AdaColmap | | | 93.82 93 | 93.06 96 | 96.10 108 | 99.88 1 | 89.07 152 | 98.33 160 | 97.55 107 | 86.81 185 | 90.39 144 | 98.65 75 | 75.09 193 | 99.98 9 | 93.32 98 | 97.53 95 | 99.26 83 |
|
DP-MVS Recon | | | 95.85 52 | 95.15 58 | 97.95 20 | 99.87 2 | 94.38 43 | 99.60 17 | 97.48 118 | 86.58 188 | 94.42 89 | 99.13 31 | 87.36 78 | 99.98 9 | 93.64 91 | 98.33 84 | 99.48 67 |
|
MCST-MVS | | | 98.18 2 | 97.95 5 | 98.86 1 | 99.85 3 | 96.60 5 | 99.70 10 | 97.98 53 | 97.18 2 | 95.96 64 | 99.33 9 | 92.62 11 | 100.00 1 | 98.99 5 | 99.93 1 | 99.98 2 |
|
CNVR-MVS | | | 98.46 1 | 98.38 1 | 98.72 3 | 99.80 4 | 96.19 9 | 99.80 7 | 97.99 52 | 97.05 3 | 99.41 1 | 99.59 2 | 92.89 10 | 100.00 1 | 98.99 5 | 99.90 4 | 99.96 4 |
|
MG-MVS | | | 97.24 12 | 96.83 21 | 98.47 9 | 99.79 5 | 95.71 12 | 99.07 72 | 99.06 15 | 94.45 19 | 96.42 59 | 98.70 73 | 88.81 51 | 99.74 60 | 95.35 65 | 99.86 9 | 99.97 3 |
|
NCCC | | | 98.12 3 | 98.11 3 | 98.13 15 | 99.76 6 | 94.46 39 | 99.81 5 | 97.88 58 | 96.54 4 | 98.84 7 | 99.46 6 | 92.55 12 | 99.98 9 | 98.25 22 | 99.93 1 | 99.94 7 |
|
region2R | | | 96.30 42 | 96.17 38 | 96.70 78 | 99.70 7 | 90.31 127 | 99.46 30 | 97.66 85 | 90.55 84 | 97.07 43 | 99.07 36 | 86.85 87 | 99.97 14 | 95.43 63 | 99.74 20 | 99.81 22 |
|
HFP-MVS | | | 96.42 38 | 96.26 35 | 96.90 63 | 99.69 8 | 90.96 113 | 99.47 27 | 97.81 67 | 90.54 85 | 96.88 46 | 99.05 39 | 87.57 69 | 99.96 17 | 95.65 57 | 99.72 22 | 99.78 29 |
|
#test# | | | 96.48 35 | 96.34 33 | 96.90 63 | 99.69 8 | 90.96 113 | 99.53 24 | 97.81 67 | 90.94 78 | 96.88 46 | 99.05 39 | 87.57 69 | 99.96 17 | 95.87 56 | 99.72 22 | 99.78 29 |
|
ACMMPR | | | 96.28 43 | 96.14 41 | 96.73 75 | 99.68 10 | 90.47 125 | 99.47 27 | 97.80 69 | 90.54 85 | 96.83 53 | 99.03 41 | 86.51 93 | 99.95 20 | 95.65 57 | 99.72 22 | 99.75 35 |
|
CP-MVS | | | 96.22 44 | 96.15 40 | 96.42 94 | 99.67 11 | 89.62 145 | 99.70 10 | 97.61 97 | 90.07 100 | 96.00 61 | 99.16 25 | 87.43 73 | 99.92 26 | 96.03 54 | 99.72 22 | 99.70 44 |
|
CPTT-MVS | | | 94.60 80 | 94.43 68 | 95.09 139 | 99.66 12 | 86.85 199 | 99.44 31 | 97.47 119 | 83.22 248 | 94.34 92 | 98.96 51 | 82.50 146 | 99.55 78 | 94.81 74 | 99.50 42 | 98.88 111 |
|
MSLP-MVS++ | | | 97.50 9 | 97.45 10 | 97.63 28 | 99.65 13 | 93.21 59 | 99.70 10 | 98.13 46 | 94.61 16 | 97.78 32 | 99.46 6 | 89.85 40 | 99.81 52 | 97.97 24 | 99.91 3 | 99.88 14 |
|
PAPR | | | 96.35 39 | 95.82 46 | 97.94 21 | 99.63 14 | 94.19 47 | 99.42 35 | 97.55 107 | 92.43 50 | 93.82 102 | 99.12 32 | 87.30 80 | 99.91 28 | 94.02 83 | 99.06 60 | 99.74 38 |
|
XVS | | | 96.47 36 | 96.37 31 | 96.77 71 | 99.62 15 | 90.66 123 | 99.43 33 | 97.58 101 | 92.41 54 | 96.86 49 | 98.96 51 | 87.37 75 | 99.87 37 | 95.65 57 | 99.43 47 | 99.78 29 |
|
X-MVStestdata | | | 90.69 167 | 88.66 180 | 96.77 71 | 99.62 15 | 90.66 123 | 99.43 33 | 97.58 101 | 92.41 54 | 96.86 49 | 29.59 364 | 87.37 75 | 99.87 37 | 95.65 57 | 99.43 47 | 99.78 29 |
|
DeepC-MVS_fast | | 93.52 2 | 97.16 15 | 96.84 20 | 98.13 15 | 99.61 17 | 94.45 40 | 98.85 99 | 97.64 91 | 96.51 6 | 95.88 65 | 99.39 8 | 87.35 79 | 99.99 4 | 96.61 41 | 99.69 27 | 99.96 4 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CDPH-MVS | | | 96.56 32 | 96.18 36 | 97.70 26 | 99.59 18 | 93.92 49 | 99.13 69 | 97.44 124 | 89.02 121 | 97.90 30 | 99.22 16 | 88.90 50 | 99.49 88 | 94.63 78 | 99.79 17 | 99.68 47 |
|
test_prior3 | | | 97.07 19 | 97.09 13 | 97.01 52 | 99.58 19 | 91.77 83 | 99.57 19 | 97.57 104 | 91.43 70 | 98.12 21 | 98.97 48 | 90.43 35 | 99.49 88 | 98.33 18 | 99.81 15 | 99.79 25 |
|
test_prior | | | | | 97.01 52 | 99.58 19 | 91.77 83 | | 97.57 104 | | | | | 99.49 88 | | | 99.79 25 |
|
APDe-MVS | | | 97.53 7 | 97.47 8 | 97.70 26 | 99.58 19 | 93.63 52 | 99.56 21 | 97.52 111 | 93.59 33 | 98.01 26 | 99.12 32 | 90.80 31 | 99.55 78 | 99.26 3 | 99.79 17 | 99.93 8 |
|
mPP-MVS | | | 95.90 51 | 95.75 49 | 96.38 96 | 99.58 19 | 89.41 150 | 99.26 50 | 97.41 128 | 90.66 80 | 94.82 84 | 98.95 53 | 86.15 99 | 99.98 9 | 95.24 68 | 99.64 30 | 99.74 38 |
|
TEST9 | | | | | | 99.57 23 | 93.17 60 | 99.38 38 | 97.66 85 | 89.57 106 | 98.39 13 | 99.18 21 | 90.88 28 | 99.66 65 | | | |
|
train_agg | | | 97.20 14 | 97.08 14 | 97.57 32 | 99.57 23 | 93.17 60 | 99.38 38 | 97.66 85 | 90.18 93 | 98.39 13 | 99.18 21 | 90.94 26 | 99.66 65 | 98.58 13 | 99.85 10 | 99.88 14 |
|
agg_prior3 | | | 97.09 18 | 96.97 17 | 97.45 35 | 99.56 25 | 92.79 72 | 99.36 42 | 97.67 84 | 89.59 104 | 98.36 15 | 99.16 25 | 90.57 33 | 99.68 62 | 98.58 13 | 99.85 10 | 99.88 14 |
|
test_8 | | | | | | 99.55 26 | 93.07 64 | 99.37 41 | 97.64 91 | 90.18 93 | 98.36 15 | 99.19 19 | 90.94 26 | 99.64 71 | | | |
|
test_part2 | | | | | | 99.54 27 | 95.42 14 | | | | 98.13 18 | | | | | | |
|
v1.0 | | | 40.64 336 | 54.18 328 | 0.00 354 | 99.54 27 | 0.00 369 | 0.00 360 | 97.69 81 | 92.81 45 | 98.13 18 | 99.48 5 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
HSP-MVS | | | 97.73 5 | 98.15 2 | 96.44 93 | 99.54 27 | 90.14 130 | 99.41 36 | 97.47 119 | 95.46 14 | 98.60 10 | 99.19 19 | 95.71 4 | 99.49 88 | 98.15 23 | 99.85 10 | 99.69 46 |
|
agg_prior1 | | | 97.12 16 | 97.03 15 | 97.38 41 | 99.54 27 | 92.66 73 | 99.35 44 | 97.64 91 | 90.38 88 | 97.98 27 | 99.17 23 | 90.84 30 | 99.61 74 | 98.57 15 | 99.78 19 | 99.87 18 |
|
agg_prior | | | | | | 99.54 27 | 92.66 73 | | 97.64 91 | | 97.98 27 | | | 99.61 74 | | | |
|
CSCG | | | 94.87 69 | 94.71 63 | 95.36 130 | 99.54 27 | 86.49 209 | 99.34 46 | 98.15 44 | 82.71 257 | 90.15 147 | 99.25 12 | 89.48 44 | 99.86 42 | 94.97 72 | 98.82 72 | 99.72 41 |
|
HPM-MVS++ | | | 97.72 6 | 97.59 7 | 98.14 14 | 99.53 33 | 94.76 30 | 99.19 53 | 97.75 74 | 95.66 11 | 98.21 17 | 99.29 10 | 91.10 18 | 99.99 4 | 97.68 28 | 99.87 6 | 99.68 47 |
|
APD-MVS | | | 96.95 22 | 96.72 24 | 97.63 28 | 99.51 34 | 93.58 53 | 99.16 58 | 97.44 124 | 90.08 99 | 98.59 11 | 99.07 36 | 89.06 47 | 99.42 97 | 97.92 25 | 99.66 28 | 99.88 14 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ESAPD | | | 98.11 4 | 98.00 4 | 98.44 10 | 99.50 35 | 95.39 15 | 99.29 49 | 97.72 79 | 94.50 17 | 98.64 9 | 99.54 3 | 93.32 9 | 99.97 14 | 99.58 1 | 99.90 4 | 99.95 6 |
|
PGM-MVS | | | 95.85 52 | 95.65 51 | 96.45 92 | 99.50 35 | 89.77 142 | 98.22 175 | 98.90 17 | 89.19 115 | 96.74 55 | 98.95 53 | 85.91 101 | 99.92 26 | 93.94 84 | 99.46 44 | 99.66 50 |
|
MP-MVS | | | 96.00 48 | 95.82 46 | 96.54 89 | 99.47 37 | 90.13 132 | 99.36 42 | 97.41 128 | 90.64 83 | 95.49 74 | 98.95 53 | 85.51 105 | 99.98 9 | 96.00 55 | 99.59 39 | 99.52 62 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
Regformer-1 | | | 96.97 21 | 96.80 22 | 97.47 34 | 99.46 38 | 93.11 62 | 98.89 96 | 97.94 54 | 92.89 42 | 96.90 45 | 99.02 42 | 89.78 41 | 99.53 81 | 97.06 32 | 99.26 56 | 99.75 35 |
|
Regformer-2 | | | 96.94 24 | 96.78 23 | 97.42 37 | 99.46 38 | 92.97 68 | 98.89 96 | 97.93 55 | 92.86 44 | 96.88 46 | 99.02 42 | 89.74 42 | 99.53 81 | 97.03 33 | 99.26 56 | 99.75 35 |
|
PAPM_NR | | | 95.43 59 | 95.05 60 | 96.57 88 | 99.42 40 | 90.14 130 | 98.58 132 | 97.51 113 | 90.65 82 | 92.44 113 | 98.90 58 | 87.77 68 | 99.90 30 | 90.88 120 | 99.32 53 | 99.68 47 |
|
Regformer-3 | | | 96.50 34 | 96.36 32 | 96.91 62 | 99.34 41 | 91.72 86 | 98.71 110 | 97.90 57 | 92.48 49 | 96.00 61 | 98.95 53 | 88.60 53 | 99.52 84 | 96.44 45 | 98.83 70 | 99.49 65 |
|
Regformer-4 | | | 96.45 37 | 96.33 34 | 96.81 70 | 99.34 41 | 91.44 94 | 98.71 110 | 97.88 58 | 92.43 50 | 95.97 63 | 98.95 53 | 88.42 57 | 99.51 85 | 96.40 46 | 98.83 70 | 99.49 65 |
|
PHI-MVS | | | 96.65 30 | 96.46 29 | 97.21 46 | 99.34 41 | 91.77 83 | 99.70 10 | 98.05 48 | 86.48 190 | 98.05 23 | 99.20 18 | 89.33 45 | 99.96 17 | 98.38 17 | 99.62 34 | 99.90 10 |
|
test12 | | | | | 97.83 23 | 99.33 44 | 94.45 40 | | 97.55 107 | | 97.56 33 | | 88.60 53 | 99.50 87 | | 99.71 26 | 99.55 60 |
|
SMA-MVS | | | 97.24 12 | 96.99 16 | 98.00 19 | 99.30 45 | 94.20 46 | 99.16 58 | 97.65 90 | 89.55 108 | 99.22 2 | 99.52 4 | 90.34 38 | 99.99 4 | 98.32 20 | 99.83 13 | 99.82 21 |
|
zzz-MVS | | | 96.21 45 | 95.96 42 | 96.96 60 | 99.29 46 | 91.19 103 | 98.69 114 | 97.45 121 | 92.58 46 | 94.39 90 | 99.24 14 | 86.43 95 | 99.99 4 | 96.22 48 | 99.40 50 | 99.71 42 |
|
MTAPA | | | 96.09 47 | 95.80 48 | 96.96 60 | 99.29 46 | 91.19 103 | 97.23 221 | 97.45 121 | 92.58 46 | 94.39 90 | 99.24 14 | 86.43 95 | 99.99 4 | 96.22 48 | 99.40 50 | 99.71 42 |
|
HPM-MVS | | | 95.41 61 | 95.22 57 | 95.99 110 | 99.29 46 | 89.14 151 | 99.17 57 | 97.09 155 | 87.28 176 | 95.40 75 | 98.48 87 | 84.93 113 | 99.38 100 | 95.64 61 | 99.65 29 | 99.47 68 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
ACMMP | | | 94.67 77 | 94.30 70 | 95.79 116 | 99.25 49 | 88.13 169 | 98.41 154 | 98.67 24 | 90.38 88 | 91.43 125 | 98.72 71 | 82.22 153 | 99.95 20 | 93.83 88 | 95.76 124 | 99.29 79 |
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_3200maxsize | | | 95.64 58 | 95.65 51 | 95.62 120 | 99.24 50 | 87.80 175 | 98.42 152 | 97.22 141 | 88.93 126 | 96.64 58 | 98.98 47 | 85.49 106 | 99.36 102 | 96.68 40 | 99.27 55 | 99.70 44 |
|
API-MVS | | | 94.78 71 | 94.18 73 | 96.59 87 | 99.21 51 | 90.06 136 | 98.80 104 | 97.78 72 | 83.59 238 | 93.85 100 | 99.21 17 | 83.79 123 | 99.97 14 | 92.37 108 | 99.00 63 | 99.74 38 |
|
PLC | | 91.07 3 | 94.23 86 | 94.01 77 | 94.87 146 | 99.17 52 | 87.49 181 | 99.25 51 | 96.55 182 | 88.43 142 | 91.26 128 | 98.21 96 | 85.92 100 | 99.86 42 | 89.77 131 | 97.57 93 | 97.24 175 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
EI-MVSNet-Vis-set | | | 95.76 57 | 95.63 53 | 96.17 104 | 99.14 53 | 90.33 126 | 98.49 143 | 97.82 64 | 91.92 61 | 94.75 85 | 98.88 60 | 87.06 83 | 99.48 93 | 95.40 64 | 97.17 101 | 98.70 126 |
|
TSAR-MVS + MP. | | | 97.44 10 | 97.46 9 | 97.39 40 | 99.12 54 | 93.49 57 | 98.52 137 | 97.50 116 | 94.46 18 | 98.99 3 | 98.64 76 | 91.58 15 | 99.08 117 | 98.49 16 | 99.83 13 | 99.60 58 |
|
HPM-MVS_fast | | | 94.89 68 | 94.62 64 | 95.70 119 | 99.11 55 | 88.44 166 | 99.14 66 | 97.11 151 | 85.82 196 | 95.69 70 | 98.47 88 | 83.46 127 | 99.32 106 | 93.16 100 | 99.63 33 | 99.35 72 |
|
MAR-MVS | | | 94.43 82 | 94.09 75 | 95.45 129 | 99.10 56 | 87.47 182 | 98.39 158 | 97.79 71 | 88.37 144 | 94.02 97 | 99.17 23 | 78.64 177 | 99.91 28 | 92.48 107 | 98.85 69 | 98.96 103 |
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 |
114514_t | | | 94.06 89 | 93.05 97 | 97.06 50 | 99.08 57 | 92.26 81 | 98.97 85 | 97.01 163 | 82.58 259 | 92.57 111 | 98.22 94 | 80.68 163 | 99.30 107 | 89.34 137 | 99.02 62 | 99.63 54 |
|
EI-MVSNet-UG-set | | | 95.43 59 | 95.29 55 | 95.86 115 | 99.07 58 | 89.87 139 | 98.43 151 | 97.80 69 | 91.78 64 | 94.11 96 | 98.77 65 | 86.25 98 | 99.48 93 | 94.95 73 | 96.45 108 | 98.22 150 |
|
原ACMM1 | | | | | 96.18 102 | 99.03 59 | 90.08 133 | | 97.63 95 | 88.98 122 | 97.00 44 | 98.97 48 | 88.14 63 | 99.71 61 | 88.23 148 | 99.62 34 | 98.76 123 |
|
SD-MVS | | | 97.51 8 | 97.40 11 | 97.81 24 | 99.01 60 | 93.79 51 | 99.33 47 | 97.38 131 | 93.73 30 | 98.83 8 | 99.02 42 | 90.87 29 | 99.88 34 | 98.69 9 | 99.74 20 | 99.77 34 |
|
旧先验1 | | | | | | 98.97 61 | 92.90 70 | | 97.74 76 | | | 99.15 27 | 91.05 19 | | | 99.33 52 | 99.60 58 |
|
LS3D | | | 90.19 172 | 88.72 178 | 94.59 153 | 98.97 61 | 86.33 216 | 96.90 234 | 96.60 176 | 74.96 315 | 84.06 205 | 98.74 68 | 75.78 190 | 99.83 47 | 74.93 277 | 97.57 93 | 97.62 168 |
|
CNLPA | | | 93.64 100 | 92.74 102 | 96.36 97 | 98.96 63 | 90.01 138 | 99.19 53 | 95.89 229 | 86.22 193 | 89.40 160 | 98.85 61 | 80.66 164 | 99.84 45 | 88.57 146 | 96.92 102 | 99.24 84 |
|
MP-MVS-pluss | | | 95.80 54 | 95.30 54 | 97.29 43 | 98.95 64 | 92.66 73 | 98.59 131 | 97.14 147 | 88.95 124 | 93.12 106 | 99.25 12 | 85.62 102 | 99.94 22 | 96.56 43 | 99.48 43 | 99.28 81 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
新几何1 | | | | | 97.40 39 | 98.92 65 | 92.51 79 | | 97.77 73 | 85.52 199 | 96.69 57 | 99.06 38 | 88.08 64 | 99.89 33 | 84.88 179 | 99.62 34 | 99.79 25 |
|
DP-MVS | | | 88.75 199 | 86.56 207 | 95.34 131 | 98.92 65 | 87.45 183 | 97.64 208 | 93.52 303 | 70.55 326 | 81.49 243 | 97.25 125 | 74.43 207 | 99.88 34 | 71.14 307 | 94.09 137 | 98.67 127 |
|
1121 | | | 95.19 65 | 94.45 67 | 97.42 37 | 98.88 67 | 92.58 77 | 96.22 260 | 97.75 74 | 85.50 201 | 96.86 49 | 99.01 46 | 88.59 55 | 99.90 30 | 87.64 154 | 99.60 37 | 99.79 25 |
|
TSAR-MVS + GP. | | | 96.95 22 | 96.91 18 | 97.07 49 | 98.88 67 | 91.62 88 | 99.58 18 | 96.54 183 | 95.09 15 | 96.84 52 | 98.63 77 | 91.16 16 | 99.77 57 | 99.04 4 | 96.42 109 | 99.81 22 |
|
CANet | | | 97.00 20 | 96.49 28 | 98.55 6 | 98.86 69 | 96.10 10 | 99.83 4 | 97.52 111 | 95.90 8 | 97.21 40 | 98.90 58 | 82.66 145 | 99.93 24 | 98.71 8 | 98.80 73 | 99.63 54 |
|
ACMMP_Plus | | | 96.59 31 | 96.18 36 | 97.81 24 | 98.82 70 | 93.55 54 | 98.88 98 | 97.59 99 | 90.66 80 | 97.98 27 | 99.14 29 | 86.59 90 | 100.00 1 | 96.47 44 | 99.46 44 | 99.89 13 |
|
PVSNet_BlendedMVS | | | 93.36 107 | 93.20 94 | 93.84 177 | 98.77 71 | 91.61 89 | 99.47 27 | 98.04 49 | 91.44 69 | 94.21 93 | 92.63 226 | 83.50 125 | 99.87 37 | 97.41 29 | 83.37 231 | 90.05 291 |
|
PVSNet_Blended | | | 95.94 50 | 95.66 50 | 96.75 73 | 98.77 71 | 91.61 89 | 99.88 1 | 98.04 49 | 93.64 32 | 94.21 93 | 97.76 104 | 83.50 125 | 99.87 37 | 97.41 29 | 97.75 92 | 98.79 118 |
|
DeepPCF-MVS | | 93.56 1 | 96.55 33 | 97.84 6 | 92.68 198 | 98.71 73 | 78.11 311 | 99.70 10 | 97.71 80 | 98.18 1 | 97.36 38 | 99.76 1 | 90.37 37 | 99.94 22 | 99.27 2 | 99.54 41 | 99.99 1 |
|
EPNet | | | 96.82 26 | 96.68 26 | 97.25 45 | 98.65 74 | 93.10 63 | 99.48 26 | 98.76 18 | 96.54 4 | 97.84 31 | 98.22 94 | 87.49 72 | 99.66 65 | 95.35 65 | 97.78 91 | 99.00 98 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
OMC-MVS | | | 93.90 91 | 93.62 88 | 94.73 150 | 98.63 75 | 87.00 195 | 98.04 192 | 96.56 181 | 92.19 58 | 92.46 112 | 98.73 69 | 79.49 169 | 99.14 114 | 92.16 111 | 94.34 136 | 98.03 156 |
|
abl_6 | | | 94.63 79 | 94.48 66 | 95.09 139 | 98.61 76 | 86.96 196 | 98.06 191 | 96.97 165 | 89.31 111 | 95.86 67 | 98.56 80 | 79.82 166 | 99.64 71 | 94.53 80 | 98.65 79 | 98.66 128 |
|
MVS_111021_HR | | | 96.69 28 | 96.69 25 | 96.72 77 | 98.58 77 | 91.00 112 | 99.14 66 | 99.45 1 | 93.86 27 | 95.15 80 | 98.73 69 | 88.48 56 | 99.76 58 | 97.23 31 | 99.56 40 | 99.40 70 |
|
0601test | | | 95.27 64 | 94.60 65 | 97.28 44 | 98.53 78 | 92.98 67 | 99.05 77 | 98.70 22 | 86.76 186 | 94.65 88 | 97.74 106 | 87.78 67 | 99.44 96 | 95.57 62 | 92.61 149 | 99.44 69 |
|
TAPA-MVS | | 87.50 9 | 90.35 168 | 89.05 172 | 94.25 164 | 98.48 79 | 85.17 245 | 98.42 152 | 96.58 180 | 82.44 263 | 87.24 183 | 98.53 81 | 82.77 144 | 98.84 122 | 59.09 335 | 97.88 87 | 98.72 124 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MVS_0304 | | | 96.12 46 | 95.26 56 | 98.69 4 | 98.44 80 | 96.54 7 | 99.70 10 | 96.89 168 | 95.76 10 | 97.53 34 | 99.12 32 | 72.42 233 | 99.93 24 | 98.75 7 | 98.69 76 | 99.61 57 |
|
test222 | | | | | | 98.32 81 | 91.21 102 | 98.08 189 | 97.58 101 | 83.74 234 | 95.87 66 | 99.02 42 | 86.74 88 | | | 99.64 30 | 99.81 22 |
|
LFMVS | | | 92.23 136 | 90.84 150 | 96.42 94 | 98.24 82 | 91.08 110 | 98.24 173 | 96.22 203 | 83.39 246 | 94.74 86 | 98.31 92 | 61.12 305 | 98.85 121 | 94.45 81 | 92.82 145 | 99.32 75 |
|
testdata | | | | | 95.26 134 | 98.20 83 | 87.28 191 | | 97.60 98 | 85.21 205 | 98.48 12 | 99.15 27 | 88.15 62 | 98.72 130 | 90.29 125 | 99.45 46 | 99.78 29 |
|
PatchMatch-RL | | | 91.47 151 | 90.54 157 | 94.26 163 | 98.20 83 | 86.36 215 | 96.94 232 | 97.14 147 | 87.75 162 | 88.98 163 | 95.75 172 | 71.80 241 | 99.40 99 | 80.92 220 | 97.39 98 | 97.02 183 |
|
MVS_111021_LR | | | 95.78 55 | 95.94 43 | 95.28 133 | 98.19 85 | 87.69 176 | 98.80 104 | 99.26 13 | 93.39 35 | 95.04 82 | 98.69 74 | 84.09 121 | 99.76 58 | 96.96 38 | 99.06 60 | 98.38 142 |
|
F-COLMAP | | | 92.07 142 | 91.75 128 | 93.02 190 | 98.16 86 | 82.89 273 | 98.79 107 | 95.97 216 | 86.54 189 | 87.92 176 | 97.80 102 | 78.69 176 | 99.65 69 | 85.97 169 | 95.93 121 | 96.53 199 |
|
Anonymous202405211 | | | 88.84 193 | 87.03 204 | 94.27 162 | 98.14 87 | 84.18 257 | 98.44 150 | 95.58 250 | 76.79 310 | 89.34 161 | 96.88 146 | 53.42 328 | 99.54 80 | 87.53 156 | 87.12 205 | 99.09 92 |
|
VNet | | | 95.08 66 | 94.26 71 | 97.55 33 | 98.07 88 | 93.88 50 | 98.68 117 | 98.73 21 | 90.33 90 | 97.16 42 | 97.43 116 | 79.19 171 | 99.53 81 | 96.91 39 | 91.85 161 | 99.24 84 |
|
DELS-MVS | | | 97.12 16 | 96.60 27 | 98.68 5 | 98.03 89 | 96.57 6 | 99.84 3 | 97.84 62 | 96.36 7 | 95.20 79 | 98.24 93 | 88.17 61 | 99.83 47 | 96.11 52 | 99.60 37 | 99.64 52 |
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 |
PVSNet | | 87.13 12 | 93.69 96 | 92.83 101 | 96.28 99 | 97.99 90 | 90.22 129 | 99.38 38 | 98.93 16 | 91.42 72 | 93.66 103 | 97.68 108 | 71.29 247 | 99.64 71 | 87.94 151 | 97.20 100 | 98.98 101 |
|
CHOSEN 280x420 | | | 96.80 27 | 96.85 19 | 96.66 81 | 97.85 91 | 94.42 42 | 94.76 290 | 98.36 27 | 92.50 48 | 95.62 73 | 97.52 112 | 97.92 1 | 97.38 199 | 98.31 21 | 98.80 73 | 98.20 152 |
|
thres200 | | | 93.69 96 | 92.59 106 | 96.97 59 | 97.76 92 | 94.74 31 | 99.35 44 | 99.36 2 | 89.23 114 | 91.21 130 | 96.97 142 | 83.42 128 | 98.77 124 | 85.08 177 | 90.96 172 | 97.39 172 |
|
tfpn_ndepth | | | 93.28 111 | 92.32 109 | 96.16 105 | 97.74 93 | 92.86 71 | 99.01 81 | 98.19 40 | 85.50 201 | 89.84 152 | 97.12 134 | 93.57 7 | 97.58 184 | 79.39 234 | 90.50 180 | 98.04 155 |
|
HY-MVS | | 88.56 7 | 95.29 63 | 94.23 72 | 98.48 8 | 97.72 94 | 96.41 8 | 94.03 298 | 98.74 19 | 92.42 53 | 95.65 72 | 94.76 185 | 86.52 92 | 99.49 88 | 95.29 67 | 92.97 144 | 99.53 61 |
|
Anonymous20231211 | | | 84.72 255 | 82.65 265 | 90.91 231 | 97.71 95 | 84.55 253 | 97.28 217 | 96.67 173 | 66.88 338 | 79.18 265 | 90.87 251 | 58.47 310 | 96.60 221 | 82.61 203 | 74.20 283 | 91.59 245 |
|
tfpn200view9 | | | 93.43 104 | 92.27 112 | 96.90 63 | 97.68 96 | 94.84 24 | 99.18 55 | 99.36 2 | 88.45 139 | 90.79 133 | 96.90 144 | 83.31 129 | 98.75 126 | 84.11 187 | 90.69 174 | 97.12 177 |
|
thres400 | | | 93.39 106 | 92.27 112 | 96.73 75 | 97.68 96 | 94.84 24 | 99.18 55 | 99.36 2 | 88.45 139 | 90.79 133 | 96.90 144 | 83.31 129 | 98.75 126 | 84.11 187 | 90.69 174 | 96.61 190 |
|
tfpn111 | | | 93.20 114 | 92.00 121 | 96.83 69 | 97.62 98 | 94.84 24 | 99.06 74 | 99.36 2 | 87.96 154 | 90.47 140 | 96.78 147 | 83.29 131 | 98.71 131 | 82.93 199 | 90.47 181 | 96.94 184 |
|
conf200view11 | | | 93.32 109 | 92.15 117 | 96.84 68 | 97.62 98 | 94.84 24 | 99.06 74 | 99.36 2 | 87.96 154 | 90.47 140 | 96.78 147 | 83.29 131 | 98.75 126 | 84.11 187 | 90.69 174 | 96.94 184 |
|
thres100view900 | | | 93.34 108 | 92.15 117 | 96.90 63 | 97.62 98 | 94.84 24 | 99.06 74 | 99.36 2 | 87.96 154 | 90.47 140 | 96.78 147 | 83.29 131 | 98.75 126 | 84.11 187 | 90.69 174 | 97.12 177 |
|
thres600view7 | | | 93.18 115 | 92.00 121 | 96.75 73 | 97.62 98 | 94.92 21 | 99.07 72 | 99.36 2 | 87.96 154 | 90.47 140 | 96.78 147 | 83.29 131 | 98.71 131 | 82.93 199 | 90.47 181 | 96.61 190 |
|
WTY-MVS | | | 95.97 49 | 95.11 59 | 98.54 7 | 97.62 98 | 96.65 4 | 99.44 31 | 98.74 19 | 92.25 57 | 95.21 78 | 98.46 90 | 86.56 91 | 99.46 95 | 95.00 71 | 92.69 148 | 99.50 64 |
|
tfpn1000 | | | 92.67 128 | 91.64 130 | 95.78 117 | 97.61 103 | 92.34 80 | 98.69 114 | 98.18 41 | 84.15 223 | 88.80 165 | 96.99 141 | 93.56 8 | 97.21 203 | 76.56 259 | 90.19 183 | 97.77 164 |
|
Anonymous20240529 | | | 87.66 210 | 85.58 226 | 93.92 174 | 97.59 104 | 85.01 248 | 98.13 184 | 97.13 149 | 66.69 339 | 88.47 167 | 96.01 170 | 55.09 322 | 99.51 85 | 87.00 161 | 84.12 225 | 97.23 176 |
|
HyFIR lowres test | | | 93.68 98 | 93.29 92 | 94.87 146 | 97.57 105 | 88.04 171 | 98.18 180 | 98.47 25 | 87.57 168 | 91.24 129 | 95.05 181 | 85.49 106 | 97.46 190 | 93.22 99 | 92.82 145 | 99.10 91 |
|
canonicalmvs | | | 95.02 67 | 93.96 81 | 98.20 12 | 97.53 106 | 95.92 11 | 98.71 110 | 96.19 206 | 91.78 64 | 95.86 67 | 98.49 86 | 79.53 168 | 99.03 118 | 96.12 51 | 91.42 169 | 99.66 50 |
|
view600 | | | 92.78 121 | 91.50 133 | 96.63 82 | 97.51 107 | 94.66 34 | 98.91 90 | 99.36 2 | 87.31 172 | 89.64 156 | 96.59 154 | 83.26 136 | 98.63 135 | 80.76 223 | 90.15 184 | 96.61 190 |
|
view800 | | | 92.78 121 | 91.50 133 | 96.63 82 | 97.51 107 | 94.66 34 | 98.91 90 | 99.36 2 | 87.31 172 | 89.64 156 | 96.59 154 | 83.26 136 | 98.63 135 | 80.76 223 | 90.15 184 | 96.61 190 |
|
conf0.05thres1000 | | | 92.78 121 | 91.50 133 | 96.63 82 | 97.51 107 | 94.66 34 | 98.91 90 | 99.36 2 | 87.31 172 | 89.64 156 | 96.59 154 | 83.26 136 | 98.63 135 | 80.76 223 | 90.15 184 | 96.61 190 |
|
tfpn | | | 92.78 121 | 91.50 133 | 96.63 82 | 97.51 107 | 94.66 34 | 98.91 90 | 99.36 2 | 87.31 172 | 89.64 156 | 96.59 154 | 83.26 136 | 98.63 135 | 80.76 223 | 90.15 184 | 96.61 190 |
|
CHOSEN 1792x2688 | | | 94.35 84 | 93.82 86 | 95.95 112 | 97.40 111 | 88.74 159 | 98.41 154 | 98.27 29 | 92.18 59 | 91.43 125 | 96.40 162 | 78.88 172 | 99.81 52 | 93.59 92 | 97.81 88 | 99.30 77 |
|
SteuartSystems-ACMMP | | | 97.25 11 | 97.34 12 | 97.01 52 | 97.38 112 | 91.46 92 | 99.75 8 | 97.66 85 | 94.14 22 | 98.13 18 | 99.26 11 | 92.16 13 | 99.66 65 | 97.91 26 | 99.64 30 | 99.90 10 |
Skip Steuart: Steuart Systems R&D Blog. |
alignmvs | | | 95.77 56 | 95.00 61 | 98.06 18 | 97.35 113 | 95.68 13 | 99.71 9 | 97.50 116 | 91.50 68 | 96.16 60 | 98.61 78 | 86.28 97 | 99.00 119 | 96.19 50 | 91.74 163 | 99.51 63 |
|
PS-MVSNAJ | | | 96.87 25 | 96.40 30 | 98.29 11 | 97.35 113 | 97.29 1 | 99.03 78 | 97.11 151 | 95.83 9 | 98.97 4 | 99.14 29 | 82.48 148 | 99.60 76 | 98.60 10 | 99.08 59 | 98.00 157 |
|
EPNet_dtu | | | 92.28 134 | 92.15 117 | 92.70 197 | 97.29 115 | 84.84 249 | 98.64 123 | 97.82 64 | 92.91 41 | 93.02 109 | 97.02 139 | 85.48 108 | 95.70 277 | 72.25 303 | 94.89 132 | 97.55 170 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVSTER | | | 92.71 126 | 92.32 109 | 93.86 176 | 97.29 115 | 92.95 69 | 99.01 81 | 96.59 177 | 90.09 98 | 85.51 194 | 94.00 196 | 94.61 5 | 96.56 224 | 90.77 123 | 83.03 234 | 92.08 232 |
|
EPMVS | | | 92.59 131 | 91.59 131 | 95.59 122 | 97.22 117 | 90.03 137 | 91.78 317 | 98.04 49 | 90.42 87 | 91.66 119 | 90.65 264 | 86.49 94 | 97.46 190 | 81.78 214 | 96.31 112 | 99.28 81 |
|
tpmvs | | | 89.16 187 | 87.76 191 | 93.35 183 | 97.19 118 | 84.75 251 | 90.58 327 | 97.36 133 | 81.99 267 | 84.56 199 | 89.31 294 | 83.98 122 | 98.17 146 | 74.85 279 | 90.00 189 | 97.12 177 |
|
DeepC-MVS | | 91.02 4 | 94.56 81 | 93.92 84 | 96.46 91 | 97.16 119 | 90.76 119 | 98.39 158 | 97.11 151 | 93.92 23 | 88.66 166 | 98.33 91 | 78.14 179 | 99.85 44 | 95.02 70 | 98.57 80 | 98.78 121 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PVSNet_Blended_VisFu | | | 94.67 77 | 94.11 74 | 96.34 98 | 97.14 120 | 91.10 108 | 99.32 48 | 97.43 126 | 92.10 60 | 91.53 123 | 96.38 165 | 83.29 131 | 99.68 62 | 93.42 96 | 96.37 110 | 98.25 149 |
|
xiu_mvs_v2_base | | | 96.66 29 | 96.17 38 | 98.11 17 | 97.11 121 | 96.96 2 | 99.01 81 | 97.04 159 | 95.51 13 | 98.86 6 | 99.11 35 | 82.19 154 | 99.36 102 | 98.59 12 | 98.14 85 | 98.00 157 |
|
VDD-MVS | | | 91.24 157 | 90.18 160 | 94.45 157 | 97.08 122 | 85.84 235 | 98.40 157 | 96.10 212 | 86.99 178 | 93.36 104 | 98.16 97 | 54.27 325 | 99.20 108 | 96.59 42 | 90.63 178 | 98.31 148 |
|
UGNet | | | 91.91 146 | 90.85 149 | 95.10 138 | 97.06 123 | 88.69 160 | 98.01 193 | 98.24 31 | 92.41 54 | 92.39 114 | 93.61 208 | 60.52 306 | 99.68 62 | 88.14 149 | 97.25 99 | 96.92 188 |
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022 |
CANet_DTU | | | 94.31 85 | 93.35 91 | 97.20 47 | 97.03 124 | 94.71 32 | 98.62 125 | 95.54 251 | 95.61 12 | 97.21 40 | 98.47 88 | 71.88 239 | 99.84 45 | 88.38 147 | 97.46 97 | 97.04 182 |
|
DWT-MVSNet_test | | | 94.36 83 | 93.95 82 | 95.62 120 | 96.99 125 | 89.47 148 | 96.62 245 | 97.38 131 | 90.96 77 | 93.07 108 | 97.27 124 | 93.73 6 | 98.09 151 | 85.86 173 | 93.65 140 | 99.29 79 |
|
PatchFormer-LS_test | | | 94.08 88 | 93.60 89 | 95.53 127 | 96.92 126 | 89.57 146 | 96.51 249 | 97.34 135 | 91.29 74 | 92.22 116 | 97.18 130 | 91.66 14 | 98.02 156 | 87.05 159 | 92.21 156 | 99.00 98 |
|
MSDG | | | 88.29 205 | 86.37 209 | 94.04 170 | 96.90 127 | 86.15 223 | 96.52 248 | 94.36 291 | 77.89 307 | 79.22 264 | 96.95 143 | 69.72 255 | 99.59 77 | 73.20 296 | 92.58 150 | 96.37 200 |
|
BH-w/o | | | 92.32 133 | 91.79 126 | 93.91 175 | 96.85 128 | 86.18 221 | 99.11 70 | 95.74 234 | 88.13 151 | 84.81 197 | 97.00 140 | 77.26 184 | 97.91 159 | 89.16 143 | 98.03 86 | 97.64 165 |
|
conf0.01 | | | 92.06 143 | 90.99 140 | 95.24 136 | 96.84 129 | 91.39 95 | 98.31 163 | 98.20 33 | 83.57 239 | 88.08 170 | 97.34 118 | 91.05 19 | 97.40 193 | 75.80 265 | 89.74 191 | 96.94 184 |
|
conf0.002 | | | 92.06 143 | 90.99 140 | 95.24 136 | 96.84 129 | 91.39 95 | 98.31 163 | 98.20 33 | 83.57 239 | 88.08 170 | 97.34 118 | 91.05 19 | 97.40 193 | 75.80 265 | 89.74 191 | 96.94 184 |
|
thresconf0.02 | | | 92.14 137 | 90.99 140 | 95.58 123 | 96.84 129 | 91.39 95 | 98.31 163 | 98.20 33 | 83.57 239 | 88.08 170 | 97.34 118 | 91.05 19 | 97.40 193 | 75.80 265 | 89.74 191 | 97.94 159 |
|
tfpn_n400 | | | 92.14 137 | 90.99 140 | 95.58 123 | 96.84 129 | 91.39 95 | 98.31 163 | 98.20 33 | 83.57 239 | 88.08 170 | 97.34 118 | 91.05 19 | 97.40 193 | 75.80 265 | 89.74 191 | 97.94 159 |
|
tfpnconf | | | 92.14 137 | 90.99 140 | 95.58 123 | 96.84 129 | 91.39 95 | 98.31 163 | 98.20 33 | 83.57 239 | 88.08 170 | 97.34 118 | 91.05 19 | 97.40 193 | 75.80 265 | 89.74 191 | 97.94 159 |
|
tfpnview11 | | | 92.14 137 | 90.99 140 | 95.58 123 | 96.84 129 | 91.39 95 | 98.31 163 | 98.20 33 | 83.57 239 | 88.08 170 | 97.34 118 | 91.05 19 | 97.40 193 | 75.80 265 | 89.74 191 | 97.94 159 |
|
AllTest | | | 84.97 253 | 83.12 255 | 90.52 239 | 96.82 135 | 78.84 304 | 95.89 272 | 92.17 325 | 77.96 304 | 75.94 287 | 95.50 175 | 55.48 319 | 99.18 109 | 71.15 305 | 87.14 203 | 93.55 210 |
|
TestCases | | | | | 90.52 239 | 96.82 135 | 78.84 304 | | 92.17 325 | 77.96 304 | 75.94 287 | 95.50 175 | 55.48 319 | 99.18 109 | 71.15 305 | 87.14 203 | 93.55 210 |
|
PMMVS | | | 93.62 101 | 93.90 85 | 92.79 194 | 96.79 137 | 81.40 284 | 98.85 99 | 96.81 169 | 91.25 75 | 96.82 54 | 98.15 98 | 77.02 185 | 98.13 148 | 93.15 101 | 96.30 113 | 98.83 115 |
|
BH-RMVSNet | | | 91.25 156 | 89.99 162 | 95.03 144 | 96.75 138 | 88.55 163 | 98.65 121 | 94.95 275 | 87.74 163 | 87.74 177 | 97.80 102 | 68.27 266 | 98.14 147 | 80.53 228 | 97.49 96 | 98.41 139 |
|
MVS_Test | | | 93.67 99 | 92.67 104 | 96.69 79 | 96.72 139 | 92.66 73 | 97.22 222 | 96.03 214 | 87.69 166 | 95.12 81 | 94.03 194 | 81.55 157 | 98.28 144 | 89.17 142 | 96.46 107 | 99.14 89 |
|
COLMAP_ROB | | 82.69 18 | 84.54 260 | 82.82 259 | 89.70 258 | 96.72 139 | 78.85 303 | 95.89 272 | 92.83 318 | 71.55 323 | 77.54 280 | 95.89 171 | 59.40 309 | 99.14 114 | 67.26 315 | 88.26 199 | 91.11 257 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
mvs_anonymous | | | 92.50 132 | 91.65 129 | 95.06 142 | 96.60 141 | 89.64 144 | 97.06 229 | 96.44 189 | 86.64 187 | 84.14 203 | 93.93 198 | 82.49 147 | 96.17 261 | 91.47 114 | 96.08 118 | 99.35 72 |
|
casdiffmvs1 | | | 94.78 71 | 94.34 69 | 96.11 107 | 96.60 141 | 90.85 116 | 97.95 195 | 96.52 184 | 90.16 96 | 97.22 39 | 94.64 187 | 84.99 111 | 98.18 145 | 94.40 82 | 96.60 106 | 99.30 77 |
|
GG-mvs-BLEND | | | | | 96.98 58 | 96.53 143 | 94.81 29 | 87.20 331 | 97.74 76 | | 93.91 99 | 96.40 162 | 96.56 2 | 96.94 213 | 95.08 69 | 98.95 67 | 99.20 87 |
|
FMVSNet3 | | | 88.81 197 | 87.08 203 | 93.99 172 | 96.52 144 | 94.59 38 | 98.08 189 | 96.20 205 | 85.85 195 | 82.12 234 | 91.60 238 | 74.05 215 | 95.40 285 | 79.04 236 | 80.24 245 | 91.99 235 |
|
BH-untuned | | | 91.46 152 | 90.84 150 | 93.33 184 | 96.51 145 | 84.83 250 | 98.84 101 | 95.50 254 | 86.44 192 | 83.50 207 | 96.70 151 | 75.49 192 | 97.77 170 | 86.78 166 | 97.81 88 | 97.40 171 |
|
sss | | | 94.85 70 | 93.94 83 | 97.58 30 | 96.43 146 | 94.09 48 | 98.93 87 | 99.16 14 | 89.50 109 | 95.27 77 | 97.85 100 | 81.50 158 | 99.65 69 | 92.79 106 | 94.02 138 | 98.99 100 |
|
dp | | | 90.16 173 | 88.83 177 | 94.14 166 | 96.38 147 | 86.42 211 | 91.57 318 | 97.06 158 | 84.76 215 | 88.81 164 | 90.19 284 | 84.29 120 | 97.43 192 | 75.05 276 | 91.35 171 | 98.56 133 |
|
casdiffmvs | | | 94.10 87 | 93.40 90 | 96.20 100 | 96.31 148 | 91.46 92 | 97.65 207 | 96.22 203 | 88.49 135 | 95.69 70 | 94.11 190 | 83.01 141 | 98.10 150 | 93.33 97 | 95.82 123 | 99.04 95 |
|
TR-MVS | | | 90.77 164 | 89.44 166 | 94.76 148 | 96.31 148 | 88.02 172 | 97.92 196 | 95.96 218 | 85.52 199 | 88.22 169 | 97.23 127 | 66.80 278 | 98.09 151 | 84.58 182 | 92.38 151 | 98.17 153 |
|
UA-Net | | | 93.30 110 | 92.62 105 | 95.34 131 | 96.27 150 | 88.53 165 | 95.88 274 | 96.97 165 | 90.90 79 | 95.37 76 | 97.07 137 | 82.38 151 | 99.10 116 | 83.91 191 | 94.86 133 | 98.38 142 |
|
tpmrst | | | 92.78 121 | 92.16 116 | 94.65 152 | 96.27 150 | 87.45 183 | 91.83 316 | 97.10 154 | 89.10 120 | 94.68 87 | 90.69 258 | 88.22 60 | 97.73 177 | 89.78 130 | 91.80 162 | 98.77 122 |
|
ADS-MVSNet2 | | | 87.62 211 | 86.88 205 | 89.86 253 | 96.21 152 | 79.14 300 | 87.15 332 | 92.99 309 | 83.01 252 | 89.91 150 | 87.27 309 | 78.87 173 | 92.80 317 | 74.20 284 | 92.27 154 | 97.64 165 |
|
ADS-MVSNet | | | 88.99 189 | 87.30 198 | 94.07 168 | 96.21 152 | 87.56 180 | 87.15 332 | 96.78 171 | 83.01 252 | 89.91 150 | 87.27 309 | 78.87 173 | 97.01 210 | 74.20 284 | 92.27 154 | 97.64 165 |
|
PatchmatchNet | | | 92.05 145 | 91.04 139 | 95.06 142 | 96.17 154 | 89.04 153 | 91.26 321 | 97.26 136 | 89.56 107 | 90.64 137 | 90.56 270 | 88.35 59 | 97.11 206 | 79.53 231 | 96.07 119 | 99.03 96 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
gg-mvs-nofinetune | | | 90.00 177 | 87.71 193 | 96.89 67 | 96.15 155 | 94.69 33 | 85.15 337 | 97.74 76 | 68.32 335 | 92.97 110 | 60.16 350 | 96.10 3 | 96.84 215 | 93.89 85 | 98.87 68 | 99.14 89 |
|
MDTV_nov1_ep13 | | | | 90.47 159 | | 96.14 156 | 88.55 163 | 91.34 320 | 97.51 113 | 89.58 105 | 92.24 115 | 90.50 273 | 86.99 86 | 97.61 183 | 77.64 248 | 92.34 152 | |
|
IS-MVSNet | | | 93.00 118 | 92.51 107 | 94.49 155 | 96.14 156 | 87.36 189 | 98.31 163 | 95.70 238 | 88.58 134 | 90.17 146 | 97.50 113 | 83.02 140 | 97.22 202 | 87.06 158 | 96.07 119 | 98.90 110 |
|
Vis-MVSNet (Re-imp) | | | 93.26 113 | 93.00 99 | 94.06 169 | 96.14 156 | 86.71 205 | 98.68 117 | 96.70 172 | 88.30 146 | 89.71 155 | 97.64 109 | 85.43 109 | 96.39 241 | 88.06 150 | 96.32 111 | 99.08 93 |
|
diffmvs | | | 93.00 118 | 92.26 114 | 95.25 135 | 96.12 159 | 88.59 161 | 96.60 246 | 96.19 206 | 88.88 128 | 94.19 95 | 93.73 204 | 80.40 165 | 98.12 149 | 89.18 141 | 95.02 130 | 99.02 97 |
|
ab-mvs | | | 91.05 159 | 89.17 170 | 96.69 79 | 95.96 160 | 91.72 86 | 92.62 310 | 97.23 140 | 85.61 198 | 89.74 153 | 93.89 200 | 68.55 264 | 99.42 97 | 91.09 116 | 87.84 201 | 98.92 109 |
|
Fast-Effi-MVS+ | | | 91.72 148 | 90.79 153 | 94.49 155 | 95.89 161 | 87.40 186 | 99.54 23 | 95.70 238 | 85.01 211 | 89.28 162 | 95.68 173 | 77.75 181 | 97.57 188 | 83.22 195 | 95.06 129 | 98.51 135 |
|
EPP-MVSNet | | | 93.75 95 | 93.67 87 | 94.01 171 | 95.86 162 | 85.70 237 | 98.67 119 | 97.66 85 | 84.46 218 | 91.36 127 | 97.18 130 | 91.16 16 | 97.79 168 | 92.93 103 | 93.75 139 | 98.53 134 |
|
Effi-MVS+ | | | 93.87 92 | 93.15 95 | 96.02 109 | 95.79 163 | 90.76 119 | 96.70 242 | 95.78 232 | 86.98 180 | 95.71 69 | 97.17 132 | 79.58 167 | 98.01 157 | 94.57 79 | 96.09 117 | 99.31 76 |
|
tpm cat1 | | | 88.89 191 | 87.27 199 | 93.76 179 | 95.79 163 | 85.32 241 | 90.76 325 | 97.09 155 | 76.14 312 | 85.72 192 | 88.59 299 | 82.92 142 | 98.04 155 | 76.96 253 | 91.43 168 | 97.90 163 |
|
tpmp4_e23 | | | 91.05 159 | 90.07 161 | 93.97 173 | 95.77 165 | 85.30 242 | 92.64 309 | 97.09 155 | 84.42 220 | 91.53 123 | 90.31 276 | 87.38 74 | 97.82 166 | 80.86 222 | 90.62 179 | 98.79 118 |
|
3Dnovator+ | | 87.72 8 | 93.43 104 | 91.84 125 | 98.17 13 | 95.73 166 | 95.08 20 | 98.92 89 | 97.04 159 | 91.42 72 | 81.48 244 | 97.60 110 | 74.60 200 | 99.79 55 | 90.84 121 | 98.97 64 | 99.64 52 |
|
MVS | | | 93.92 90 | 92.28 111 | 98.83 2 | 95.69 167 | 96.82 3 | 96.22 260 | 98.17 42 | 84.89 213 | 84.34 202 | 98.61 78 | 79.32 170 | 99.83 47 | 93.88 86 | 99.43 47 | 99.86 19 |
|
cascas | | | 90.93 162 | 89.33 169 | 95.76 118 | 95.69 167 | 93.03 66 | 98.99 84 | 96.59 177 | 80.49 281 | 86.79 189 | 94.45 189 | 65.23 288 | 98.60 139 | 93.52 93 | 92.18 157 | 95.66 203 |
|
QAPM | | | 91.41 153 | 89.49 165 | 97.17 48 | 95.66 169 | 93.42 58 | 98.60 129 | 97.51 113 | 80.92 279 | 81.39 245 | 97.41 117 | 72.89 230 | 99.87 37 | 82.33 205 | 98.68 77 | 98.21 151 |
|
1112_ss | | | 92.71 126 | 91.55 132 | 96.20 100 | 95.56 170 | 91.12 106 | 98.48 144 | 94.69 281 | 88.29 147 | 86.89 187 | 98.50 84 | 87.02 84 | 98.66 133 | 84.75 180 | 89.77 190 | 98.81 116 |
|
LCM-MVSNet-Re | | | 88.59 201 | 88.61 181 | 88.51 281 | 95.53 171 | 72.68 327 | 96.85 235 | 88.43 350 | 88.45 139 | 73.14 301 | 90.63 265 | 75.82 189 | 94.38 303 | 92.95 102 | 95.71 125 | 98.48 137 |
|
Test_1112_low_res | | | 92.27 135 | 90.97 146 | 96.18 102 | 95.53 171 | 91.10 108 | 98.47 146 | 94.66 282 | 88.28 148 | 86.83 188 | 93.50 212 | 87.00 85 | 98.65 134 | 84.69 181 | 89.74 191 | 98.80 117 |
|
PCF-MVS | | 89.78 5 | 91.26 154 | 89.63 164 | 96.16 105 | 95.44 173 | 91.58 91 | 95.29 286 | 96.10 212 | 85.07 209 | 82.75 222 | 97.45 115 | 78.28 178 | 99.78 56 | 80.60 227 | 95.65 126 | 97.12 177 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
3Dnovator | | 87.35 11 | 93.17 116 | 91.77 127 | 97.37 42 | 95.41 174 | 93.07 64 | 98.82 102 | 97.85 61 | 91.53 67 | 82.56 226 | 97.58 111 | 71.97 238 | 99.82 50 | 91.01 118 | 99.23 58 | 99.22 86 |
|
IB-MVS | | 89.43 6 | 92.12 141 | 90.83 152 | 95.98 111 | 95.40 175 | 90.78 118 | 99.81 5 | 98.06 47 | 91.23 76 | 85.63 193 | 93.66 207 | 90.63 32 | 98.78 123 | 91.22 115 | 71.85 306 | 98.36 145 |
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 |
1314 | | | 93.44 103 | 91.98 123 | 97.84 22 | 95.24 176 | 94.38 43 | 96.22 260 | 97.92 56 | 90.18 93 | 82.28 231 | 97.71 107 | 77.63 182 | 99.80 54 | 91.94 113 | 98.67 78 | 99.34 74 |
|
XVG-OURS | | | 90.83 163 | 90.49 158 | 91.86 208 | 95.23 177 | 81.25 288 | 95.79 279 | 95.92 223 | 88.96 123 | 90.02 149 | 98.03 99 | 71.60 243 | 99.35 104 | 91.06 117 | 87.78 202 | 94.98 204 |
|
TESTMET0.1,1 | | | 93.82 93 | 93.26 93 | 95.49 128 | 95.21 178 | 90.25 128 | 99.15 63 | 97.54 110 | 89.18 117 | 91.79 118 | 94.87 183 | 89.13 46 | 97.63 181 | 86.21 167 | 96.29 114 | 98.60 129 |
|
xiu_mvs_v1_base_debu | | | 94.73 73 | 93.98 78 | 96.99 55 | 95.19 179 | 95.24 17 | 98.62 125 | 96.50 185 | 92.99 38 | 97.52 35 | 98.83 62 | 72.37 234 | 99.15 111 | 97.03 33 | 96.74 103 | 96.58 196 |
|
xiu_mvs_v1_base | | | 94.73 73 | 93.98 78 | 96.99 55 | 95.19 179 | 95.24 17 | 98.62 125 | 96.50 185 | 92.99 38 | 97.52 35 | 98.83 62 | 72.37 234 | 99.15 111 | 97.03 33 | 96.74 103 | 96.58 196 |
|
xiu_mvs_v1_base_debi | | | 94.73 73 | 93.98 78 | 96.99 55 | 95.19 179 | 95.24 17 | 98.62 125 | 96.50 185 | 92.99 38 | 97.52 35 | 98.83 62 | 72.37 234 | 99.15 111 | 97.03 33 | 96.74 103 | 96.58 196 |
|
XVG-OURS-SEG-HR | | | 90.95 161 | 90.66 156 | 91.83 209 | 95.18 182 | 81.14 290 | 95.92 271 | 95.92 223 | 88.40 143 | 90.33 145 | 97.85 100 | 70.66 250 | 99.38 100 | 92.83 105 | 88.83 198 | 94.98 204 |
|
Effi-MVS+-dtu | | | 89.97 178 | 90.68 155 | 87.81 295 | 95.15 183 | 71.98 329 | 97.87 200 | 95.40 262 | 91.92 61 | 87.57 178 | 91.44 239 | 74.27 210 | 96.84 215 | 89.45 133 | 93.10 143 | 94.60 206 |
|
mvs-test1 | | | 91.57 149 | 92.20 115 | 89.70 258 | 95.15 183 | 74.34 320 | 99.51 25 | 95.40 262 | 91.92 61 | 91.02 131 | 97.25 125 | 74.27 210 | 98.08 154 | 89.45 133 | 95.83 122 | 96.67 189 |
|
Vis-MVSNet | | | 92.64 129 | 91.85 124 | 95.03 144 | 95.12 185 | 88.23 167 | 98.48 144 | 96.81 169 | 91.61 66 | 92.16 117 | 97.22 128 | 71.58 244 | 98.00 158 | 85.85 174 | 97.81 88 | 98.88 111 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
GBi-Net | | | 86.67 229 | 84.96 234 | 91.80 211 | 95.11 186 | 88.81 156 | 96.77 237 | 95.25 269 | 82.94 254 | 82.12 234 | 90.25 278 | 62.89 297 | 94.97 292 | 79.04 236 | 80.24 245 | 91.62 242 |
|
test1 | | | 86.67 229 | 84.96 234 | 91.80 211 | 95.11 186 | 88.81 156 | 96.77 237 | 95.25 269 | 82.94 254 | 82.12 234 | 90.25 278 | 62.89 297 | 94.97 292 | 79.04 236 | 80.24 245 | 91.62 242 |
|
FMVSNet2 | | | 86.90 225 | 84.79 240 | 93.24 185 | 95.11 186 | 92.54 78 | 97.67 206 | 95.86 231 | 82.94 254 | 80.55 248 | 91.17 242 | 62.89 297 | 95.29 287 | 77.23 250 | 79.71 251 | 91.90 236 |
|
MVSFormer | | | 94.71 76 | 94.08 76 | 96.61 86 | 95.05 189 | 94.87 22 | 97.77 203 | 96.17 208 | 86.84 183 | 98.04 24 | 98.52 82 | 85.52 103 | 95.99 267 | 89.83 128 | 98.97 64 | 98.96 103 |
|
lupinMVS | | | 96.32 41 | 95.94 43 | 97.44 36 | 95.05 189 | 94.87 22 | 99.86 2 | 96.50 185 | 93.82 28 | 98.04 24 | 98.77 65 | 85.52 103 | 98.09 151 | 96.98 37 | 98.97 64 | 99.37 71 |
|
CostFormer | | | 92.89 120 | 92.48 108 | 94.12 167 | 94.99 191 | 85.89 231 | 92.89 308 | 97.00 164 | 86.98 180 | 95.00 83 | 90.78 253 | 90.05 39 | 97.51 189 | 92.92 104 | 91.73 164 | 98.96 103 |
|
Patchmatch-test1 | | | 90.10 174 | 88.61 181 | 94.57 154 | 94.95 192 | 88.83 155 | 96.26 256 | 97.21 142 | 90.06 101 | 90.03 148 | 90.68 260 | 66.61 280 | 95.83 274 | 77.31 249 | 94.36 135 | 99.05 94 |
|
test-LLR | | | 93.11 117 | 92.68 103 | 94.40 158 | 94.94 193 | 87.27 192 | 99.15 63 | 97.25 137 | 90.21 91 | 91.57 120 | 94.04 192 | 84.89 114 | 97.58 184 | 85.94 170 | 96.13 115 | 98.36 145 |
|
test-mter | | | 93.27 112 | 92.89 100 | 94.40 158 | 94.94 193 | 87.27 192 | 99.15 63 | 97.25 137 | 88.95 124 | 91.57 120 | 94.04 192 | 88.03 65 | 97.58 184 | 85.94 170 | 96.13 115 | 98.36 145 |
|
tpm2 | | | 91.77 147 | 91.09 138 | 93.82 178 | 94.83 195 | 85.56 240 | 92.51 311 | 97.16 146 | 84.00 225 | 93.83 101 | 90.66 263 | 87.54 71 | 97.17 204 | 87.73 153 | 91.55 167 | 98.72 124 |
|
PVSNet_0 | | 83.28 16 | 87.31 214 | 85.16 232 | 93.74 180 | 94.78 196 | 84.59 252 | 98.91 90 | 98.69 23 | 89.81 102 | 78.59 271 | 93.23 216 | 61.95 301 | 99.34 105 | 94.75 75 | 55.72 345 | 97.30 174 |
|
CDS-MVSNet | | | 93.47 102 | 93.04 98 | 94.76 148 | 94.75 197 | 89.45 149 | 98.82 102 | 97.03 161 | 87.91 158 | 90.97 132 | 96.48 160 | 89.06 47 | 96.36 243 | 89.50 132 | 92.81 147 | 98.49 136 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
gm-plane-assit | | | | | | 94.69 198 | 88.14 168 | | | 88.22 149 | | 97.20 129 | | 98.29 143 | 90.79 122 | | |
|
RPSCF | | | 85.33 251 | 85.55 227 | 84.67 315 | 94.63 199 | 62.28 341 | 93.73 301 | 93.76 298 | 74.38 318 | 85.23 196 | 97.06 138 | 64.09 291 | 98.31 142 | 80.98 218 | 86.08 212 | 93.41 212 |
|
Patchmatch-test | | | 86.25 237 | 84.06 249 | 92.82 193 | 94.42 200 | 82.88 274 | 82.88 347 | 94.23 293 | 71.58 322 | 79.39 262 | 90.62 266 | 89.00 49 | 96.42 238 | 63.03 325 | 91.37 170 | 99.16 88 |
|
VDDNet | | | 90.08 176 | 88.54 186 | 94.69 151 | 94.41 201 | 87.68 177 | 98.21 178 | 96.40 190 | 76.21 311 | 93.33 105 | 97.75 105 | 54.93 323 | 98.77 124 | 94.71 77 | 90.96 172 | 97.61 169 |
|
EI-MVSNet | | | 89.87 179 | 89.38 168 | 91.36 224 | 94.32 202 | 85.87 232 | 97.61 209 | 96.59 177 | 85.10 207 | 85.51 194 | 97.10 135 | 81.30 161 | 96.56 224 | 83.85 193 | 83.03 234 | 91.64 240 |
|
CVMVSNet | | | 90.30 169 | 90.91 148 | 88.46 282 | 94.32 202 | 73.58 324 | 97.61 209 | 97.59 99 | 90.16 96 | 88.43 168 | 97.10 135 | 76.83 186 | 92.86 313 | 82.64 202 | 93.54 141 | 98.93 108 |
|
testpf | | | 80.59 295 | 80.13 282 | 81.97 323 | 94.25 204 | 71.65 330 | 60.37 357 | 95.46 258 | 70.99 324 | 76.97 281 | 87.74 303 | 73.58 220 | 91.67 334 | 76.86 255 | 84.97 218 | 82.60 344 |
|
IterMVS-LS | | | 88.34 203 | 87.44 196 | 91.04 228 | 94.10 205 | 85.85 234 | 98.10 187 | 95.48 256 | 85.12 206 | 82.03 238 | 91.21 241 | 81.35 160 | 95.63 279 | 83.86 192 | 75.73 265 | 91.63 241 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
TAMVS | | | 92.62 130 | 92.09 120 | 94.20 165 | 94.10 205 | 87.68 177 | 98.41 154 | 96.97 165 | 87.53 169 | 89.74 153 | 96.04 169 | 84.77 117 | 96.49 232 | 88.97 144 | 92.31 153 | 98.42 138 |
|
PAPM | | | 96.35 39 | 95.94 43 | 97.58 30 | 94.10 205 | 95.25 16 | 98.93 87 | 98.17 42 | 94.26 20 | 93.94 98 | 98.72 71 | 89.68 43 | 97.88 162 | 96.36 47 | 99.29 54 | 99.62 56 |
|
CLD-MVS | | | 91.06 158 | 90.71 154 | 92.10 205 | 94.05 208 | 86.10 224 | 99.55 22 | 96.29 199 | 94.16 21 | 84.70 198 | 97.17 132 | 69.62 256 | 97.82 166 | 94.74 76 | 86.08 212 | 92.39 218 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
HQP-NCC | | | | | | 93.95 209 | | 99.16 58 | | 93.92 23 | 87.57 178 | | | | | | |
|
ACMP_Plane | | | | | | 93.95 209 | | 99.16 58 | | 93.92 23 | 87.57 178 | | | | | | |
|
HQP-MVS | | | 91.50 150 | 91.23 137 | 92.29 202 | 93.95 209 | 86.39 213 | 99.16 58 | 96.37 191 | 93.92 23 | 87.57 178 | 96.67 152 | 73.34 223 | 97.77 170 | 93.82 89 | 86.29 207 | 92.72 213 |
|
NP-MVS | | | | | | 93.94 212 | 86.22 220 | | | | | 96.67 152 | | | | | |
|
plane_prior6 | | | | | | 93.92 213 | 86.02 229 | | | | | | 72.92 228 | | | | |
|
ACMP | | 87.39 10 | 88.71 200 | 88.24 189 | 90.12 247 | 93.91 214 | 81.06 291 | 98.50 141 | 95.67 240 | 89.43 110 | 80.37 250 | 95.55 174 | 65.67 285 | 97.83 165 | 90.55 124 | 84.51 221 | 91.47 247 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
plane_prior1 | | | | | | 93.90 215 | | | | | | | | | | | |
|
HQP_MVS | | | 91.26 154 | 90.95 147 | 92.16 204 | 93.84 216 | 86.07 226 | 99.02 79 | 96.30 196 | 93.38 36 | 86.99 184 | 96.52 158 | 72.92 228 | 97.75 175 | 93.46 94 | 86.17 210 | 92.67 215 |
|
plane_prior7 | | | | | | 93.84 216 | 85.73 236 | | | | | | | | | | |
|
MVS-HIRNet | | | 79.01 302 | 75.13 309 | 90.66 236 | 93.82 218 | 81.69 282 | 85.16 336 | 93.75 299 | 54.54 349 | 74.17 297 | 59.15 352 | 57.46 313 | 96.58 222 | 63.74 323 | 94.38 134 | 93.72 209 |
|
FMVSNet5 | | | 82.29 276 | 80.54 281 | 87.52 297 | 93.79 219 | 84.01 259 | 93.73 301 | 92.47 322 | 76.92 309 | 74.27 296 | 86.15 317 | 63.69 294 | 89.24 338 | 69.07 310 | 74.79 272 | 89.29 303 |
|
ACMH+ | | 83.78 15 | 84.21 264 | 82.56 268 | 89.15 270 | 93.73 220 | 79.16 299 | 96.43 250 | 94.28 292 | 81.09 276 | 74.00 298 | 94.03 194 | 54.58 324 | 97.67 178 | 76.10 262 | 78.81 253 | 90.63 279 |
|
ACMM | | 86.95 13 | 88.77 198 | 88.22 190 | 90.43 241 | 93.61 221 | 81.34 286 | 98.50 141 | 95.92 223 | 87.88 159 | 83.85 206 | 95.20 180 | 67.20 275 | 97.89 161 | 86.90 164 | 84.90 219 | 92.06 233 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
OpenMVS | | 85.28 14 | 90.75 165 | 88.84 176 | 96.48 90 | 93.58 222 | 93.51 56 | 98.80 104 | 97.41 128 | 82.59 258 | 78.62 269 | 97.49 114 | 68.00 269 | 99.82 50 | 84.52 183 | 98.55 81 | 96.11 201 |
|
IterMVS | | | 85.81 244 | 84.67 242 | 89.22 268 | 93.51 223 | 83.67 264 | 96.32 254 | 94.80 277 | 85.09 208 | 78.69 267 | 90.17 285 | 66.57 281 | 93.17 309 | 79.48 233 | 77.42 260 | 90.81 269 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CR-MVSNet | | | 88.83 195 | 87.38 197 | 93.16 187 | 93.47 224 | 86.24 218 | 84.97 339 | 94.20 294 | 88.92 127 | 90.76 135 | 86.88 313 | 84.43 118 | 94.82 297 | 70.64 308 | 92.17 158 | 98.41 139 |
|
RPMNet | | | 84.62 257 | 81.78 271 | 93.16 187 | 93.47 224 | 86.24 218 | 84.97 339 | 96.28 200 | 64.85 343 | 90.76 135 | 78.80 342 | 80.95 162 | 94.82 297 | 53.76 340 | 92.17 158 | 98.41 139 |
|
semantic-postprocess | | | | | 89.00 273 | 93.46 226 | 82.90 272 | | 94.70 280 | 85.02 210 | 78.62 269 | 90.35 274 | 66.63 279 | 93.33 308 | 79.38 235 | 77.36 261 | 90.76 273 |
|
Fast-Effi-MVS+-dtu | | | 88.84 193 | 88.59 184 | 89.58 261 | 93.44 227 | 78.18 309 | 98.65 121 | 94.62 283 | 88.46 138 | 84.12 204 | 95.37 179 | 68.91 261 | 96.52 230 | 82.06 208 | 91.70 165 | 94.06 207 |
|
Patchmtry | | | 83.61 274 | 81.64 273 | 89.50 263 | 93.36 228 | 82.84 275 | 84.10 342 | 94.20 294 | 69.47 332 | 79.57 260 | 86.88 313 | 84.43 118 | 94.78 299 | 68.48 313 | 74.30 281 | 90.88 268 |
|
LPG-MVS_test | | | 88.86 192 | 88.47 187 | 90.06 248 | 93.35 229 | 80.95 292 | 98.22 175 | 95.94 220 | 87.73 164 | 83.17 212 | 96.11 167 | 66.28 282 | 97.77 170 | 90.19 126 | 85.19 216 | 91.46 248 |
|
LGP-MVS_train | | | | | 90.06 248 | 93.35 229 | 80.95 292 | | 95.94 220 | 87.73 164 | 83.17 212 | 96.11 167 | 66.28 282 | 97.77 170 | 90.19 126 | 85.19 216 | 91.46 248 |
|
JIA-IIPM | | | 85.97 240 | 84.85 238 | 89.33 267 | 93.23 231 | 73.68 323 | 85.05 338 | 97.13 149 | 69.62 331 | 91.56 122 | 68.03 348 | 88.03 65 | 96.96 211 | 77.89 247 | 93.12 142 | 97.34 173 |
|
ACMH | | 83.09 17 | 84.60 258 | 82.61 266 | 90.57 237 | 93.18 232 | 82.94 270 | 96.27 255 | 94.92 276 | 81.01 277 | 72.61 307 | 93.61 208 | 56.54 315 | 97.79 168 | 74.31 282 | 81.07 244 | 90.99 265 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PatchT | | | 85.44 250 | 83.19 254 | 92.22 203 | 93.13 233 | 83.00 269 | 83.80 345 | 96.37 191 | 70.62 325 | 90.55 138 | 79.63 340 | 84.81 116 | 94.87 295 | 58.18 337 | 91.59 166 | 98.79 118 |
|
jason | | | 95.40 62 | 94.86 62 | 97.03 51 | 92.91 234 | 94.23 45 | 99.70 10 | 96.30 196 | 93.56 34 | 96.73 56 | 98.52 82 | 81.46 159 | 97.91 159 | 96.08 53 | 98.47 82 | 98.96 103 |
jason: jason. |
LTVRE_ROB | | 81.71 19 | 84.59 259 | 82.72 264 | 90.18 245 | 92.89 235 | 83.18 268 | 93.15 306 | 94.74 278 | 78.99 290 | 75.14 293 | 92.69 224 | 65.64 286 | 97.63 181 | 69.46 309 | 81.82 242 | 89.74 297 |
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 |
VPA-MVSNet | | | 89.10 188 | 87.66 194 | 93.45 182 | 92.56 236 | 91.02 111 | 97.97 194 | 98.32 28 | 86.92 182 | 86.03 191 | 92.01 231 | 68.84 263 | 97.10 208 | 90.92 119 | 75.34 267 | 92.23 225 |
|
tpm | | | 89.67 181 | 88.95 174 | 91.82 210 | 92.54 237 | 81.43 283 | 92.95 307 | 95.92 223 | 87.81 160 | 90.50 139 | 89.44 291 | 84.99 111 | 95.65 278 | 83.67 194 | 82.71 237 | 98.38 142 |
|
GA-MVS | | | 90.10 174 | 88.69 179 | 94.33 160 | 92.44 238 | 87.97 173 | 99.08 71 | 96.26 201 | 89.65 103 | 86.92 186 | 93.11 219 | 68.09 267 | 96.96 211 | 82.54 204 | 90.15 184 | 98.05 154 |
|
FIs | | | 90.70 166 | 89.87 163 | 93.18 186 | 92.29 239 | 91.12 106 | 98.17 183 | 98.25 30 | 89.11 119 | 83.44 208 | 94.82 184 | 82.26 152 | 96.17 261 | 87.76 152 | 82.76 236 | 92.25 223 |
|
ITE_SJBPF | | | | | 87.93 293 | 92.26 240 | 76.44 315 | | 93.47 304 | 87.67 167 | 79.95 255 | 95.49 177 | 56.50 316 | 97.38 199 | 75.24 275 | 82.33 240 | 89.98 294 |
|
UniMVSNet (Re) | | | 89.50 184 | 88.32 188 | 93.03 189 | 92.21 241 | 90.96 113 | 98.90 95 | 98.39 26 | 89.13 118 | 83.22 209 | 92.03 229 | 81.69 156 | 96.34 250 | 86.79 165 | 72.53 297 | 91.81 237 |
|
UniMVSNet_NR-MVSNet | | | 89.60 182 | 88.55 185 | 92.75 196 | 92.17 242 | 90.07 134 | 98.74 109 | 98.15 44 | 88.37 144 | 83.21 210 | 93.98 197 | 82.86 143 | 95.93 271 | 86.95 162 | 72.47 298 | 92.25 223 |
|
TinyColmap | | | 80.42 297 | 77.94 297 | 87.85 294 | 92.09 243 | 78.58 306 | 93.74 300 | 89.94 345 | 74.99 314 | 69.77 313 | 91.78 235 | 46.09 339 | 97.58 184 | 65.17 322 | 77.89 257 | 87.38 316 |
|
MS-PatchMatch | | | 86.75 227 | 85.92 215 | 89.22 268 | 91.97 244 | 82.47 277 | 96.91 233 | 96.14 211 | 83.74 234 | 77.73 277 | 93.53 211 | 58.19 311 | 97.37 201 | 76.75 257 | 98.35 83 | 87.84 311 |
|
VPNet | | | 88.30 204 | 86.57 206 | 93.49 181 | 91.95 245 | 91.35 101 | 98.18 180 | 97.20 143 | 88.61 133 | 84.52 201 | 94.89 182 | 62.21 300 | 96.76 219 | 89.34 137 | 72.26 302 | 92.36 219 |
|
FMVSNet1 | | | 83.94 270 | 81.32 278 | 91.80 211 | 91.94 246 | 88.81 156 | 96.77 237 | 95.25 269 | 77.98 302 | 78.25 276 | 90.25 278 | 50.37 335 | 94.97 292 | 73.27 295 | 77.81 258 | 91.62 242 |
|
WR-MVS | | | 88.54 202 | 87.22 201 | 92.52 200 | 91.93 247 | 89.50 147 | 98.56 133 | 97.84 62 | 86.99 178 | 81.87 241 | 93.81 201 | 74.25 212 | 95.92 273 | 85.29 175 | 74.43 276 | 92.12 230 |
|
LP | | | 77.80 310 | 74.39 312 | 88.01 291 | 91.93 247 | 79.02 302 | 80.88 349 | 92.90 315 | 65.43 341 | 72.00 308 | 81.29 332 | 65.78 284 | 92.73 322 | 43.76 349 | 75.58 266 | 92.27 222 |
|
FC-MVSNet-test | | | 90.22 171 | 89.40 167 | 92.67 199 | 91.78 249 | 89.86 140 | 97.89 197 | 98.22 32 | 88.81 130 | 82.96 217 | 94.66 186 | 81.90 155 | 95.96 269 | 85.89 172 | 82.52 239 | 92.20 228 |
|
MIMVSNet | | | 84.48 261 | 81.83 270 | 92.42 201 | 91.73 250 | 87.36 189 | 85.52 335 | 94.42 289 | 81.40 273 | 81.91 239 | 87.58 305 | 51.92 331 | 92.81 316 | 73.84 289 | 88.15 200 | 97.08 181 |
|
USDC | | | 84.74 254 | 82.93 256 | 90.16 246 | 91.73 250 | 83.54 265 | 95.00 288 | 93.30 305 | 88.77 131 | 73.19 300 | 93.30 214 | 53.62 327 | 97.65 180 | 75.88 264 | 81.54 243 | 89.30 302 |
|
nrg030 | | | 90.23 170 | 88.87 175 | 94.32 161 | 91.53 252 | 93.54 55 | 98.79 107 | 95.89 229 | 88.12 152 | 84.55 200 | 94.61 188 | 78.80 175 | 96.88 214 | 92.35 109 | 75.21 268 | 92.53 217 |
|
DU-MVS | | | 88.83 195 | 87.51 195 | 92.79 194 | 91.46 253 | 90.07 134 | 98.71 110 | 97.62 96 | 88.87 129 | 83.21 210 | 93.68 205 | 74.63 198 | 95.93 271 | 86.95 162 | 72.47 298 | 92.36 219 |
|
NR-MVSNet | | | 87.74 209 | 86.00 214 | 92.96 191 | 91.46 253 | 90.68 122 | 96.65 244 | 97.42 127 | 88.02 153 | 73.42 299 | 93.68 205 | 77.31 183 | 95.83 274 | 84.26 184 | 71.82 307 | 92.36 219 |
|
tfpnnormal | | | 83.65 272 | 81.35 277 | 90.56 238 | 91.37 255 | 88.06 170 | 97.29 216 | 97.87 60 | 78.51 296 | 76.20 284 | 90.91 249 | 64.78 289 | 96.47 235 | 61.71 328 | 73.50 290 | 87.13 321 |
|
test_0402 | | | 78.81 304 | 76.33 306 | 86.26 305 | 91.18 256 | 78.44 308 | 95.88 274 | 91.34 336 | 68.55 333 | 70.51 311 | 89.91 286 | 52.65 330 | 94.99 291 | 47.14 345 | 79.78 250 | 85.34 338 |
|
test0.0.03 1 | | | 88.96 190 | 88.61 181 | 90.03 251 | 91.09 257 | 84.43 254 | 98.97 85 | 97.02 162 | 90.21 91 | 80.29 251 | 96.31 166 | 84.89 114 | 91.93 333 | 72.98 299 | 85.70 215 | 93.73 208 |
|
WR-MVS_H | | | 86.53 233 | 85.49 228 | 89.66 260 | 91.04 258 | 83.31 267 | 97.53 211 | 98.20 33 | 84.95 212 | 79.64 258 | 90.90 250 | 78.01 180 | 95.33 286 | 76.29 261 | 72.81 294 | 90.35 283 |
|
CP-MVSNet | | | 86.54 232 | 85.45 229 | 89.79 256 | 91.02 259 | 82.78 276 | 97.38 214 | 97.56 106 | 85.37 203 | 79.53 261 | 93.03 220 | 71.86 240 | 95.25 288 | 79.92 229 | 73.43 292 | 91.34 251 |
|
TranMVSNet+NR-MVSNet | | | 87.75 207 | 86.31 210 | 92.07 206 | 90.81 260 | 88.56 162 | 98.33 160 | 97.18 144 | 87.76 161 | 81.87 241 | 93.90 199 | 72.45 232 | 95.43 283 | 83.13 197 | 71.30 310 | 92.23 225 |
|
PS-CasMVS | | | 85.81 244 | 84.58 243 | 89.49 265 | 90.77 261 | 82.11 279 | 97.20 223 | 97.36 133 | 84.83 214 | 79.12 266 | 92.84 223 | 67.42 274 | 95.16 290 | 78.39 243 | 73.25 293 | 91.21 255 |
|
DeepMVS_CX | | | | | 76.08 330 | 90.74 262 | 51.65 353 | | 90.84 338 | 86.47 191 | 57.89 342 | 87.98 301 | 35.88 351 | 92.60 324 | 65.77 321 | 65.06 323 | 83.97 340 |
|
Anonymous20240521 | | | 85.45 249 | 83.91 252 | 90.05 250 | 90.73 263 | 83.74 263 | 97.13 226 | 96.15 210 | 82.08 266 | 76.93 282 | 90.84 252 | 71.53 246 | 96.36 243 | 75.26 274 | 74.57 274 | 90.04 293 |
|
OPM-MVS | | | 89.76 180 | 89.15 171 | 91.57 219 | 90.53 264 | 85.58 239 | 98.11 186 | 95.93 222 | 92.88 43 | 86.05 190 | 96.47 161 | 67.06 277 | 97.87 163 | 89.29 140 | 86.08 212 | 91.26 254 |
|
XXY-MVS | | | 87.75 207 | 86.02 213 | 92.95 192 | 90.46 265 | 89.70 143 | 97.71 205 | 95.90 227 | 84.02 224 | 80.95 246 | 94.05 191 | 67.51 273 | 97.10 208 | 85.16 176 | 78.41 254 | 92.04 234 |
|
v1neww | | | 87.29 215 | 85.88 216 | 91.50 220 | 90.07 266 | 86.87 197 | 98.45 147 | 95.66 243 | 83.84 231 | 83.07 215 | 90.99 245 | 74.58 202 | 96.56 224 | 81.96 211 | 74.33 279 | 91.07 261 |
|
v7new | | | 87.29 215 | 85.88 216 | 91.50 220 | 90.07 266 | 86.87 197 | 98.45 147 | 95.66 243 | 83.84 231 | 83.07 215 | 90.99 245 | 74.58 202 | 96.56 224 | 81.96 211 | 74.33 279 | 91.07 261 |
|
v7 | | | 86.91 224 | 85.45 229 | 91.29 225 | 90.06 268 | 86.73 203 | 98.26 171 | 95.49 255 | 83.08 251 | 82.95 218 | 90.96 248 | 73.37 221 | 96.42 238 | 79.90 230 | 74.97 269 | 90.71 276 |
|
v18 | | | 82.00 278 | 79.76 286 | 88.72 276 | 90.03 269 | 86.81 202 | 96.17 265 | 93.12 306 | 78.70 293 | 68.39 316 | 82.10 322 | 74.64 196 | 93.00 310 | 74.21 283 | 60.45 333 | 86.35 325 |
|
v10 | | | 85.73 247 | 84.01 250 | 90.87 233 | 90.03 269 | 86.73 203 | 97.20 223 | 95.22 274 | 81.25 275 | 79.85 257 | 89.75 288 | 73.30 226 | 96.28 258 | 76.87 254 | 72.64 296 | 89.61 300 |
|
v16 | | | 81.90 281 | 79.65 287 | 88.65 277 | 90.02 271 | 86.66 206 | 96.01 269 | 93.07 308 | 78.53 295 | 68.27 318 | 82.05 323 | 74.39 208 | 92.96 311 | 74.02 287 | 60.48 332 | 86.33 327 |
|
v8 | | | 86.11 238 | 84.45 244 | 91.10 227 | 89.99 272 | 86.85 199 | 97.24 220 | 95.36 264 | 81.99 267 | 79.89 256 | 89.86 287 | 74.53 204 | 96.39 241 | 78.83 240 | 72.32 300 | 90.05 291 |
|
v6 | | | 87.27 217 | 85.86 218 | 91.50 220 | 89.97 273 | 86.84 201 | 98.45 147 | 95.67 240 | 83.85 230 | 83.11 214 | 90.97 247 | 74.46 205 | 96.58 222 | 81.97 210 | 74.34 278 | 91.09 258 |
|
v17 | | | 81.87 283 | 79.61 288 | 88.64 278 | 89.91 274 | 86.64 207 | 96.01 269 | 93.08 307 | 78.54 294 | 68.27 318 | 81.96 324 | 74.44 206 | 92.95 312 | 74.03 286 | 60.22 335 | 86.34 326 |
|
V42 | | | 87.00 223 | 85.68 225 | 90.98 230 | 89.91 274 | 86.08 225 | 98.32 162 | 95.61 248 | 83.67 237 | 82.72 223 | 90.67 261 | 74.00 216 | 96.53 228 | 81.94 213 | 74.28 282 | 90.32 284 |
|
XVG-ACMP-BASELINE | | | 85.86 242 | 84.95 236 | 88.57 279 | 89.90 276 | 77.12 314 | 94.30 294 | 95.60 249 | 87.40 171 | 82.12 234 | 92.99 222 | 53.42 328 | 97.66 179 | 85.02 178 | 83.83 227 | 90.92 267 |
|
PEN-MVS | | | 85.21 252 | 83.93 251 | 89.07 272 | 89.89 277 | 81.31 287 | 97.09 228 | 97.24 139 | 84.45 219 | 78.66 268 | 92.68 225 | 68.44 265 | 94.87 295 | 75.98 263 | 70.92 311 | 91.04 264 |
|
v1141 | | | 87.23 219 | 85.75 222 | 91.67 216 | 89.88 278 | 87.43 185 | 98.52 137 | 95.62 246 | 83.91 227 | 82.83 221 | 90.69 258 | 74.70 195 | 96.49 232 | 81.53 217 | 74.08 286 | 91.07 261 |
|
divwei89l23v2f112 | | | 87.23 219 | 85.75 222 | 91.66 217 | 89.88 278 | 87.40 186 | 98.53 136 | 95.62 246 | 83.91 227 | 82.84 220 | 90.67 261 | 74.75 194 | 96.49 232 | 81.55 215 | 74.05 288 | 91.08 259 |
|
v1 | | | 87.23 219 | 85.76 220 | 91.66 217 | 89.88 278 | 87.37 188 | 98.54 135 | 95.64 245 | 83.91 227 | 82.88 219 | 90.70 256 | 74.64 196 | 96.53 228 | 81.54 216 | 74.08 286 | 91.08 259 |
|
v15 | | | 81.62 284 | 79.32 291 | 88.52 280 | 89.80 281 | 86.56 208 | 95.83 278 | 92.96 311 | 78.50 297 | 67.88 322 | 81.68 326 | 74.22 213 | 92.82 315 | 73.46 293 | 59.55 336 | 86.18 330 |
|
V14 | | | 81.55 286 | 79.26 292 | 88.42 283 | 89.80 281 | 86.33 216 | 95.72 281 | 92.96 311 | 78.35 298 | 67.82 323 | 81.70 325 | 74.13 214 | 92.78 319 | 73.32 294 | 59.50 338 | 86.16 332 |
|
v1144 | | | 86.83 226 | 85.31 231 | 91.40 223 | 89.75 283 | 87.21 194 | 98.31 163 | 95.45 259 | 83.22 248 | 82.70 224 | 90.78 253 | 73.36 222 | 96.36 243 | 79.49 232 | 74.69 273 | 90.63 279 |
|
V9 | | | 81.46 287 | 79.15 293 | 88.39 286 | 89.75 283 | 86.17 222 | 95.62 282 | 92.92 313 | 78.22 299 | 67.65 327 | 81.64 327 | 73.95 217 | 92.80 317 | 73.15 297 | 59.43 341 | 86.21 329 |
|
TransMVSNet (Re) | | | 81.97 279 | 79.61 288 | 89.08 271 | 89.70 285 | 84.01 259 | 97.26 218 | 91.85 331 | 78.84 291 | 73.07 303 | 91.62 237 | 67.17 276 | 95.21 289 | 67.50 314 | 59.46 340 | 88.02 310 |
|
v12 | | | 81.37 289 | 79.05 294 | 88.33 287 | 89.68 286 | 86.05 228 | 95.48 284 | 92.92 313 | 78.08 300 | 67.55 328 | 81.58 328 | 73.75 218 | 92.75 320 | 73.05 298 | 59.37 342 | 86.18 330 |
|
v11 | | | 81.38 288 | 79.03 295 | 88.41 284 | 89.68 286 | 86.43 210 | 95.74 280 | 92.82 320 | 78.03 301 | 67.74 324 | 81.45 330 | 73.33 225 | 92.69 323 | 72.23 304 | 60.27 334 | 86.11 334 |
|
v13 | | | 81.30 290 | 78.99 296 | 88.25 288 | 89.61 288 | 85.87 232 | 95.39 285 | 92.90 315 | 77.93 306 | 67.45 331 | 81.52 329 | 73.66 219 | 92.75 320 | 72.91 300 | 59.53 337 | 86.14 333 |
|
v2v482 | | | 87.27 217 | 85.76 220 | 91.78 215 | 89.59 289 | 87.58 179 | 98.56 133 | 95.54 251 | 84.53 217 | 82.51 227 | 91.78 235 | 73.11 227 | 96.47 235 | 82.07 207 | 74.14 285 | 91.30 253 |
|
pm-mvs1 | | | 84.68 256 | 82.78 262 | 90.40 242 | 89.58 290 | 85.18 244 | 97.31 215 | 94.73 279 | 81.93 269 | 76.05 286 | 92.01 231 | 65.48 287 | 96.11 264 | 78.75 241 | 69.14 314 | 89.91 295 |
|
pmmvs4 | | | 87.58 212 | 86.17 212 | 91.80 211 | 89.58 290 | 88.92 154 | 97.25 219 | 95.28 268 | 82.54 260 | 80.49 249 | 93.17 218 | 75.62 191 | 96.05 266 | 82.75 201 | 78.90 252 | 90.42 282 |
|
v1192 | | | 86.32 236 | 84.71 241 | 91.17 226 | 89.53 292 | 86.40 212 | 98.13 184 | 95.44 260 | 82.52 261 | 82.42 229 | 90.62 266 | 71.58 244 | 96.33 251 | 77.23 250 | 74.88 270 | 90.79 271 |
|
pcd1.5k->3k | | | 35.91 338 | 37.64 338 | 30.74 350 | 89.49 293 | 0.00 369 | 0.00 360 | 96.36 194 | 0.00 364 | 0.00 366 | 0.00 366 | 69.17 260 | 0.00 366 | 0.00 363 | 83.71 229 | 92.21 227 |
|
v144192 | | | 86.40 234 | 84.89 237 | 90.91 231 | 89.48 294 | 85.59 238 | 98.21 178 | 95.43 261 | 82.45 262 | 82.62 225 | 90.58 269 | 72.79 231 | 96.36 243 | 78.45 242 | 74.04 289 | 90.79 271 |
|
v148 | | | 86.38 235 | 85.06 233 | 90.37 243 | 89.47 295 | 84.10 258 | 98.52 137 | 95.48 256 | 83.80 233 | 80.93 247 | 90.22 281 | 74.60 200 | 96.31 254 | 80.92 220 | 71.55 308 | 90.69 277 |
|
v1921920 | | | 86.02 239 | 84.44 245 | 90.77 234 | 89.32 296 | 85.20 243 | 98.10 187 | 95.35 266 | 82.19 264 | 82.25 232 | 90.71 255 | 70.73 248 | 96.30 257 | 76.85 256 | 74.49 275 | 90.80 270 |
|
v1240 | | | 85.77 246 | 84.11 248 | 90.73 235 | 89.26 297 | 85.15 246 | 97.88 199 | 95.23 273 | 81.89 270 | 82.16 233 | 90.55 271 | 69.60 257 | 96.31 254 | 75.59 273 | 74.87 271 | 90.72 275 |
|
DI_MVS_plusplus_test | | | 89.41 185 | 87.24 200 | 95.92 114 | 89.06 298 | 90.75 121 | 98.18 180 | 96.63 174 | 89.29 113 | 70.54 310 | 90.31 276 | 63.50 295 | 98.40 140 | 92.25 110 | 95.44 127 | 98.60 129 |
|
our_test_3 | | | 84.47 262 | 82.80 260 | 89.50 263 | 89.01 299 | 83.90 261 | 97.03 230 | 94.56 284 | 81.33 274 | 75.36 292 | 90.52 272 | 71.69 242 | 94.54 302 | 68.81 311 | 76.84 262 | 90.07 289 |
|
ppachtmachnet_test | | | 83.63 273 | 81.57 275 | 89.80 255 | 89.01 299 | 85.09 247 | 97.13 226 | 94.50 285 | 78.84 291 | 76.14 285 | 91.00 244 | 69.78 254 | 94.61 301 | 63.40 324 | 74.36 277 | 89.71 299 |
|
DTE-MVSNet | | | 84.14 268 | 82.80 260 | 88.14 289 | 88.95 301 | 79.87 298 | 96.81 236 | 96.24 202 | 83.50 245 | 77.60 279 | 92.52 227 | 67.89 271 | 94.24 304 | 72.64 302 | 69.05 315 | 90.32 284 |
|
test_normal | | | 89.37 186 | 87.18 202 | 95.93 113 | 88.94 302 | 90.83 117 | 98.24 173 | 96.62 175 | 89.31 111 | 70.38 312 | 90.20 283 | 63.50 295 | 98.37 141 | 92.06 112 | 95.41 128 | 98.59 132 |
|
PS-MVSNAJss | | | 89.54 183 | 89.05 172 | 91.00 229 | 88.77 303 | 84.36 255 | 97.39 212 | 95.97 216 | 88.47 136 | 81.88 240 | 93.80 202 | 82.48 148 | 96.50 231 | 89.34 137 | 83.34 232 | 92.15 229 |
|
Baseline_NR-MVSNet | | | 85.83 243 | 84.82 239 | 88.87 275 | 88.73 304 | 83.34 266 | 98.63 124 | 91.66 332 | 80.41 282 | 82.44 228 | 91.35 240 | 74.63 198 | 95.42 284 | 84.13 186 | 71.39 309 | 87.84 311 |
|
MVP-Stereo | | | 86.61 231 | 85.83 219 | 88.93 274 | 88.70 305 | 83.85 262 | 96.07 267 | 94.41 290 | 82.15 265 | 75.64 290 | 91.96 233 | 67.65 272 | 96.45 237 | 77.20 252 | 98.72 75 | 86.51 324 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
EU-MVSNet | | | 84.19 266 | 84.42 246 | 83.52 318 | 88.64 306 | 67.37 338 | 96.04 268 | 95.76 233 | 85.29 204 | 78.44 274 | 93.18 217 | 70.67 249 | 91.48 336 | 75.79 271 | 75.98 263 | 91.70 239 |
|
pmmvs5 | | | 85.87 241 | 84.40 247 | 90.30 244 | 88.53 307 | 84.23 256 | 98.60 129 | 93.71 300 | 81.53 272 | 80.29 251 | 92.02 230 | 64.51 290 | 95.52 281 | 82.04 209 | 78.34 255 | 91.15 256 |
|
MDA-MVSNet-bldmvs | | | 77.82 309 | 74.75 311 | 87.03 301 | 88.33 308 | 78.52 307 | 96.34 253 | 92.85 317 | 75.57 313 | 48.87 349 | 87.89 302 | 57.32 314 | 92.49 327 | 60.79 330 | 64.80 324 | 90.08 288 |
|
N_pmnet | | | 70.19 320 | 69.87 319 | 71.12 334 | 88.24 309 | 30.63 365 | 95.85 277 | 28.70 367 | 70.18 329 | 68.73 315 | 86.55 315 | 64.04 292 | 93.81 305 | 53.12 341 | 73.46 291 | 88.94 306 |
|
v7n | | | 84.42 263 | 82.75 263 | 89.43 266 | 88.15 310 | 81.86 280 | 96.75 240 | 95.67 240 | 80.53 280 | 78.38 275 | 89.43 292 | 69.89 252 | 96.35 249 | 73.83 290 | 72.13 304 | 90.07 289 |
|
SixPastTwentyTwo | | | 82.63 275 | 81.58 274 | 85.79 308 | 88.12 311 | 71.01 332 | 95.17 287 | 92.54 321 | 84.33 221 | 72.93 304 | 92.08 228 | 60.41 307 | 95.61 280 | 74.47 281 | 74.15 284 | 90.75 274 |
|
test_djsdf | | | 88.26 206 | 87.73 192 | 89.84 254 | 88.05 312 | 82.21 278 | 97.77 203 | 96.17 208 | 86.84 183 | 82.41 230 | 91.95 234 | 72.07 237 | 95.99 267 | 89.83 128 | 84.50 222 | 91.32 252 |
|
mvs_tets | | | 87.09 222 | 86.22 211 | 89.71 257 | 87.87 313 | 81.39 285 | 96.73 241 | 95.90 227 | 88.19 150 | 79.99 254 | 93.61 208 | 59.96 308 | 96.31 254 | 89.40 136 | 84.34 224 | 91.43 250 |
|
OurMVSNet-221017-0 | | | 84.13 269 | 83.59 253 | 85.77 309 | 87.81 314 | 70.24 333 | 94.89 289 | 93.65 302 | 86.08 194 | 76.53 283 | 93.28 215 | 61.41 303 | 96.14 263 | 80.95 219 | 77.69 259 | 90.93 266 |
|
YYNet1 | | | 79.64 301 | 77.04 303 | 87.43 299 | 87.80 315 | 79.98 295 | 96.23 258 | 94.44 287 | 73.83 320 | 51.83 346 | 87.53 307 | 67.96 270 | 92.07 332 | 66.00 320 | 67.75 320 | 90.23 286 |
|
MDA-MVSNet_test_wron | | | 79.65 300 | 77.05 302 | 87.45 298 | 87.79 316 | 80.13 294 | 96.25 257 | 94.44 287 | 73.87 319 | 51.80 347 | 87.47 308 | 68.04 268 | 92.12 331 | 66.02 319 | 67.79 319 | 90.09 287 |
|
jajsoiax | | | 87.35 213 | 86.51 208 | 89.87 252 | 87.75 317 | 81.74 281 | 97.03 230 | 95.98 215 | 88.47 136 | 80.15 253 | 93.80 202 | 61.47 302 | 96.36 243 | 89.44 135 | 84.47 223 | 91.50 246 |
|
v748 | | | 83.84 271 | 82.31 269 | 88.41 284 | 87.65 318 | 79.10 301 | 96.66 243 | 95.51 253 | 80.09 283 | 77.65 278 | 88.53 300 | 69.81 253 | 96.23 259 | 75.67 272 | 69.25 313 | 89.91 295 |
|
v52 | | | 84.19 266 | 82.92 257 | 88.01 291 | 87.64 319 | 79.92 296 | 96.23 258 | 95.32 267 | 79.87 285 | 78.51 272 | 89.05 295 | 69.50 259 | 96.32 252 | 77.95 246 | 72.24 303 | 87.79 314 |
|
V4 | | | 84.20 265 | 82.92 257 | 88.02 290 | 87.59 320 | 79.91 297 | 96.21 263 | 95.36 264 | 79.88 284 | 78.51 272 | 89.00 296 | 69.52 258 | 96.32 252 | 77.96 245 | 72.29 301 | 87.83 313 |
|
K. test v3 | | | 81.04 291 | 79.77 285 | 84.83 313 | 87.41 321 | 70.23 334 | 95.60 283 | 93.93 297 | 83.70 236 | 67.51 329 | 89.35 293 | 55.76 317 | 93.58 307 | 76.67 258 | 68.03 318 | 90.67 278 |
|
testgi | | | 82.29 276 | 81.00 280 | 86.17 306 | 87.24 322 | 74.84 319 | 97.39 212 | 91.62 333 | 88.63 132 | 75.85 289 | 95.42 178 | 46.07 340 | 91.55 335 | 66.87 318 | 79.94 248 | 92.12 230 |
|
LF4IMVS | | | 81.94 280 | 81.17 279 | 84.25 316 | 87.23 323 | 68.87 337 | 93.35 305 | 91.93 330 | 83.35 247 | 75.40 291 | 93.00 221 | 49.25 337 | 96.65 220 | 78.88 239 | 78.11 256 | 87.22 320 |
|
EG-PatchMatch MVS | | | 79.92 298 | 77.59 298 | 86.90 302 | 87.06 324 | 77.90 313 | 96.20 264 | 94.06 296 | 74.61 316 | 66.53 333 | 88.76 298 | 40.40 348 | 96.20 260 | 67.02 316 | 83.66 230 | 86.61 322 |
|
Gipuma | | | 54.77 328 | 52.22 330 | 62.40 341 | 86.50 325 | 59.37 344 | 50.20 358 | 90.35 342 | 36.52 354 | 41.20 353 | 49.49 356 | 18.33 358 | 81.29 351 | 32.10 355 | 65.34 322 | 46.54 357 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
anonymousdsp | | | 86.69 228 | 85.75 222 | 89.53 262 | 86.46 326 | 82.94 270 | 96.39 251 | 95.71 237 | 83.97 226 | 79.63 259 | 90.70 256 | 68.85 262 | 95.94 270 | 86.01 168 | 84.02 226 | 89.72 298 |
|
lessismore_v0 | | | | | 85.08 311 | 85.59 327 | 69.28 336 | | 90.56 340 | | 67.68 326 | 90.21 282 | 54.21 326 | 95.46 282 | 73.88 288 | 62.64 327 | 90.50 281 |
|
CMPMVS | | 58.40 21 | 80.48 296 | 80.11 284 | 81.59 325 | 85.10 328 | 59.56 343 | 94.14 297 | 95.95 219 | 68.54 334 | 60.71 339 | 93.31 213 | 55.35 321 | 97.87 163 | 83.06 198 | 84.85 220 | 87.33 317 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
Anonymous20231206 | | | 80.76 294 | 79.42 290 | 84.79 314 | 84.78 329 | 72.98 325 | 96.53 247 | 92.97 310 | 79.56 287 | 74.33 295 | 88.83 297 | 61.27 304 | 92.15 330 | 60.59 331 | 75.92 264 | 89.24 304 |
|
Test4 | | | 85.71 248 | 82.59 267 | 95.07 141 | 84.45 330 | 89.84 141 | 97.20 223 | 95.73 235 | 89.19 115 | 64.59 335 | 87.58 305 | 40.59 347 | 96.77 218 | 88.95 145 | 95.01 131 | 98.60 129 |
|
DSMNet-mixed | | | 81.60 285 | 81.43 276 | 82.10 321 | 84.36 331 | 60.79 342 | 93.63 303 | 86.74 352 | 79.00 289 | 79.32 263 | 87.15 311 | 63.87 293 | 89.78 337 | 66.89 317 | 91.92 160 | 95.73 202 |
|
pmmvs6 | | | 79.90 299 | 77.31 300 | 87.67 296 | 84.17 332 | 78.13 310 | 95.86 276 | 93.68 301 | 67.94 336 | 72.67 306 | 89.62 290 | 50.98 334 | 95.75 276 | 74.80 280 | 66.04 321 | 89.14 305 |
|
new_pmnet | | | 76.02 312 | 73.71 313 | 82.95 319 | 83.88 333 | 72.85 326 | 91.26 321 | 92.26 324 | 70.44 327 | 62.60 337 | 81.37 331 | 47.64 338 | 92.32 328 | 61.85 327 | 72.10 305 | 83.68 341 |
|
OpenMVS_ROB | | 73.86 20 | 77.99 308 | 75.06 310 | 86.77 303 | 83.81 334 | 77.94 312 | 96.38 252 | 91.53 335 | 67.54 337 | 68.38 317 | 87.13 312 | 43.94 341 | 96.08 265 | 55.03 339 | 81.83 241 | 86.29 328 |
|
test20.03 | | | 78.51 306 | 77.48 299 | 81.62 324 | 83.07 335 | 71.03 331 | 96.11 266 | 92.83 318 | 81.66 271 | 69.31 314 | 89.68 289 | 57.53 312 | 87.29 342 | 58.65 336 | 68.47 316 | 86.53 323 |
|
UnsupCasMVSNet_eth | | | 78.90 303 | 76.67 305 | 85.58 310 | 82.81 336 | 74.94 318 | 91.98 315 | 96.31 195 | 84.64 216 | 65.84 334 | 87.71 304 | 51.33 332 | 92.23 329 | 72.89 301 | 56.50 344 | 89.56 301 |
|
MIMVSNet1 | | | 75.92 313 | 73.30 314 | 83.81 317 | 81.29 337 | 75.57 317 | 92.26 314 | 92.05 328 | 73.09 321 | 67.48 330 | 86.18 316 | 40.87 346 | 87.64 341 | 55.78 338 | 70.68 312 | 88.21 307 |
|
test2356 | | | 80.96 292 | 81.77 272 | 78.52 328 | 81.02 338 | 62.33 340 | 98.22 175 | 94.49 286 | 79.38 288 | 74.56 294 | 90.34 275 | 70.65 251 | 85.10 346 | 60.83 329 | 86.42 206 | 88.14 308 |
|
Patchmatch-RL test | | | 81.90 281 | 80.13 282 | 87.23 300 | 80.71 339 | 70.12 335 | 84.07 343 | 88.19 351 | 83.16 250 | 70.57 309 | 82.18 321 | 87.18 81 | 92.59 325 | 82.28 206 | 62.78 326 | 98.98 101 |
|
pmmvs-eth3d | | | 78.71 305 | 76.16 307 | 86.38 304 | 80.25 340 | 81.19 289 | 94.17 296 | 92.13 327 | 77.97 303 | 66.90 332 | 82.31 320 | 55.76 317 | 92.56 326 | 73.63 292 | 62.31 329 | 85.38 336 |
|
testus | | | 77.11 311 | 76.95 304 | 77.58 329 | 80.02 341 | 58.93 345 | 97.78 201 | 90.48 341 | 79.68 286 | 72.84 305 | 90.61 268 | 37.72 350 | 86.57 345 | 60.28 333 | 83.18 233 | 87.23 319 |
|
UnsupCasMVSNet_bld | | | 73.85 316 | 70.14 318 | 84.99 312 | 79.44 342 | 75.73 316 | 88.53 330 | 95.24 272 | 70.12 330 | 61.94 338 | 74.81 344 | 41.41 345 | 93.62 306 | 68.65 312 | 51.13 350 | 85.62 335 |
|
PM-MVS | | | 74.88 314 | 72.85 315 | 80.98 326 | 78.98 343 | 64.75 339 | 90.81 324 | 85.77 354 | 80.95 278 | 68.23 321 | 82.81 319 | 29.08 353 | 92.84 314 | 76.54 260 | 62.46 328 | 85.36 337 |
|
testing_2 | | | 80.92 293 | 77.24 301 | 91.98 207 | 78.88 344 | 87.83 174 | 93.96 299 | 95.72 236 | 84.27 222 | 56.20 344 | 80.42 335 | 38.64 349 | 96.40 240 | 87.20 157 | 79.85 249 | 91.72 238 |
|
new-patchmatchnet | | | 74.80 315 | 72.40 316 | 81.99 322 | 78.36 345 | 72.20 328 | 94.44 291 | 92.36 323 | 77.06 308 | 63.47 336 | 79.98 339 | 51.04 333 | 88.85 339 | 60.53 332 | 54.35 346 | 84.92 339 |
|
pmmvs3 | | | 72.86 317 | 69.76 320 | 82.17 320 | 73.86 346 | 74.19 321 | 94.20 295 | 89.01 348 | 64.23 344 | 67.72 325 | 80.91 334 | 41.48 344 | 88.65 340 | 62.40 326 | 54.02 347 | 83.68 341 |
|
1111 | | | 72.28 318 | 71.36 317 | 75.02 332 | 73.04 347 | 57.38 347 | 92.30 312 | 90.22 343 | 62.27 345 | 59.46 340 | 80.36 336 | 76.23 187 | 87.07 343 | 44.29 347 | 64.08 325 | 80.59 345 |
|
.test1245 | | | 61.50 323 | 64.44 323 | 52.65 347 | 73.04 347 | 57.38 347 | 92.30 312 | 90.22 343 | 62.27 345 | 59.46 340 | 80.36 336 | 76.23 187 | 87.07 343 | 44.29 347 | 1.80 361 | 13.50 361 |
|
ambc | | | | | 79.60 327 | 72.76 349 | 56.61 349 | 76.20 351 | 92.01 329 | | 68.25 320 | 80.23 338 | 23.34 354 | 94.73 300 | 73.78 291 | 60.81 331 | 87.48 315 |
|
test1235678 | | | 71.07 319 | 69.53 321 | 75.71 331 | 71.87 350 | 55.27 351 | 94.32 292 | 90.76 339 | 70.23 328 | 57.61 343 | 79.06 341 | 43.13 342 | 83.72 348 | 50.48 342 | 68.30 317 | 88.14 308 |
|
TDRefinement | | | 78.01 307 | 75.31 308 | 86.10 307 | 70.06 351 | 73.84 322 | 93.59 304 | 91.58 334 | 74.51 317 | 73.08 302 | 91.04 243 | 49.63 336 | 97.12 205 | 74.88 278 | 59.47 339 | 87.33 317 |
|
test12356 | | | 66.36 321 | 65.12 322 | 70.08 337 | 66.92 352 | 50.46 354 | 89.96 328 | 88.58 349 | 66.00 340 | 53.38 345 | 78.13 343 | 32.89 352 | 82.87 349 | 48.36 344 | 61.87 330 | 76.92 346 |
|
PMMVS2 | | | 58.97 326 | 55.07 327 | 70.69 336 | 62.72 353 | 55.37 350 | 85.97 334 | 80.52 358 | 49.48 350 | 45.94 350 | 68.31 347 | 15.73 361 | 80.78 352 | 49.79 343 | 37.12 351 | 75.91 348 |
|
E-PMN | | | 41.02 335 | 40.93 335 | 41.29 348 | 61.97 354 | 33.83 362 | 84.00 344 | 65.17 365 | 27.17 357 | 27.56 356 | 46.72 358 | 17.63 360 | 60.41 361 | 19.32 358 | 18.82 356 | 29.61 358 |
|
PNet_i23d | | | 48.05 331 | 44.98 333 | 57.28 343 | 60.15 355 | 42.39 360 | 80.85 350 | 73.14 363 | 36.78 353 | 27.46 357 | 56.66 353 | 6.38 364 | 68.34 357 | 36.65 353 | 26.72 353 | 61.10 353 |
|
wuyk23d | | | 16.71 341 | 16.73 343 | 16.65 351 | 60.15 355 | 25.22 366 | 41.24 359 | 5.17 368 | 6.56 361 | 5.48 365 | 3.61 365 | 3.64 366 | 22.72 363 | 15.20 360 | 9.52 360 | 1.99 363 |
|
FPMVS | | | 61.57 322 | 60.32 324 | 65.34 339 | 60.14 357 | 42.44 359 | 91.02 323 | 89.72 346 | 44.15 351 | 42.63 352 | 80.93 333 | 19.02 356 | 80.59 353 | 42.50 350 | 72.76 295 | 73.00 349 |
|
EMVS | | | 39.96 337 | 39.88 336 | 40.18 349 | 59.57 358 | 32.12 364 | 84.79 341 | 64.57 366 | 26.27 358 | 26.14 359 | 44.18 361 | 18.73 357 | 59.29 362 | 17.03 359 | 17.67 358 | 29.12 359 |
|
no-one | | | 56.69 327 | 51.89 331 | 71.08 335 | 59.35 359 | 58.65 346 | 83.78 346 | 84.81 357 | 61.73 347 | 36.46 355 | 56.52 354 | 18.15 359 | 84.78 347 | 47.03 346 | 19.19 355 | 69.81 351 |
|
testmv | | | 60.41 324 | 57.98 325 | 67.69 338 | 58.16 360 | 47.14 356 | 89.09 329 | 86.74 352 | 61.52 348 | 44.30 351 | 68.44 346 | 20.98 355 | 79.92 354 | 40.94 351 | 51.67 348 | 76.01 347 |
|
LCM-MVSNet | | | 60.07 325 | 56.37 326 | 71.18 333 | 54.81 361 | 48.67 355 | 82.17 348 | 89.48 347 | 37.95 352 | 49.13 348 | 69.12 345 | 13.75 363 | 81.76 350 | 59.28 334 | 51.63 349 | 83.10 343 |
|
MVE | | 44.00 22 | 41.70 334 | 37.64 338 | 53.90 346 | 49.46 362 | 43.37 358 | 65.09 356 | 66.66 364 | 26.19 359 | 25.77 360 | 48.53 357 | 3.58 368 | 63.35 360 | 26.15 357 | 27.28 352 | 54.97 356 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuykxyi23d | | | 43.53 333 | 37.95 337 | 60.27 342 | 45.36 363 | 44.79 357 | 68.27 354 | 74.26 362 | 33.48 355 | 18.21 363 | 40.16 363 | 3.64 366 | 71.01 356 | 38.85 352 | 19.31 354 | 65.02 352 |
|
ANet_high | | | 50.71 330 | 46.17 332 | 64.33 340 | 44.27 364 | 52.30 352 | 76.13 352 | 78.73 359 | 64.95 342 | 27.37 358 | 55.23 355 | 14.61 362 | 67.74 358 | 36.01 354 | 18.23 357 | 72.95 350 |
|
PMVS | | 41.42 23 | 45.67 332 | 42.50 334 | 55.17 345 | 34.28 365 | 32.37 363 | 66.24 355 | 78.71 360 | 30.72 356 | 22.04 361 | 59.59 351 | 4.59 365 | 77.85 355 | 27.49 356 | 58.84 343 | 55.29 355 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
tmp_tt | | | 53.66 329 | 52.86 329 | 56.05 344 | 32.75 366 | 41.97 361 | 73.42 353 | 76.12 361 | 21.91 360 | 39.68 354 | 96.39 164 | 42.59 343 | 65.10 359 | 78.00 244 | 14.92 359 | 61.08 354 |
|
testmvs | | | 18.81 340 | 23.05 341 | 6.10 353 | 4.48 367 | 2.29 368 | 97.78 201 | 3.00 369 | 3.27 362 | 18.60 362 | 62.71 349 | 1.53 370 | 2.49 365 | 14.26 361 | 1.80 361 | 13.50 361 |
|
test123 | | | 16.58 342 | 19.47 342 | 7.91 352 | 3.59 368 | 5.37 367 | 94.32 292 | 1.39 370 | 2.49 363 | 13.98 364 | 44.60 360 | 2.91 369 | 2.65 364 | 11.35 362 | 0.57 363 | 15.70 360 |
|
cdsmvs_eth3d_5k | | | 22.52 339 | 30.03 340 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 97.17 145 | 0.00 364 | 0.00 366 | 98.77 65 | 74.35 209 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
pcd_1.5k_mvsjas | | | 6.87 344 | 9.16 345 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 0.00 371 | 0.00 364 | 0.00 366 | 0.00 366 | 82.48 148 | 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 | | | 8.21 343 | 10.94 344 | 0.00 354 | 0.00 369 | 0.00 369 | 0.00 360 | 0.00 371 | 0.00 364 | 0.00 366 | 98.50 84 | 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 | | | | | | | | | | | | | | | | | 98.84 113 |
|
test_part1 | | | | | 0.00 354 | | 0.00 369 | 0.00 360 | 97.69 81 | | | | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
sam_mvs1 | | | | | | | | | | | | | 88.39 58 | | | | 98.84 113 |
|
sam_mvs | | | | | | | | | | | | | 87.08 82 | | | | |
|
MTGPA | | | | | | | | | 97.45 121 | | | | | | | | |
|
test_post1 | | | | | | | | 90.74 326 | | | | 41.37 362 | 85.38 110 | 96.36 243 | 83.16 196 | | |
|
test_post | | | | | | | | | | | | 46.00 359 | 87.37 75 | 97.11 206 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 84.86 318 | 88.73 52 | 96.81 217 | | | |
|
MTMP | | | | | | | | 99.21 52 | 91.09 337 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 98.60 10 | 99.87 6 | 99.90 10 |
|
agg_prior2 | | | | | | | | | | | | | | | 97.84 27 | 99.87 6 | 99.91 9 |
|
test_prior4 | | | | | | | 92.00 82 | 99.41 36 | | | | | | | | | |
|
test_prior2 | | | | | | | | 99.57 19 | | 91.43 70 | 98.12 21 | 98.97 48 | 90.43 35 | | 98.33 18 | 99.81 15 | |
|
旧先验2 | | | | | | | | 98.67 119 | | 85.75 197 | 98.96 5 | | | 98.97 120 | 93.84 87 | | |
|
新几何2 | | | | | | | | 98.26 171 | | | | | | | | | |
|
无先验 | | | | | | | | 98.52 137 | 97.82 64 | 87.20 177 | | | | 99.90 30 | 87.64 154 | | 99.85 20 |
|
原ACMM2 | | | | | | | | 98.69 114 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.88 34 | 84.16 185 | | |
|
segment_acmp | | | | | | | | | | | | | 90.56 34 | | | | |
|
testdata1 | | | | | | | | 97.89 197 | | 92.43 50 | | | | | | | |
|
plane_prior5 | | | | | | | | | 96.30 196 | | | | | 97.75 175 | 93.46 94 | 86.17 210 | 92.67 215 |
|
plane_prior4 | | | | | | | | | | | | 96.52 158 | | | | | |
|
plane_prior3 | | | | | | | 85.91 230 | | | 93.65 31 | 86.99 184 | | | | | | |
|
plane_prior2 | | | | | | | | 99.02 79 | | 93.38 36 | | | | | | | |
|
plane_prior | | | | | | | 86.07 226 | 99.14 66 | | 93.81 29 | | | | | | 86.26 209 | |
|
n2 | | | | | | | | | 0.00 371 | | | | | | | | |
|
nn | | | | | | | | | 0.00 371 | | | | | | | | |
|
door-mid | | | | | | | | | 84.90 356 | | | | | | | | |
|
test11 | | | | | | | | | 97.68 83 | | | | | | | | |
|
door | | | | | | | | | 85.30 355 | | | | | | | | |
|
HQP5-MVS | | | | | | | 86.39 213 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 93.82 89 | | |
|
HQP4-MVS | | | | | | | | | | | 87.57 178 | | | 97.77 170 | | | 92.72 213 |
|
HQP3-MVS | | | | | | | | | 96.37 191 | | | | | | | 86.29 207 | |
|
HQP2-MVS | | | | | | | | | | | | | 73.34 223 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 91.17 105 | 91.38 319 | | 87.45 170 | 93.08 107 | | 86.67 89 | | 87.02 160 | | 98.95 107 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 82.64 238 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 83.83 227 | |
|
Test By Simon | | | | | | | | | | | | | 83.62 124 | | | | |
|