 80.59 351 | | 73.01 321 | 90.11 261 | 55.39 320 | 96.43 249 | 75.06 247 | 65.06 339 | 92.90 261 |
|
test0.0.03 1 | | | 82.41 273 | 81.69 266 | 84.59 309 | 88.23 316 | 72.89 295 | 90.24 274 | 87.83 334 | 83.41 173 | 79.86 274 | 89.78 266 | 67.25 260 | 88.99 337 | 65.18 314 | 83.42 248 | 91.90 286 |
|
MDA-MVSNet_test_wron | | | 79.21 301 | 77.19 301 | 85.29 304 | 88.22 317 | 72.77 298 | 85.87 319 | 90.06 307 | 74.34 298 | 62.62 342 | 87.56 298 | 66.14 273 | 91.99 329 | 66.90 303 | 73.01 316 | 91.10 303 |
|
YYNet1 | | | 79.22 300 | 77.20 300 | 85.28 305 | 88.20 318 | 72.66 300 | 85.87 319 | 90.05 309 | 74.33 299 | 62.70 341 | 87.61 297 | 66.09 274 | 92.03 328 | 66.94 300 | 72.97 317 | 91.15 300 |
|
Test4 | | | 85.75 223 | 83.72 241 | 91.83 119 | 88.08 319 | 81.03 150 | 92.48 237 | 95.54 138 | 83.38 175 | 73.40 319 | 88.57 282 | 50.99 331 | 97.37 184 | 86.61 107 | 94.47 106 | 97.09 88 |
|
pmmvs6 | | | 83.42 264 | 81.60 267 | 88.87 237 | 88.01 320 | 77.87 248 | 94.96 91 | 94.24 205 | 74.67 296 | 78.80 280 | 91.09 242 | 60.17 305 | 96.49 244 | 77.06 231 | 75.40 313 | 92.23 282 |
|
testgi | | | 80.94 291 | 80.20 280 | 83.18 315 | 87.96 321 | 66.29 331 | 91.28 265 | 90.70 298 | 83.70 163 | 78.12 283 | 92.84 167 | 51.37 330 | 90.82 334 | 63.34 320 | 82.46 255 | 92.43 275 |
|
LP | | | 75.51 308 | 72.15 312 | 85.61 302 | 87.86 322 | 73.93 287 | 80.20 343 | 88.43 331 | 67.39 331 | 70.05 328 | 80.56 336 | 58.18 313 | 93.18 322 | 46.28 347 | 70.36 332 | 89.71 321 |
|
Anonymous20231206 | | | 81.03 289 | 79.77 285 | 84.82 308 | 87.85 323 | 70.26 319 | 91.42 263 | 92.08 254 | 73.67 302 | 77.75 287 | 89.25 272 | 62.43 289 | 93.08 323 | 61.50 326 | 82.00 262 | 91.12 302 |
|
OpenMVS_ROB | | 74.94 19 | 79.51 298 | 77.03 302 | 86.93 288 | 87.00 324 | 76.23 274 | 92.33 242 | 90.74 297 | 68.93 329 | 74.52 313 | 88.23 289 | 49.58 333 | 96.62 237 | 57.64 333 | 84.29 236 | 87.94 337 |
|
LF4IMVS | | | 80.37 293 | 79.07 293 | 84.27 313 | 86.64 325 | 69.87 322 | 89.39 288 | 91.05 288 | 76.38 278 | 74.97 311 | 90.00 262 | 47.85 337 | 94.25 310 | 74.55 252 | 80.82 284 | 88.69 330 |
|
MIMVSNet1 | | | 79.38 299 | 77.28 299 | 85.69 301 | 86.35 326 | 73.67 290 | 91.61 261 | 92.75 240 | 78.11 268 | 72.64 323 | 88.12 290 | 48.16 336 | 91.97 330 | 60.32 328 | 77.49 307 | 91.43 296 |
|
test20.03 | | | 79.95 295 | 79.08 292 | 82.55 318 | 85.79 327 | 67.74 328 | 91.09 270 | 91.08 286 | 81.23 235 | 74.48 314 | 89.96 264 | 61.63 293 | 90.15 335 | 60.08 329 | 76.38 310 | 89.76 319 |
|
Patchmatch-RL test | | | 81.67 279 | 79.96 283 | 86.81 293 | 85.42 328 | 71.23 310 | 82.17 339 | 87.50 338 | 78.47 261 | 77.19 292 | 82.50 329 | 70.81 207 | 93.48 317 | 82.66 147 | 72.89 318 | 95.71 133 |
|
UnsupCasMVSNet_eth | | | 80.07 294 | 78.27 296 | 85.46 303 | 85.24 329 | 72.63 301 | 88.45 302 | 94.87 186 | 82.99 191 | 71.64 327 | 88.07 291 | 56.34 317 | 91.75 331 | 73.48 259 | 63.36 344 | 92.01 285 |
|
testing_2 | | | 83.40 266 | 81.02 271 | 90.56 156 | 85.06 330 | 80.51 165 | 91.37 264 | 95.57 134 | 82.92 193 | 67.06 335 | 85.54 318 | 49.47 334 | 97.24 195 | 86.74 102 | 85.44 226 | 93.93 208 |
|
pmmvs-eth3d | | | 80.97 290 | 78.72 295 | 87.74 269 | 84.99 331 | 79.97 177 | 90.11 277 | 91.65 267 | 75.36 287 | 73.51 317 | 86.03 315 | 59.45 308 | 93.96 312 | 75.17 245 | 72.21 319 | 89.29 323 |
|
testpf | | | 71.41 316 | 72.11 313 | 69.30 336 | 84.53 332 | 59.79 340 | 62.74 354 | 83.14 346 | 71.11 322 | 68.83 332 | 81.57 334 | 46.70 339 | 84.83 350 | 74.51 253 | 75.86 312 | 63.30 350 |
|
CMPMVS | | 59.16 21 | 80.52 292 | 79.20 290 | 84.48 310 | 83.98 333 | 67.63 329 | 89.95 280 | 93.84 224 | 64.79 339 | 66.81 336 | 91.14 240 | 57.93 314 | 95.17 298 | 76.25 236 | 88.10 204 | 90.65 313 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
UnsupCasMVSNet_bld | | | 76.23 307 | 73.27 309 | 85.09 307 | 83.79 334 | 72.92 294 | 85.65 323 | 93.47 229 | 71.52 318 | 68.84 331 | 79.08 339 | 49.77 332 | 93.21 320 | 66.81 304 | 60.52 346 | 89.13 327 |
|
PM-MVS | | | 78.11 303 | 76.12 305 | 84.09 314 | 83.54 335 | 70.08 320 | 88.97 295 | 85.27 343 | 79.93 245 | 74.73 312 | 86.43 307 | 34.70 349 | 93.48 317 | 79.43 206 | 72.06 320 | 88.72 329 |
|
DSMNet-mixed | | | 76.94 305 | 76.29 304 | 78.89 322 | 83.10 336 | 56.11 348 | 87.78 307 | 79.77 352 | 60.65 344 | 75.64 308 | 88.71 279 | 61.56 294 | 88.34 339 | 60.07 330 | 89.29 181 | 92.21 283 |
|
Anonymous20231211 | | | 72.97 312 | 69.63 317 | 83.00 317 | 83.05 337 | 66.91 330 | 92.69 230 | 89.45 318 | 61.06 343 | 67.50 334 | 83.46 325 | 34.34 350 | 93.61 316 | 51.11 338 | 63.97 342 | 88.48 334 |
|
new_pmnet | | | 72.15 314 | 70.13 315 | 78.20 323 | 82.95 338 | 65.68 332 | 83.91 332 | 82.40 348 | 62.94 342 | 64.47 339 | 79.82 338 | 42.85 343 | 86.26 345 | 57.41 334 | 74.44 315 | 82.65 343 |
|
new-patchmatchnet | | | 76.41 306 | 75.17 306 | 80.13 321 | 82.65 339 | 59.61 341 | 87.66 309 | 91.08 286 | 78.23 266 | 69.85 329 | 83.22 326 | 54.76 322 | 91.63 333 | 64.14 319 | 64.89 340 | 89.16 325 |
|
testus | | | 74.41 310 | 73.35 308 | 77.59 327 | 82.49 340 | 57.08 344 | 86.02 317 | 90.21 303 | 72.28 315 | 72.89 322 | 84.32 321 | 37.08 347 | 86.96 343 | 52.24 337 | 82.65 253 | 88.73 328 |
|
test2356 | | | 74.50 309 | 73.27 309 | 78.20 323 | 80.81 341 | 59.84 339 | 83.76 334 | 88.33 332 | 71.43 320 | 72.37 324 | 81.84 332 | 45.60 341 | 86.26 345 | 50.97 339 | 84.32 235 | 88.50 332 |
|
ambc | | | | | 83.06 316 | 79.99 342 | 63.51 337 | 77.47 347 | 92.86 236 | | 74.34 315 | 84.45 320 | 28.74 351 | 95.06 302 | 73.06 261 | 68.89 336 | 90.61 314 |
|
1111 | | | 70.54 317 | 69.71 316 | 73.04 331 | 79.30 343 | 44.83 356 | 84.23 329 | 88.96 327 | 67.33 332 | 65.42 337 | 82.28 330 | 41.11 345 | 88.11 340 | 47.12 345 | 71.60 324 | 86.19 339 |
|
.test1245 | | | 57.63 327 | 61.79 324 | 45.14 345 | 79.30 343 | 44.83 356 | 84.23 329 | 88.96 327 | 67.33 332 | 65.42 337 | 82.28 330 | 41.11 345 | 88.11 340 | 47.12 345 | 0.39 360 | 2.46 361 |
|
TDRefinement | | | 79.81 296 | 77.34 298 | 87.22 284 | 79.24 345 | 75.48 279 | 93.12 215 | 92.03 256 | 76.45 277 | 75.01 310 | 91.58 214 | 49.19 335 | 96.44 248 | 70.22 275 | 69.18 334 | 89.75 320 |
|
test1235678 | | | 72.22 313 | 70.31 314 | 77.93 326 | 78.04 346 | 58.04 343 | 85.76 321 | 89.80 314 | 70.15 327 | 63.43 340 | 80.20 337 | 42.24 344 | 87.24 342 | 48.68 343 | 74.50 314 | 88.50 332 |
|
pmmvs3 | | | 71.81 315 | 68.71 318 | 81.11 320 | 75.86 347 | 70.42 318 | 86.74 313 | 83.66 345 | 58.95 345 | 68.64 333 | 80.89 335 | 36.93 348 | 89.52 336 | 63.10 322 | 63.59 343 | 83.39 341 |
|
DeepMVS_CX | | | | | 56.31 343 | 74.23 348 | 51.81 352 | | 56.67 362 | 44.85 350 | 48.54 349 | 75.16 341 | 27.87 353 | 58.74 360 | 40.92 351 | 52.22 348 | 58.39 354 |
|
test12356 | | | 64.99 321 | 63.78 320 | 68.61 338 | 72.69 349 | 39.14 359 | 78.46 345 | 87.61 337 | 64.91 338 | 55.77 344 | 77.48 340 | 28.10 352 | 85.59 347 | 44.69 348 | 64.35 341 | 81.12 345 |
|
FPMVS | | | 64.63 322 | 62.55 322 | 70.88 333 | 70.80 350 | 56.71 345 | 84.42 328 | 84.42 344 | 51.78 348 | 49.57 347 | 81.61 333 | 23.49 356 | 81.48 352 | 40.61 352 | 76.25 311 | 74.46 349 |
|
PMMVS2 | | | 59.60 324 | 56.40 326 | 69.21 337 | 68.83 351 | 46.58 354 | 73.02 352 | 77.48 356 | 55.07 347 | 49.21 348 | 72.95 345 | 17.43 361 | 80.04 353 | 49.32 342 | 44.33 350 | 80.99 346 |
|
testmv | | | 65.49 320 | 62.66 321 | 73.96 330 | 68.78 352 | 53.14 351 | 84.70 327 | 88.56 329 | 65.94 337 | 52.35 346 | 74.65 342 | 25.02 355 | 85.14 348 | 43.54 349 | 60.40 347 | 83.60 340 |
|
PNet_i23d | | | 50.48 330 | 47.18 330 | 60.36 341 | 68.59 353 | 44.56 358 | 72.75 353 | 72.61 357 | 43.92 351 | 33.91 354 | 60.19 352 | 6.16 363 | 73.52 356 | 38.50 353 | 28.04 353 | 63.01 351 |
|
wuyk23d | | | 21.27 337 | 20.48 338 | 23.63 349 | 68.59 353 | 36.41 361 | 49.57 358 | 6.85 365 | 9.37 358 | 7.89 360 | 4.46 363 | 4.03 366 | 31.37 361 | 17.47 359 | 16.07 359 | 3.12 359 |
|
no-one | | | 61.56 323 | 56.58 325 | 76.49 329 | 67.80 355 | 62.76 338 | 78.13 346 | 86.11 339 | 63.16 341 | 43.24 350 | 64.70 349 | 26.12 354 | 88.95 338 | 50.84 340 | 29.15 352 | 77.77 347 |
|
E-PMN | | | 43.23 332 | 42.29 332 | 46.03 344 | 65.58 356 | 37.41 360 | 73.51 349 | 64.62 358 | 33.99 355 | 28.47 357 | 47.87 355 | 19.90 360 | 67.91 357 | 22.23 357 | 24.45 355 | 32.77 356 |
|
LCM-MVSNet | | | 66.00 319 | 62.16 323 | 77.51 328 | 64.51 357 | 58.29 342 | 83.87 333 | 90.90 292 | 48.17 349 | 54.69 345 | 73.31 344 | 16.83 362 | 86.75 344 | 65.47 312 | 61.67 345 | 87.48 338 |
|
EMVS | | | 42.07 333 | 41.12 333 | 44.92 346 | 63.45 358 | 35.56 362 | 73.65 348 | 63.48 359 | 33.05 356 | 26.88 358 | 45.45 357 | 21.27 358 | 67.14 358 | 19.80 358 | 23.02 357 | 32.06 357 |
|
wuykxyi23d | | | 50.55 329 | 44.13 331 | 69.81 335 | 56.77 359 | 54.58 350 | 73.22 351 | 80.78 350 | 39.79 354 | 22.08 359 | 46.69 356 | 4.03 366 | 79.71 354 | 47.65 344 | 26.13 354 | 75.14 348 |
|
MVE | | 39.65 23 | 43.39 331 | 38.59 336 | 57.77 342 | 56.52 360 | 48.77 353 | 55.38 356 | 58.64 361 | 29.33 357 | 28.96 356 | 52.65 353 | 4.68 365 | 64.62 359 | 28.11 356 | 33.07 351 | 59.93 353 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
ANet_high | | | 58.88 325 | 54.22 328 | 72.86 332 | 56.50 361 | 56.67 346 | 80.75 342 | 86.00 340 | 73.09 308 | 37.39 352 | 64.63 350 | 22.17 357 | 79.49 355 | 43.51 350 | 23.96 356 | 82.43 344 |
|
PMVS | | 47.18 22 | 52.22 328 | 48.46 329 | 63.48 340 | 45.72 362 | 46.20 355 | 73.41 350 | 78.31 354 | 41.03 353 | 30.06 355 | 65.68 348 | 6.05 364 | 83.43 351 | 30.04 355 | 65.86 338 | 60.80 352 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
tmp_tt | | | 35.64 335 | 39.24 334 | 24.84 348 | 14.87 363 | 23.90 364 | 62.71 355 | 51.51 363 | 6.58 359 | 36.66 353 | 62.08 351 | 44.37 342 | 30.34 362 | 52.40 336 | 22.00 358 | 20.27 358 |
|
testmvs | | | 8.92 338 | 11.52 339 | 1.12 351 | 1.06 364 | 0.46 366 | 86.02 317 | 0.65 366 | 0.62 360 | 2.74 361 | 9.52 361 | 0.31 369 | 0.45 364 | 2.38 360 | 0.39 360 | 2.46 361 |
|
test123 | | | 8.76 339 | 11.22 340 | 1.39 350 | 0.85 365 | 0.97 365 | 85.76 321 | 0.35 367 | 0.54 361 | 2.45 362 | 8.14 362 | 0.60 368 | 0.48 363 | 2.16 361 | 0.17 362 | 2.71 360 |
|
cdsmvs_eth3d_5k | | | 22.14 336 | 29.52 337 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 95.76 122 | 0.00 362 | 0.00 363 | 94.29 116 | 75.66 144 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
pcd_1.5k_mvsjas | | | 6.64 341 | 8.86 342 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 79.70 92 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
sosnet-low-res | | | 0.00 342 | 0.00 343 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
sosnet | | | 0.00 342 | 0.00 343 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
uncertanet | | | 0.00 342 | 0.00 343 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
Regformer | | | 0.00 342 | 0.00 343 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
ab-mvs-re | | | 7.82 340 | 10.43 341 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 93.88 135 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
uanet | | | 0.00 342 | 0.00 343 | 0.00 352 | 0.00 366 | 0.00 367 | 0.00 359 | 0.00 368 | 0.00 362 | 0.00 363 | 0.00 364 | 0.00 370 | 0.00 365 | 0.00 362 | 0.00 363 | 0.00 363 |
|
GSMVS | | | | | | | | | | | | | | | | | 96.12 114 |
|
test_part3 | | | | | | | | 95.99 37 | | 88.25 66 | | 97.60 5 | | 99.62 1 | 93.18 19 | | |
|
test_part1 | | | | | | | | | 97.45 6 | | | | 91.93 1 | | | 99.02 3 | 98.67 5 |
|
sam_mvs1 | | | | | | | | | | | | | 71.70 193 | | | | 96.12 114 |
|
sam_mvs | | | | | | | | | | | | | 70.60 209 | | | | |
|
MTGPA | | | | | | | | | 96.97 35 | | | | | | | | |
|
test_post1 | | | | | | | | 88.00 305 | | | | 9.81 360 | 69.31 228 | 95.53 280 | 76.65 232 | | |
|
test_post | | | | | | | | | | | | 10.29 359 | 70.57 213 | 95.91 269 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 83.76 323 | 71.53 197 | 96.48 245 | | | |
|
MTMP | | | | | | | | 96.16 31 | 60.64 360 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 91.91 43 | 98.71 20 | 98.07 46 |
|
agg_prior2 | | | | | | | | | | | | | | | 90.54 63 | 98.68 25 | 98.27 32 |
|
test_prior4 | | | | | | | 85.96 44 | 94.11 159 | | | | | | | | | |
|
test_prior2 | | | | | | | | 94.12 157 | | 87.67 81 | 92.63 46 | 96.39 53 | 86.62 26 | | 91.50 50 | 98.67 27 | |
|
旧先验2 | | | | | | | | 93.36 203 | | 71.25 321 | 94.37 14 | | | 97.13 204 | 86.74 102 | | |
|
新几何2 | | | | | | | | 93.11 217 | | | | | | | | | |
|
无先验 | | | | | | | | 93.28 210 | 96.26 83 | 73.95 301 | | | | 99.05 47 | 80.56 181 | | 96.59 102 |
|
原ACMM2 | | | | | | | | 92.94 225 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 98.75 80 | 78.30 216 | | |
|
segment_acmp | | | | | | | | | | | | | 87.16 22 | | | | |
|
testdata1 | | | | | | | | 92.15 248 | | 87.94 72 | | | | | | | |
|
plane_prior5 | | | | | | | | | 96.22 87 | | | | | 98.12 113 | 88.15 81 | 89.99 167 | 94.63 174 |
|
plane_prior4 | | | | | | | | | | | | 94.86 100 | | | | | |
|
plane_prior3 | | | | | | | 82.75 111 | | | 90.26 25 | 86.91 133 | | | | | | |
|
plane_prior2 | | | | | | | | 95.85 45 | | 90.81 18 | | | | | | | |
|
plane_prior | | | | | | | 82.73 114 | 95.21 77 | | 89.66 35 | | | | | | 89.88 171 | |
|
n2 | | | | | | | | | 0.00 368 | | | | | | | | |
|
nn | | | | | | | | | 0.00 368 | | | | | | | | |
|
door-mid | | | | | | | | | 85.49 341 | | | | | | | | |
|
test11 | | | | | | | | | 96.57 71 | | | | | | | | |
|
door | | | | | | | | | 85.33 342 | | | | | | | | |
|
HQP5-MVS | | | | | | | 81.56 132 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 87.11 99 | | |
|
HQP4-MVS | | | | | | | | | | | 85.43 176 | | | 97.96 134 | | | 94.51 184 |
|
HQP3-MVS | | | | | | | | | 96.04 101 | | | | | | | 89.77 173 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.83 169 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 55.91 349 | 87.62 310 | | 73.32 305 | 84.59 201 | | 70.33 216 | | 74.65 250 | | 95.50 137 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 87.47 210 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 88.01 207 | |
|
Test By Simon | | | | | | | | | | | | | 80.02 87 | | | | |
|