UA-Net | | | 97.96 45 | 97.62 48 | 98.98 48 | 98.86 88 | 97.47 59 | 98.89 64 | 99.08 19 | 96.67 49 | 98.72 35 | 99.54 1 | 93.15 79 | 99.81 50 | 94.87 133 | 98.83 98 | 99.65 50 |
|
APDe-MVS | | | 99.02 1 | 98.84 1 | 99.55 1 | 99.57 23 | 98.96 2 | 99.39 5 | 98.93 35 | 97.38 18 | 99.41 3 | 99.54 1 | 96.66 5 | 99.84 42 | 98.86 1 | 99.85 2 | 99.87 1 |
|
DeepC-MVS | | 95.98 3 | 97.88 49 | 97.58 50 | 98.77 58 | 99.25 64 | 96.93 77 | 98.83 74 | 98.75 74 | 96.96 41 | 96.89 112 | 99.50 3 | 90.46 124 | 99.87 34 | 97.84 35 | 99.76 23 | 99.52 66 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ACMMP_Plus | | | 98.61 13 | 98.30 25 | 99.55 1 | 99.62 21 | 98.95 3 | 98.82 76 | 98.81 58 | 95.80 72 | 99.16 12 | 99.47 4 | 95.37 40 | 99.92 13 | 97.89 31 | 99.75 29 | 99.79 4 |
|
MP-MVS-pluss | | | 98.31 39 | 97.92 42 | 99.49 4 | 99.72 11 | 98.88 4 | 98.43 148 | 98.78 68 | 94.10 130 | 97.69 84 | 99.42 5 | 95.25 45 | 99.92 13 | 98.09 23 | 99.80 9 | 99.67 46 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
SteuartSystems-ACMMP | | | 98.90 2 | 98.75 2 | 99.36 12 | 99.22 71 | 98.43 16 | 99.10 44 | 98.87 48 | 97.38 18 | 99.35 5 | 99.40 6 | 97.78 1 | 99.87 34 | 97.77 38 | 99.85 2 | 99.78 7 |
Skip Steuart: Steuart Systems R&D Blog. |
MPTG | | | 98.55 22 | 98.25 29 | 99.46 6 | 99.76 1 | 98.64 8 | 98.55 133 | 98.74 75 | 97.27 26 | 98.02 63 | 99.39 7 | 94.81 54 | 99.96 1 | 97.91 28 | 99.79 10 | 99.77 14 |
|
MTAPA | | | 98.58 18 | 98.29 26 | 99.46 6 | 99.76 1 | 98.64 8 | 98.90 60 | 98.74 75 | 97.27 26 | 98.02 63 | 99.39 7 | 94.81 54 | 99.96 1 | 97.91 28 | 99.79 10 | 99.77 14 |
|
VDDNet | | | 95.36 150 | 94.53 161 | 97.86 111 | 98.10 132 | 95.13 151 | 98.85 71 | 97.75 225 | 90.46 247 | 98.36 51 | 99.39 7 | 73.27 306 | 99.64 100 | 97.98 26 | 96.58 159 | 98.81 136 |
|
SD-MVS | | | 98.64 10 | 98.68 3 | 98.53 72 | 99.33 42 | 98.36 21 | 98.90 60 | 98.85 52 | 97.28 22 | 99.72 1 | 99.39 7 | 96.63 7 | 97.60 274 | 98.17 22 | 99.85 2 | 99.64 53 |
|
DeepPCF-MVS | | 96.37 2 | 97.93 48 | 98.48 13 | 96.30 210 | 99.00 82 | 89.54 271 | 97.43 242 | 98.87 48 | 98.16 2 | 99.26 6 | 99.38 11 | 96.12 17 | 99.64 100 | 98.30 20 | 99.77 17 | 99.72 30 |
|
EI-MVSNet-UG-set | | | 98.41 30 | 98.34 21 | 98.61 66 | 99.45 33 | 96.32 102 | 98.28 165 | 98.68 93 | 97.17 31 | 98.74 34 | 99.37 12 | 95.25 45 | 99.79 69 | 98.57 7 | 99.54 63 | 99.73 27 |
|
APD-MVS_3200maxsize | | | 98.53 25 | 98.33 24 | 99.15 37 | 99.50 27 | 97.92 45 | 99.15 36 | 98.81 58 | 96.24 58 | 99.20 10 | 99.37 12 | 95.30 43 | 99.80 57 | 97.73 40 | 99.67 39 | 99.72 30 |
|
abl_6 | | | 98.30 40 | 98.03 38 | 99.13 38 | 99.56 24 | 97.76 50 | 99.13 40 | 98.82 55 | 96.14 61 | 99.26 6 | 99.37 12 | 93.33 76 | 99.93 9 | 96.96 66 | 99.67 39 | 99.69 35 |
|
LS3D | | | 97.16 84 | 96.66 91 | 98.68 62 | 98.53 112 | 97.19 69 | 98.93 58 | 98.90 41 | 92.83 187 | 95.99 141 | 99.37 12 | 92.12 95 | 99.87 34 | 93.67 166 | 99.57 54 | 98.97 125 |
|
EI-MVSNet-Vis-set | | | 98.47 28 | 98.39 15 | 98.69 61 | 99.46 32 | 96.49 95 | 98.30 163 | 98.69 90 | 97.21 28 | 98.84 27 | 99.36 16 | 95.41 39 | 99.78 74 | 98.62 5 | 99.65 43 | 99.80 3 |
|
ACMMP | | | 98.23 41 | 97.95 41 | 99.09 42 | 99.74 7 | 97.62 54 | 99.03 48 | 99.41 6 | 95.98 67 | 97.60 90 | 99.36 16 | 94.45 64 | 99.93 9 | 97.14 60 | 98.85 97 | 99.70 34 |
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 |
DP-MVS | | | 96.59 105 | 95.93 114 | 98.57 68 | 99.34 39 | 96.19 106 | 98.70 110 | 98.39 150 | 89.45 273 | 94.52 163 | 99.35 18 | 91.85 101 | 99.85 40 | 92.89 191 | 98.88 94 | 99.68 41 |
|
VDD-MVS | | | 95.82 131 | 95.23 141 | 97.61 127 | 98.84 91 | 93.98 206 | 98.68 115 | 97.40 258 | 95.02 102 | 97.95 69 | 99.34 19 | 74.37 304 | 99.78 74 | 98.64 3 | 96.80 154 | 99.08 117 |
|
PGM-MVS | | | 98.49 27 | 98.23 32 | 99.27 23 | 99.72 11 | 98.08 38 | 98.99 51 | 99.49 5 | 95.43 86 | 99.03 15 | 99.32 20 | 95.56 35 | 99.94 3 | 96.80 78 | 99.77 17 | 99.78 7 |
|
TSAR-MVS + MP. | | | 98.78 3 | 98.62 4 | 99.24 25 | 99.69 17 | 98.28 27 | 99.14 37 | 98.66 103 | 96.84 43 | 99.56 2 | 99.31 21 | 96.34 10 | 99.70 91 | 98.32 19 | 99.73 34 | 99.73 27 |
|
Regformer-3 | | | 98.59 16 | 98.50 11 | 98.86 56 | 99.43 35 | 97.05 73 | 98.40 151 | 98.68 93 | 97.43 14 | 99.06 14 | 99.31 21 | 95.80 32 | 99.77 79 | 98.62 5 | 99.76 23 | 99.78 7 |
|
Regformer-4 | | | 98.64 10 | 98.53 7 | 98.99 46 | 99.43 35 | 97.37 62 | 98.40 151 | 98.79 66 | 97.46 13 | 99.09 13 | 99.31 21 | 95.86 31 | 99.80 57 | 98.64 3 | 99.76 23 | 99.79 4 |
|
XVG-OURS | | | 96.55 107 | 96.41 97 | 96.99 150 | 98.75 95 | 93.76 212 | 97.50 239 | 98.52 126 | 95.67 76 | 96.83 114 | 99.30 24 | 88.95 150 | 99.53 113 | 95.88 104 | 96.26 168 | 97.69 173 |
|
MSLP-MVS++ | | | 98.56 21 | 98.57 5 | 98.55 70 | 99.26 63 | 96.80 82 | 98.71 107 | 99.05 22 | 97.28 22 | 98.84 27 | 99.28 25 | 96.47 9 | 99.40 121 | 98.52 13 | 99.70 37 | 99.47 77 |
|
DeepC-MVS_fast | | 96.70 1 | 98.55 22 | 98.34 21 | 99.18 32 | 99.25 64 | 98.04 39 | 98.50 141 | 98.78 68 | 97.72 4 | 98.92 26 | 99.28 25 | 95.27 44 | 99.82 48 | 97.55 49 | 99.77 17 | 99.69 35 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
RPSCF | | | 94.87 172 | 95.40 129 | 93.26 285 | 98.89 85 | 82.06 309 | 98.33 156 | 98.06 212 | 90.30 251 | 96.56 123 | 99.26 27 | 87.09 207 | 99.49 115 | 93.82 162 | 96.32 166 | 98.24 161 |
|
APD-MVS | | | 98.35 35 | 98.00 40 | 99.42 9 | 99.51 26 | 98.72 7 | 98.80 85 | 98.82 55 | 94.52 120 | 99.23 8 | 99.25 28 | 95.54 37 | 99.80 57 | 96.52 88 | 99.77 17 | 99.74 25 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MP-MVS | | | 98.33 38 | 98.01 39 | 99.28 20 | 99.75 3 | 98.18 33 | 99.22 27 | 98.79 66 | 96.13 62 | 97.92 72 | 99.23 29 | 94.54 59 | 99.94 3 | 96.74 80 | 99.78 14 | 99.73 27 |
|
mPP-MVS | | | 98.51 26 | 98.26 28 | 99.25 24 | 99.75 3 | 98.04 39 | 99.28 16 | 98.81 58 | 96.24 58 | 98.35 52 | 99.23 29 | 95.46 38 | 99.94 3 | 97.42 54 | 99.81 8 | 99.77 14 |
|
MG-MVS | | | 97.81 53 | 97.60 49 | 98.44 79 | 99.12 80 | 95.97 115 | 97.75 223 | 98.78 68 | 96.89 42 | 98.46 45 | 99.22 31 | 93.90 73 | 99.68 95 | 94.81 136 | 99.52 65 | 99.67 46 |
|
Regformer-1 | | | 98.66 8 | 98.51 10 | 99.12 40 | 99.35 37 | 97.81 49 | 98.37 153 | 98.76 71 | 97.49 10 | 99.20 10 | 99.21 32 | 96.08 19 | 99.79 69 | 98.42 15 | 99.73 34 | 99.75 20 |
|
Regformer-2 | | | 98.69 7 | 98.52 8 | 99.19 28 | 99.35 37 | 98.01 41 | 98.37 153 | 98.81 58 | 97.48 12 | 99.21 9 | 99.21 32 | 96.13 16 | 99.80 57 | 98.40 17 | 99.73 34 | 99.75 20 |
|
Vis-MVSNet | | | 97.42 72 | 97.11 70 | 98.34 85 | 98.66 102 | 96.23 105 | 99.22 27 | 99.00 25 | 96.63 51 | 98.04 62 | 99.21 32 | 88.05 185 | 99.35 125 | 96.01 101 | 99.21 83 | 99.45 83 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
XVS | | | 98.70 5 | 98.49 12 | 99.34 13 | 99.70 15 | 98.35 22 | 99.29 14 | 98.88 46 | 97.40 15 | 98.46 45 | 99.20 35 | 95.90 29 | 99.89 25 | 97.85 33 | 99.74 32 | 99.78 7 |
|
LFMVS | | | 95.86 129 | 94.98 150 | 98.47 77 | 98.87 87 | 96.32 102 | 98.84 73 | 96.02 299 | 93.40 166 | 98.62 39 | 99.20 35 | 74.99 299 | 99.63 103 | 97.72 41 | 97.20 148 | 99.46 81 |
|
HPM-MVS_fast | | | 98.38 32 | 98.13 35 | 99.12 40 | 99.75 3 | 97.86 46 | 99.44 4 | 98.82 55 | 94.46 124 | 98.94 21 | 99.20 35 | 95.16 48 | 99.74 85 | 97.58 46 | 99.85 2 | 99.77 14 |
|
ACMMPR | | | 98.59 16 | 98.36 18 | 99.29 18 | 99.74 7 | 98.15 35 | 99.23 21 | 98.95 32 | 96.10 65 | 98.93 25 | 99.19 38 | 95.70 33 | 99.94 3 | 97.62 44 | 99.79 10 | 99.78 7 |
|
HFP-MVS | | | 98.63 12 | 98.40 14 | 99.32 16 | 99.72 11 | 98.29 25 | 99.23 21 | 98.96 30 | 96.10 65 | 98.94 21 | 99.17 39 | 96.06 20 | 99.92 13 | 97.62 44 | 99.78 14 | 99.75 20 |
|
region2R | | | 98.61 13 | 98.38 16 | 99.29 18 | 99.74 7 | 98.16 34 | 99.23 21 | 98.93 35 | 96.15 60 | 98.94 21 | 99.17 39 | 95.91 28 | 99.94 3 | 97.55 49 | 99.79 10 | 99.78 7 |
|
#test# | | | 98.54 24 | 98.27 27 | 99.32 16 | 99.72 11 | 98.29 25 | 98.98 54 | 98.96 30 | 95.65 78 | 98.94 21 | 99.17 39 | 96.06 20 | 99.92 13 | 97.21 59 | 99.78 14 | 99.75 20 |
|
CNVR-MVS | | | 98.78 3 | 98.56 6 | 99.45 8 | 99.32 45 | 98.87 5 | 98.47 144 | 98.81 58 | 97.72 4 | 98.76 33 | 99.16 42 | 97.05 2 | 99.78 74 | 98.06 24 | 99.66 42 | 99.69 35 |
|
3Dnovator | | 94.51 5 | 97.46 66 | 96.93 77 | 99.07 43 | 97.78 152 | 97.64 52 | 99.35 10 | 99.06 20 | 97.02 39 | 93.75 208 | 99.16 42 | 89.25 139 | 99.92 13 | 97.22 58 | 99.75 29 | 99.64 53 |
|
CP-MVS | | | 98.57 20 | 98.36 18 | 99.19 28 | 99.66 19 | 97.86 46 | 99.34 11 | 98.87 48 | 95.96 68 | 98.60 41 | 99.13 44 | 96.05 22 | 99.94 3 | 97.77 38 | 99.86 1 | 99.77 14 |
|
3Dnovator+ | | 94.38 6 | 97.43 71 | 96.78 84 | 99.38 10 | 97.83 149 | 98.52 11 | 99.37 7 | 98.71 86 | 97.09 37 | 92.99 229 | 99.13 44 | 89.36 136 | 99.89 25 | 96.97 64 | 99.57 54 | 99.71 32 |
|
EPNet | | | 97.28 79 | 96.87 80 | 98.51 73 | 94.98 289 | 96.14 107 | 98.90 60 | 97.02 279 | 98.28 1 | 95.99 141 | 99.11 46 | 91.36 110 | 99.89 25 | 96.98 63 | 99.19 84 | 99.50 70 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
114514_t | | | 96.93 91 | 96.27 104 | 98.92 52 | 99.50 27 | 97.63 53 | 98.85 71 | 98.90 41 | 84.80 301 | 97.77 77 | 99.11 46 | 92.84 81 | 99.66 97 | 94.85 134 | 99.77 17 | 99.47 77 |
|
testdata | | | | | 98.26 88 | 99.20 74 | 95.36 142 | | 98.68 93 | 91.89 217 | 98.60 41 | 99.10 48 | 94.44 65 | 99.82 48 | 94.27 151 | 99.44 73 | 99.58 63 |
|
PHI-MVS | | | 98.34 36 | 98.06 37 | 99.18 32 | 99.15 78 | 98.12 37 | 99.04 47 | 99.09 18 | 93.32 169 | 98.83 29 | 99.10 48 | 96.54 8 | 99.83 43 | 97.70 42 | 99.76 23 | 99.59 61 |
|
OMC-MVS | | | 97.55 65 | 97.34 62 | 98.20 92 | 99.33 42 | 95.92 122 | 98.28 165 | 98.59 111 | 95.52 83 | 97.97 68 | 99.10 48 | 93.28 78 | 99.49 115 | 95.09 132 | 98.88 94 | 99.19 103 |
|
COLMAP_ROB | | 93.27 12 | 95.33 153 | 94.87 155 | 96.71 165 | 99.29 55 | 93.24 225 | 98.58 126 | 98.11 198 | 89.92 261 | 93.57 211 | 99.10 48 | 86.37 220 | 99.79 69 | 90.78 234 | 98.10 128 | 97.09 186 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
旧先验1 | | | | | | 99.29 55 | 97.48 58 | | 98.70 89 | | | 99.09 52 | 95.56 35 | | | 99.47 68 | 99.61 56 |
|
XVG-OURS-SEG-HR | | | 96.51 108 | 96.34 100 | 97.02 149 | 98.77 93 | 93.76 212 | 97.79 221 | 98.50 133 | 95.45 85 | 96.94 107 | 99.09 52 | 87.87 191 | 99.55 112 | 96.76 79 | 95.83 177 | 97.74 170 |
|
CPTT-MVS | | | 97.72 56 | 97.32 63 | 98.92 52 | 99.64 20 | 97.10 72 | 99.12 42 | 98.81 58 | 92.34 207 | 98.09 58 | 99.08 54 | 93.01 80 | 99.92 13 | 96.06 98 | 99.77 17 | 99.75 20 |
|
EPP-MVSNet | | | 97.46 66 | 97.28 64 | 97.99 106 | 98.64 104 | 95.38 141 | 99.33 13 | 98.31 158 | 93.61 160 | 97.19 98 | 99.07 55 | 94.05 70 | 99.23 131 | 96.89 70 | 98.43 117 | 99.37 86 |
|
OpenMVS | | 93.04 13 | 95.83 130 | 95.00 148 | 98.32 86 | 97.18 192 | 97.32 63 | 99.21 30 | 98.97 28 | 89.96 258 | 91.14 256 | 99.05 56 | 86.64 215 | 99.92 13 | 93.38 171 | 99.47 68 | 97.73 171 |
|
EI-MVSNet | | | 95.96 124 | 95.83 117 | 96.36 205 | 97.93 142 | 93.70 216 | 98.12 183 | 98.27 164 | 93.70 153 | 95.07 149 | 99.02 57 | 92.23 91 | 98.54 200 | 94.68 138 | 93.46 206 | 96.84 209 |
|
CVMVSNet | | | 95.43 145 | 96.04 111 | 93.57 281 | 97.93 142 | 83.62 303 | 98.12 183 | 98.59 111 | 95.68 75 | 96.56 123 | 99.02 57 | 87.51 201 | 97.51 277 | 93.56 169 | 97.44 145 | 99.60 59 |
|
TSAR-MVS + GP. | | | 98.38 32 | 98.24 31 | 98.81 57 | 99.22 71 | 97.25 67 | 98.11 185 | 98.29 163 | 97.19 30 | 98.99 20 | 99.02 57 | 96.22 11 | 99.67 96 | 98.52 13 | 98.56 110 | 99.51 69 |
|
QAPM | | | 96.29 116 | 95.40 129 | 98.96 50 | 97.85 148 | 97.60 55 | 99.23 21 | 98.93 35 | 89.76 265 | 93.11 226 | 99.02 57 | 89.11 143 | 99.93 9 | 91.99 212 | 99.62 45 | 99.34 87 |
|
MVS_111021_LR | | | 98.34 36 | 98.23 32 | 98.67 63 | 99.27 61 | 96.90 79 | 97.95 200 | 99.58 3 | 97.14 33 | 98.44 49 | 99.01 61 | 95.03 51 | 99.62 105 | 97.91 28 | 99.75 29 | 99.50 70 |
|
MVS_111021_HR | | | 98.47 28 | 98.34 21 | 98.88 54 | 99.22 71 | 97.32 63 | 97.91 205 | 99.58 3 | 97.20 29 | 98.33 53 | 99.00 62 | 95.99 24 | 99.64 100 | 98.05 25 | 99.76 23 | 99.69 35 |
|
IS-MVSNet | | | 97.22 81 | 96.88 79 | 98.25 90 | 98.85 90 | 96.36 100 | 99.19 33 | 97.97 217 | 95.39 88 | 97.23 97 | 98.99 63 | 91.11 115 | 98.93 168 | 94.60 141 | 98.59 108 | 99.47 77 |
|
原ACMM1 | | | | | 98.65 64 | 99.32 45 | 96.62 88 | | 98.67 100 | 93.27 172 | 97.81 76 | 98.97 64 | 95.18 47 | 99.83 43 | 93.84 161 | 99.46 71 | 99.50 70 |
|
1121 | | | 97.37 76 | 96.77 86 | 99.16 35 | 99.34 39 | 97.99 44 | 98.19 174 | 98.68 93 | 90.14 254 | 98.01 65 | 98.97 64 | 94.80 56 | 99.87 34 | 93.36 172 | 99.46 71 | 99.61 56 |
|
HPM-MVS | | | 98.36 34 | 98.10 36 | 99.13 38 | 99.74 7 | 97.82 48 | 99.53 1 | 98.80 65 | 94.63 117 | 98.61 40 | 98.97 64 | 95.13 49 | 99.77 79 | 97.65 43 | 99.83 7 | 99.79 4 |
|
DELS-MVS | | | 98.40 31 | 98.20 34 | 98.99 46 | 99.00 82 | 97.66 51 | 97.75 223 | 98.89 43 | 97.71 6 | 98.33 53 | 98.97 64 | 94.97 52 | 99.88 33 | 98.42 15 | 99.76 23 | 99.42 84 |
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 |
test222 | | | | | | 99.23 70 | 97.17 71 | 97.40 243 | 98.66 103 | 88.68 280 | 98.05 60 | 98.96 68 | 94.14 69 | | | 99.53 64 | 99.61 56 |
|
æ–°å‡ ä½•1 | | | | | 99.16 35 | 99.34 39 | 98.01 41 | | 98.69 90 | 90.06 256 | 98.13 56 | 98.95 69 | 94.60 58 | 99.89 25 | 91.97 213 | 99.47 68 | 99.59 61 |
|
DP-MVS Recon | | | 97.86 51 | 97.46 58 | 99.06 44 | 99.53 25 | 98.35 22 | 98.33 156 | 98.89 43 | 92.62 190 | 98.05 60 | 98.94 70 | 95.34 42 | 99.65 98 | 96.04 99 | 99.42 74 | 99.19 103 |
|
NCCC | | | 98.61 13 | 98.35 20 | 99.38 10 | 99.28 60 | 98.61 10 | 98.45 145 | 98.76 71 | 97.82 3 | 98.45 48 | 98.93 71 | 96.65 6 | 99.83 43 | 97.38 56 | 99.41 75 | 99.71 32 |
|
MVS_0305 | | | 98.00 43 | 97.71 46 | 98.87 55 | 98.77 93 | 97.19 69 | 98.28 165 | 98.71 86 | 97.57 7 | 97.70 82 | 98.92 72 | 91.16 113 | 99.93 9 | 98.71 2 | 99.60 47 | 99.48 75 |
|
CSCG | | | 97.85 52 | 97.74 45 | 98.20 92 | 99.67 18 | 95.16 149 | 99.22 27 | 99.32 7 | 93.04 177 | 97.02 105 | 98.92 72 | 95.36 41 | 99.91 21 | 97.43 53 | 99.64 44 | 99.52 66 |
|
CHOSEN 1792x2688 | | | 97.12 86 | 96.80 81 | 98.08 101 | 99.30 52 | 94.56 190 | 98.05 190 | 99.71 1 | 93.57 161 | 97.09 99 | 98.91 74 | 88.17 180 | 99.89 25 | 96.87 76 | 99.56 60 | 99.81 2 |
|
PVSNet_Blended_VisFu | | | 97.70 57 | 97.46 58 | 98.44 79 | 99.27 61 | 95.91 124 | 98.63 120 | 99.16 16 | 94.48 123 | 97.67 85 | 98.88 75 | 92.80 82 | 99.91 21 | 97.11 61 | 99.12 86 | 99.50 70 |
|
Vis-MVSNet (Re-imp) | | | 96.87 94 | 96.55 94 | 97.83 113 | 98.73 96 | 95.46 139 | 99.20 31 | 98.30 161 | 94.96 105 | 96.60 122 | 98.87 76 | 90.05 131 | 98.59 196 | 93.67 166 | 98.60 107 | 99.46 81 |
|
MVS_dtu | | | 96.84 95 | 96.38 99 | 98.24 91 | 97.81 150 | 96.01 110 | 97.98 197 | 98.09 206 | 97.49 10 | 96.55 125 | 98.86 77 | 86.53 216 | 99.89 25 | 95.19 131 | 98.89 93 | 98.82 135 |
|
CDPH-MVS | | | 97.94 47 | 97.49 56 | 99.28 20 | 99.47 31 | 98.44 14 | 97.91 205 | 98.67 100 | 92.57 193 | 98.77 32 | 98.85 78 | 95.93 27 | 99.72 86 | 95.56 117 | 99.69 38 | 99.68 41 |
|
VNet | | | 97.79 54 | 97.40 61 | 98.96 50 | 98.88 86 | 97.55 56 | 98.63 120 | 98.93 35 | 96.74 46 | 99.02 16 | 98.84 79 | 90.33 127 | 99.83 43 | 98.53 9 | 96.66 156 | 99.50 70 |
|
HPM-MVS++ | | | 98.58 18 | 98.25 29 | 99.55 1 | 99.50 27 | 99.08 1 | 98.72 106 | 98.66 103 | 97.51 9 | 98.15 55 | 98.83 80 | 95.70 33 | 99.92 13 | 97.53 51 | 99.67 39 | 99.66 48 |
|
MVSFormer | | | 97.57 63 | 97.49 56 | 97.84 112 | 98.07 133 | 95.76 129 | 99.47 2 | 98.40 148 | 94.98 103 | 98.79 30 | 98.83 80 | 92.34 86 | 98.41 231 | 96.91 68 | 99.59 51 | 99.34 87 |
|
jason | | | 97.32 78 | 97.08 72 | 98.06 104 | 97.45 174 | 95.59 132 | 97.87 213 | 97.91 220 | 94.79 110 | 98.55 43 | 98.83 80 | 91.12 114 | 99.23 131 | 97.58 46 | 99.60 47 | 99.34 87 |
jason: jason. |
MCST-MVS | | | 98.65 9 | 98.37 17 | 99.48 5 | 99.60 22 | 98.87 5 | 98.41 150 | 98.68 93 | 97.04 38 | 98.52 44 | 98.80 83 | 96.78 4 | 99.83 43 | 97.93 27 | 99.61 46 | 99.74 25 |
|
HSP-MVS | | | 98.70 5 | 98.52 8 | 99.24 25 | 99.75 3 | 98.23 28 | 99.26 17 | 98.58 116 | 97.52 8 | 99.41 3 | 98.78 84 | 96.00 23 | 99.79 69 | 97.79 37 | 99.59 51 | 99.69 35 |
|
OPM-MVS | | | 95.69 137 | 95.33 136 | 96.76 163 | 96.16 258 | 94.63 183 | 98.43 148 | 98.39 150 | 96.64 50 | 95.02 151 | 98.78 84 | 85.15 238 | 99.05 151 | 95.21 130 | 94.20 188 | 96.60 243 |
|
AllTest | | | 95.24 155 | 94.65 159 | 96.99 150 | 99.25 64 | 93.21 226 | 98.59 124 | 98.18 180 | 91.36 232 | 93.52 213 | 98.77 86 | 84.67 243 | 99.72 86 | 89.70 255 | 97.87 134 | 98.02 166 |
|
TestCases | | | | | 96.99 150 | 99.25 64 | 93.21 226 | | 98.18 180 | 91.36 232 | 93.52 213 | 98.77 86 | 84.67 243 | 99.72 86 | 89.70 255 | 97.87 134 | 98.02 166 |
|
LPG-MVS_test | | | 95.62 140 | 95.34 134 | 96.47 197 | 97.46 171 | 93.54 217 | 98.99 51 | 98.54 121 | 94.67 113 | 94.36 176 | 98.77 86 | 85.39 233 | 99.11 144 | 95.71 112 | 94.15 191 | 96.76 216 |
|
LGP-MVS_train | | | | | 96.47 197 | 97.46 171 | 93.54 217 | | 98.54 121 | 94.67 113 | 94.36 176 | 98.77 86 | 85.39 233 | 99.11 144 | 95.71 112 | 94.15 191 | 96.76 216 |
|
MSDG | | | 95.93 126 | 95.30 139 | 97.83 113 | 98.90 84 | 95.36 142 | 96.83 277 | 98.37 153 | 91.32 236 | 94.43 173 | 98.73 90 | 90.27 128 | 99.60 106 | 90.05 247 | 98.82 99 | 98.52 151 |
|
test_prior3 | | | 98.22 42 | 97.90 43 | 99.19 28 | 99.31 47 | 98.22 30 | 97.80 219 | 98.84 53 | 96.12 63 | 97.89 74 | 98.69 91 | 95.96 25 | 99.70 91 | 96.89 70 | 99.60 47 | 99.65 50 |
|
test_prior2 | | | | | | | | 97.80 219 | | 96.12 63 | 97.89 74 | 98.69 91 | 95.96 25 | | 96.89 70 | 99.60 47 | |
|
TEST9 | | | | | | 99.31 47 | 98.50 12 | 97.92 202 | 98.73 79 | 92.63 189 | 97.74 80 | 98.68 93 | 96.20 12 | 99.80 57 | | | |
|
train_agg | | | 97.97 44 | 97.52 54 | 99.33 15 | 99.31 47 | 98.50 12 | 97.92 202 | 98.73 79 | 92.98 180 | 97.74 80 | 98.68 93 | 96.20 12 | 99.80 57 | 96.59 84 | 99.57 54 | 99.68 41 |
|
AdaColmap | | | 97.15 85 | 96.70 87 | 98.48 76 | 99.16 76 | 96.69 87 | 98.01 194 | 98.89 43 | 94.44 125 | 96.83 114 | 98.68 93 | 90.69 122 | 99.76 81 | 94.36 147 | 99.29 82 | 98.98 124 |
|
test_8 | | | | | | 99.29 55 | 98.44 14 | 97.89 210 | 98.72 81 | 92.98 180 | 97.70 82 | 98.66 96 | 96.20 12 | 99.80 57 | | | |
|
agg_prior1 | | | 97.95 46 | 97.51 55 | 99.28 20 | 99.30 52 | 98.38 17 | 97.81 218 | 98.72 81 | 93.16 174 | 97.57 92 | 98.66 96 | 96.14 15 | 99.81 50 | 96.63 83 | 99.56 60 | 99.66 48 |
|
agg_prior3 | | | 97.87 50 | 97.42 60 | 99.23 27 | 99.29 55 | 98.23 28 | 97.92 202 | 98.72 81 | 92.38 206 | 97.59 91 | 98.64 98 | 96.09 18 | 99.79 69 | 96.59 84 | 99.57 54 | 99.68 41 |
|
cdsmvs_eth3d_5k | | | 23.98 309 | 31.98 309 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 98.59 111 | 0.00 333 | 0.00 334 | 98.61 99 | 90.60 123 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
lupinMVS | | | 97.44 70 | 97.22 67 | 98.12 98 | 98.07 133 | 95.76 129 | 97.68 228 | 97.76 224 | 94.50 121 | 98.79 30 | 98.61 99 | 92.34 86 | 99.30 126 | 97.58 46 | 99.59 51 | 99.31 90 |
|
BH-RMVSNet | | | 95.92 127 | 95.32 137 | 97.69 123 | 98.32 119 | 94.64 182 | 98.19 174 | 97.45 253 | 94.56 118 | 96.03 139 | 98.61 99 | 85.02 239 | 99.12 140 | 90.68 236 | 99.06 87 | 99.30 93 |
|
TAMVS | | | 97.02 89 | 96.79 83 | 97.70 122 | 98.06 135 | 95.31 146 | 98.52 136 | 98.31 158 | 93.95 138 | 97.05 104 | 98.61 99 | 93.49 75 | 98.52 207 | 95.33 123 | 97.81 137 | 99.29 95 |
|
TAPA-MVS | | 93.98 7 | 95.35 151 | 94.56 160 | 97.74 118 | 99.13 79 | 94.83 168 | 98.33 156 | 98.64 108 | 86.62 289 | 96.29 133 | 98.61 99 | 94.00 72 | 99.29 127 | 80.00 301 | 99.41 75 | 99.09 114 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
F-COLMAP | | | 97.09 88 | 96.80 81 | 97.97 107 | 99.45 33 | 94.95 158 | 98.55 133 | 98.62 109 | 93.02 178 | 96.17 135 | 98.58 104 | 94.01 71 | 99.81 50 | 93.95 158 | 98.90 92 | 99.14 111 |
|
WTY-MVS | | | 97.37 76 | 96.92 78 | 98.72 60 | 98.86 88 | 96.89 81 | 98.31 161 | 98.71 86 | 95.26 91 | 97.67 85 | 98.56 105 | 92.21 92 | 99.78 74 | 95.89 103 | 96.85 153 | 99.48 75 |
|
CNLPA | | | 97.45 69 | 97.03 74 | 98.73 59 | 99.05 81 | 97.44 61 | 98.07 189 | 98.53 124 | 95.32 89 | 96.80 118 | 98.53 106 | 93.32 77 | 99.72 86 | 94.31 150 | 99.31 80 | 99.02 120 |
|
ACMP | | 93.49 10 | 95.34 152 | 94.98 150 | 96.43 201 | 97.67 156 | 93.48 219 | 98.73 104 | 98.44 142 | 94.94 108 | 92.53 239 | 98.53 106 | 84.50 246 | 99.14 138 | 95.48 120 | 94.00 196 | 96.66 234 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMH | | 92.88 16 | 94.55 196 | 93.95 193 | 96.34 208 | 97.63 158 | 93.26 224 | 98.81 82 | 98.49 137 | 93.43 165 | 89.74 268 | 98.53 106 | 81.91 264 | 99.08 149 | 93.69 164 | 93.30 212 | 96.70 225 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
OurMVSNet-221017-0 | | | 94.21 211 | 94.00 189 | 94.85 261 | 95.60 278 | 89.22 276 | 98.89 64 | 97.43 255 | 95.29 90 | 92.18 249 | 98.52 109 | 82.86 260 | 98.59 196 | 93.46 170 | 91.76 229 | 96.74 218 |
|
MVS_test0326 | | | 96.78 98 | 96.28 103 | 98.26 88 | 97.92 144 | 96.13 109 | 97.88 211 | 98.07 209 | 97.38 18 | 96.05 138 | 98.49 110 | 86.68 214 | 99.87 34 | 94.78 137 | 99.30 81 | 98.79 137 |
|
CDS-MVSNet | | | 96.99 90 | 96.69 88 | 97.90 110 | 98.05 136 | 95.98 111 | 98.20 172 | 98.33 157 | 93.67 158 | 96.95 106 | 98.49 110 | 93.54 74 | 98.42 224 | 95.24 129 | 97.74 141 | 99.31 90 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
sss | | | 97.39 74 | 96.98 76 | 98.61 66 | 98.60 108 | 96.61 90 | 98.22 170 | 98.93 35 | 93.97 137 | 98.01 65 | 98.48 112 | 91.98 99 | 99.85 40 | 96.45 90 | 98.15 126 | 99.39 85 |
|
ACMH+ | | 92.99 14 | 94.30 207 | 93.77 204 | 95.88 225 | 97.81 150 | 92.04 240 | 98.71 107 | 98.37 153 | 93.99 135 | 90.60 263 | 98.47 113 | 80.86 271 | 99.05 151 | 92.75 193 | 92.40 221 | 96.55 250 |
|
ACMM | | 93.85 9 | 95.69 137 | 95.38 133 | 96.61 182 | 97.61 160 | 93.84 210 | 98.91 59 | 98.44 142 | 95.25 92 | 94.28 184 | 98.47 113 | 86.04 227 | 99.12 140 | 95.50 119 | 93.95 198 | 96.87 206 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
1112_ss | | | 96.63 102 | 96.00 113 | 98.50 74 | 98.56 109 | 96.37 99 | 98.18 178 | 98.10 203 | 92.92 182 | 94.84 154 | 98.43 115 | 92.14 94 | 99.58 107 | 94.35 148 | 96.51 162 | 99.56 65 |
|
ab-mvs-re | | | 8.20 312 | 10.94 313 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 98.43 115 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
xiu_mvs_v1_base_debu | | | 97.60 60 | 97.56 51 | 97.72 119 | 98.35 114 | 95.98 111 | 97.86 214 | 98.51 128 | 97.13 34 | 99.01 17 | 98.40 117 | 91.56 106 | 99.80 57 | 98.53 9 | 98.68 102 | 97.37 181 |
|
xiu_mvs_v1_base | | | 97.60 60 | 97.56 51 | 97.72 119 | 98.35 114 | 95.98 111 | 97.86 214 | 98.51 128 | 97.13 34 | 99.01 17 | 98.40 117 | 91.56 106 | 99.80 57 | 98.53 9 | 98.68 102 | 97.37 181 |
|
xiu_mvs_v1_base_debi | | | 97.60 60 | 97.56 51 | 97.72 119 | 98.35 114 | 95.98 111 | 97.86 214 | 98.51 128 | 97.13 34 | 99.01 17 | 98.40 117 | 91.56 106 | 99.80 57 | 98.53 9 | 98.68 102 | 97.37 181 |
|
mvs_tets | | | 95.41 148 | 95.00 148 | 96.65 176 | 95.58 279 | 94.42 193 | 99.00 50 | 98.55 120 | 95.73 74 | 93.21 221 | 98.38 120 | 83.45 258 | 98.63 193 | 97.09 62 | 94.00 196 | 96.91 201 |
|
FC-MVSNet-test | | | 96.42 111 | 96.05 110 | 97.53 130 | 96.95 202 | 97.27 65 | 99.36 8 | 99.23 12 | 95.83 71 | 93.93 201 | 98.37 121 | 92.00 98 | 98.32 240 | 96.02 100 | 92.72 219 | 97.00 191 |
|
jajsoiax | | | 95.45 144 | 95.03 147 | 96.73 164 | 95.42 284 | 94.63 183 | 99.14 37 | 98.52 126 | 95.74 73 | 93.22 220 | 98.36 122 | 83.87 255 | 98.65 192 | 96.95 67 | 94.04 194 | 96.91 201 |
|
nrg030 | | | 96.28 118 | 95.72 120 | 97.96 108 | 96.90 206 | 98.15 35 | 99.39 5 | 98.31 158 | 95.47 84 | 94.42 174 | 98.35 123 | 92.09 96 | 98.69 188 | 97.50 52 | 89.05 253 | 97.04 189 |
|
FIs | | | 96.51 108 | 96.12 109 | 97.67 125 | 97.13 195 | 97.54 57 | 99.36 8 | 99.22 14 | 95.89 69 | 94.03 199 | 98.35 123 | 91.98 99 | 98.44 221 | 96.40 92 | 92.76 218 | 97.01 190 |
|
ITE_SJBPF | | | | | 95.44 241 | 97.42 175 | 91.32 250 | | 97.50 244 | 95.09 100 | 93.59 209 | 98.35 123 | 81.70 265 | 98.88 175 | 89.71 254 | 93.39 210 | 96.12 268 |
|
LTVRE_ROB | | 92.95 15 | 94.60 192 | 93.90 196 | 96.68 171 | 97.41 178 | 94.42 193 | 98.52 136 | 98.59 111 | 91.69 222 | 91.21 255 | 98.35 123 | 84.87 241 | 99.04 155 | 91.06 230 | 93.44 209 | 96.60 243 |
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 |
PS-MVSNAJss | | | 96.43 110 | 96.26 105 | 96.92 158 | 95.84 271 | 95.08 153 | 99.16 35 | 98.50 133 | 95.87 70 | 93.84 206 | 98.34 127 | 94.51 60 | 98.61 194 | 96.88 73 | 93.45 208 | 97.06 187 |
|
EPNet_dtu | | | 95.21 157 | 94.95 153 | 95.99 220 | 96.17 255 | 90.45 263 | 98.16 179 | 97.27 269 | 96.77 44 | 93.14 225 | 98.33 128 | 90.34 126 | 98.42 224 | 85.57 289 | 98.81 100 | 99.09 114 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PCF-MVS | | 93.45 11 | 94.68 188 | 93.43 224 | 98.42 82 | 98.62 106 | 96.77 84 | 95.48 300 | 98.20 176 | 84.63 302 | 93.34 218 | 98.32 129 | 88.55 172 | 99.81 50 | 84.80 292 | 98.96 90 | 98.68 144 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
PLC | | 95.07 4 | 97.20 82 | 96.78 84 | 98.44 79 | 99.29 55 | 96.31 104 | 98.14 180 | 98.76 71 | 92.41 204 | 96.39 131 | 98.31 130 | 94.92 53 | 99.78 74 | 94.06 156 | 98.77 101 | 99.23 101 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
HQP_MVS | | | 96.14 121 | 95.90 115 | 96.85 159 | 97.42 175 | 94.60 188 | 98.80 85 | 98.56 118 | 97.28 22 | 95.34 145 | 98.28 131 | 87.09 207 | 99.03 156 | 96.07 96 | 94.27 185 | 96.92 196 |
|
plane_prior4 | | | | | | | | | | | | 98.28 131 | | | | | |
|
API-MVS | | | 97.41 73 | 97.25 65 | 97.91 109 | 98.70 98 | 96.80 82 | 98.82 76 | 98.69 90 | 94.53 119 | 98.11 57 | 98.28 131 | 94.50 63 | 99.57 108 | 94.12 155 | 99.49 66 | 97.37 181 |
|
mvs_anonymous | | | 96.70 101 | 96.53 95 | 97.18 140 | 98.19 127 | 93.78 211 | 98.31 161 | 98.19 177 | 94.01 133 | 94.47 165 | 98.27 134 | 92.08 97 | 98.46 216 | 97.39 55 | 97.91 132 | 99.31 90 |
|
XXY-MVS | | | 95.20 158 | 94.45 166 | 97.46 131 | 96.75 214 | 96.56 92 | 98.86 70 | 98.65 107 | 93.30 171 | 93.27 219 | 98.27 134 | 84.85 242 | 98.87 176 | 94.82 135 | 91.26 235 | 96.96 193 |
|
SixPastTwentyTwo | | | 93.34 234 | 92.86 232 | 94.75 265 | 95.67 276 | 89.41 274 | 98.75 97 | 96.67 291 | 93.89 140 | 90.15 266 | 98.25 136 | 80.87 270 | 98.27 247 | 90.90 233 | 90.64 237 | 96.57 247 |
|
VPNet | | | 94.99 164 | 94.19 177 | 97.40 134 | 97.16 193 | 96.57 91 | 98.71 107 | 98.97 28 | 95.67 76 | 94.84 154 | 98.24 137 | 80.36 276 | 98.67 191 | 96.46 89 | 87.32 277 | 96.96 193 |
|
PVSNet_Blended | | | 97.38 75 | 97.12 69 | 98.14 95 | 99.25 64 | 95.35 144 | 97.28 255 | 99.26 8 | 93.13 175 | 97.94 70 | 98.21 138 | 92.74 83 | 99.81 50 | 96.88 73 | 99.40 77 | 99.27 97 |
|
HyFIR lowres test | | | 96.90 93 | 96.49 96 | 98.14 95 | 99.33 42 | 95.56 135 | 97.38 245 | 99.65 2 | 92.34 207 | 97.61 89 | 98.20 139 | 89.29 138 | 99.10 147 | 96.97 64 | 97.60 144 | 99.77 14 |
|
ab-mvs | | | 96.42 111 | 95.71 123 | 98.55 70 | 98.63 105 | 96.75 85 | 97.88 211 | 98.74 75 | 93.84 143 | 96.54 126 | 98.18 140 | 85.34 236 | 99.75 83 | 95.93 102 | 96.35 165 | 99.15 109 |
|
xiu_mvs_v2_base | | | 97.66 59 | 97.70 47 | 97.56 129 | 98.61 107 | 95.46 139 | 97.44 240 | 98.46 138 | 97.15 32 | 98.65 38 | 98.15 141 | 94.33 66 | 99.80 57 | 97.84 35 | 98.66 106 | 97.41 178 |
|
USDC | | | 93.33 235 | 92.71 235 | 95.21 251 | 96.83 210 | 90.83 255 | 96.91 269 | 97.50 244 | 93.84 143 | 90.72 261 | 98.14 142 | 77.69 287 | 98.82 182 | 89.51 259 | 93.21 215 | 95.97 272 |
|
EU-MVSNet | | | 93.66 230 | 94.14 180 | 92.25 290 | 95.96 265 | 83.38 304 | 98.52 136 | 98.12 193 | 94.69 111 | 92.61 236 | 98.13 143 | 87.36 205 | 96.39 303 | 91.82 216 | 90.00 241 | 96.98 192 |
|
CHOSEN 280x420 | | | 97.18 83 | 97.18 68 | 97.20 138 | 98.81 92 | 93.27 223 | 95.78 298 | 99.15 17 | 95.25 92 | 96.79 119 | 98.11 144 | 92.29 88 | 99.07 150 | 98.56 8 | 99.85 2 | 99.25 99 |
|
MVSTER | | | 96.06 122 | 95.72 120 | 97.08 147 | 98.23 123 | 95.93 121 | 98.73 104 | 98.27 164 | 94.86 109 | 95.07 149 | 98.09 145 | 88.21 179 | 98.54 200 | 96.59 84 | 93.46 206 | 96.79 213 |
|
MVS_Test | | | 97.28 79 | 97.00 75 | 98.13 97 | 98.33 118 | 95.97 115 | 98.74 101 | 98.07 209 | 94.27 127 | 98.44 49 | 98.07 146 | 92.48 85 | 99.26 128 | 96.43 91 | 98.19 125 | 99.16 108 |
|
PAPM_NR | | | 97.46 66 | 97.11 70 | 98.50 74 | 99.50 27 | 96.41 98 | 98.63 120 | 98.60 110 | 95.18 94 | 97.06 103 | 98.06 147 | 94.26 68 | 99.57 108 | 93.80 163 | 98.87 96 | 99.52 66 |
|
PatchMatch-RL | | | 96.59 105 | 96.03 112 | 98.27 87 | 99.31 47 | 96.51 94 | 97.91 205 | 99.06 20 | 93.72 150 | 96.92 110 | 98.06 147 | 88.50 175 | 99.65 98 | 91.77 218 | 99.00 89 | 98.66 146 |
|
Effi-MVS+ | | | 97.12 86 | 96.69 88 | 98.39 83 | 98.19 127 | 96.72 86 | 97.37 247 | 98.43 145 | 93.71 151 | 97.65 88 | 98.02 149 | 92.20 93 | 99.25 129 | 96.87 76 | 97.79 138 | 99.19 103 |
|
MVS | | | 94.67 189 | 93.54 218 | 98.08 101 | 96.88 207 | 96.56 92 | 98.19 174 | 98.50 133 | 78.05 315 | 92.69 234 | 98.02 149 | 91.07 117 | 99.63 103 | 90.09 244 | 98.36 119 | 98.04 165 |
|
BH-untuned | | | 95.95 125 | 95.72 120 | 96.65 176 | 98.55 111 | 92.26 236 | 98.23 169 | 97.79 223 | 93.73 149 | 94.62 160 | 98.01 151 | 88.97 149 | 99.00 159 | 93.04 182 | 98.51 111 | 98.68 144 |
|
CLD-MVS | | | 95.62 140 | 95.34 134 | 96.46 200 | 97.52 168 | 93.75 214 | 97.27 256 | 98.46 138 | 95.53 82 | 94.42 174 | 98.00 152 | 86.21 222 | 98.97 160 | 96.25 95 | 94.37 183 | 96.66 234 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
HY-MVS | | 93.96 8 | 96.82 97 | 96.23 107 | 98.57 68 | 98.46 113 | 97.00 74 | 98.14 180 | 98.21 174 | 93.95 138 | 96.72 120 | 97.99 153 | 91.58 105 | 99.76 81 | 94.51 145 | 96.54 161 | 98.95 129 |
|
MAR-MVS | | | 96.91 92 | 96.40 98 | 98.45 78 | 98.69 100 | 96.90 79 | 98.66 118 | 98.68 93 | 92.40 205 | 97.07 102 | 97.96 154 | 91.54 109 | 99.75 83 | 93.68 165 | 98.92 91 | 98.69 143 |
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 |
PS-CasMVS | | | 94.67 189 | 93.99 191 | 96.71 165 | 96.68 218 | 95.26 147 | 99.13 40 | 99.03 23 | 93.68 156 | 92.33 245 | 97.95 155 | 85.35 235 | 98.10 253 | 93.59 168 | 88.16 268 | 96.79 213 |
|
mvs-test1 | | | 96.60 103 | 96.68 90 | 96.37 204 | 97.89 146 | 91.81 242 | 98.56 131 | 98.10 203 | 96.57 52 | 96.52 127 | 97.94 156 | 90.81 119 | 99.45 120 | 95.72 110 | 98.01 129 | 97.86 169 |
|
TranMVSNet+NR-MVSNet | | | 95.14 160 | 94.48 162 | 97.11 145 | 96.45 228 | 96.36 100 | 99.03 48 | 99.03 23 | 95.04 101 | 93.58 210 | 97.93 157 | 88.27 178 | 98.03 258 | 94.13 154 | 86.90 284 | 96.95 195 |
|
testgi | | | 93.06 241 | 92.45 238 | 94.88 260 | 96.43 229 | 89.90 266 | 98.75 97 | 97.54 237 | 95.60 79 | 91.63 254 | 97.91 158 | 74.46 303 | 97.02 284 | 86.10 285 | 93.67 201 | 97.72 172 |
|
CP-MVSNet | | | 94.94 170 | 94.30 171 | 96.83 160 | 96.72 216 | 95.56 135 | 99.11 43 | 98.95 32 | 93.89 140 | 92.42 244 | 97.90 159 | 87.19 206 | 98.12 252 | 94.32 149 | 88.21 266 | 96.82 212 |
|
XVG-ACMP-BASELINE | | | 94.54 197 | 94.14 180 | 95.75 231 | 96.55 222 | 91.65 247 | 98.11 185 | 98.44 142 | 94.96 105 | 94.22 188 | 97.90 159 | 79.18 282 | 99.11 144 | 94.05 157 | 93.85 199 | 96.48 258 |
|
PS-MVSNAJ | | | 97.73 55 | 97.77 44 | 97.62 126 | 98.68 101 | 95.58 133 | 97.34 251 | 98.51 128 | 97.29 21 | 98.66 37 | 97.88 161 | 94.51 60 | 99.90 23 | 97.87 32 | 99.17 85 | 97.39 180 |
|
TransMVSNet (Re) | | | 92.67 243 | 91.51 247 | 96.15 215 | 96.58 221 | 94.65 181 | 98.90 60 | 96.73 287 | 90.86 245 | 89.46 271 | 97.86 162 | 85.62 231 | 98.09 255 | 86.45 283 | 81.12 301 | 95.71 278 |
|
test_djsdf | | | 96.00 123 | 95.69 125 | 96.93 156 | 95.72 275 | 95.49 138 | 99.47 2 | 98.40 148 | 94.98 103 | 94.58 161 | 97.86 162 | 89.16 142 | 98.41 231 | 96.91 68 | 94.12 193 | 96.88 205 |
|
TinyColmap | | | 92.31 247 | 91.53 246 | 94.65 267 | 96.92 203 | 89.75 268 | 96.92 267 | 96.68 290 | 90.45 248 | 89.62 269 | 97.85 164 | 76.06 295 | 98.81 183 | 86.74 281 | 92.51 220 | 95.41 283 |
|
pm-mvs1 | | | 93.94 226 | 93.06 229 | 96.59 184 | 96.49 226 | 95.16 149 | 98.95 56 | 98.03 216 | 92.32 209 | 91.08 257 | 97.84 165 | 84.54 245 | 98.41 231 | 92.16 205 | 86.13 290 | 96.19 267 |
|
UGNet | | | 96.78 98 | 96.30 102 | 98.19 94 | 98.24 122 | 95.89 126 | 98.88 66 | 98.93 35 | 97.39 17 | 96.81 117 | 97.84 165 | 82.60 261 | 99.90 23 | 96.53 87 | 99.49 66 | 98.79 137 |
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 |
TDRefinement | | | 91.06 267 | 89.68 270 | 95.21 251 | 85.35 319 | 91.49 248 | 98.51 140 | 97.07 275 | 91.47 226 | 88.83 276 | 97.84 165 | 77.31 291 | 99.09 148 | 92.79 192 | 77.98 313 | 95.04 287 |
|
PEN-MVS | | | 94.42 202 | 93.73 208 | 96.49 195 | 96.28 248 | 94.84 166 | 99.17 34 | 99.00 25 | 93.51 162 | 92.23 247 | 97.83 168 | 86.10 224 | 97.90 265 | 92.55 199 | 86.92 283 | 96.74 218 |
|
1314 | | | 96.25 120 | 95.73 119 | 97.79 116 | 97.13 195 | 95.55 137 | 98.19 174 | 98.59 111 | 93.47 164 | 92.03 251 | 97.82 169 | 91.33 111 | 99.49 115 | 94.62 140 | 98.44 115 | 98.32 160 |
|
DTE-MVSNet | | | 93.98 225 | 93.26 228 | 96.14 216 | 96.06 261 | 94.39 195 | 99.20 31 | 98.86 51 | 93.06 176 | 91.78 252 | 97.81 170 | 85.87 228 | 97.58 275 | 90.53 239 | 86.17 288 | 96.46 259 |
|
PAPM | | | 94.95 168 | 94.00 189 | 97.78 117 | 97.04 198 | 95.65 131 | 96.03 293 | 98.25 169 | 91.23 241 | 94.19 190 | 97.80 171 | 91.27 112 | 98.86 178 | 82.61 296 | 97.61 143 | 98.84 134 |
|
PVSNet | | 91.96 18 | 96.35 113 | 96.15 108 | 96.96 153 | 99.17 75 | 92.05 239 | 96.08 290 | 98.68 93 | 93.69 154 | 97.75 79 | 97.80 171 | 88.86 153 | 99.69 94 | 94.26 152 | 99.01 88 | 99.15 109 |
|
CMPMVS | | 66.06 21 | 89.70 276 | 89.67 271 | 89.78 295 | 93.19 301 | 76.56 314 | 97.00 264 | 98.35 155 | 80.97 311 | 81.57 308 | 97.75 173 | 74.75 301 | 98.61 194 | 89.85 250 | 93.63 203 | 94.17 303 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
diffmvs | | | 96.32 115 | 95.74 118 | 98.07 103 | 98.26 121 | 96.14 107 | 98.53 135 | 98.23 172 | 90.10 255 | 96.88 113 | 97.73 174 | 90.16 130 | 99.15 136 | 93.90 160 | 97.85 136 | 98.91 131 |
|
NP-MVS | | | | | | 97.28 183 | 94.51 191 | | | | | 97.73 174 | | | | | |
|
HQP-MVS | | | 95.72 134 | 95.40 129 | 96.69 168 | 97.20 189 | 94.25 201 | 98.05 190 | 98.46 138 | 96.43 54 | 94.45 166 | 97.73 174 | 86.75 212 | 98.96 163 | 95.30 124 | 94.18 189 | 96.86 208 |
|
UniMVSNet_NR-MVSNet | | | 95.71 135 | 95.15 144 | 97.40 134 | 96.84 209 | 96.97 75 | 98.74 101 | 99.24 10 | 95.16 95 | 93.88 203 | 97.72 177 | 91.68 103 | 98.31 242 | 95.81 106 | 87.25 279 | 96.92 196 |
|
DU-MVS | | | 95.42 146 | 94.76 157 | 97.40 134 | 96.53 223 | 96.97 75 | 98.66 118 | 98.99 27 | 95.43 86 | 93.88 203 | 97.69 178 | 88.57 170 | 98.31 242 | 95.81 106 | 87.25 279 | 96.92 196 |
|
WR-MVS | | | 95.15 159 | 94.46 164 | 97.22 137 | 96.67 219 | 96.45 96 | 98.21 171 | 98.81 58 | 94.15 128 | 93.16 222 | 97.69 178 | 87.51 201 | 98.30 244 | 95.29 126 | 88.62 263 | 96.90 203 |
|
NR-MVSNet | | | 94.98 166 | 94.16 178 | 97.44 132 | 96.53 223 | 97.22 68 | 98.74 101 | 98.95 32 | 94.96 105 | 89.25 273 | 97.69 178 | 89.32 137 | 98.18 250 | 94.59 142 | 87.40 276 | 96.92 196 |
|
Fast-Effi-MVS+-dtu | | | 95.87 128 | 95.85 116 | 95.91 223 | 97.74 154 | 91.74 246 | 98.69 111 | 98.15 188 | 95.56 81 | 94.92 152 | 97.68 181 | 88.98 148 | 98.79 185 | 93.19 177 | 97.78 139 | 97.20 185 |
|
alignmvs | | | 97.56 64 | 97.07 73 | 99.01 45 | 98.66 102 | 98.37 20 | 98.83 74 | 98.06 212 | 96.74 46 | 98.00 67 | 97.65 182 | 90.80 121 | 99.48 119 | 98.37 18 | 96.56 160 | 99.19 103 |
|
LF4IMVS | | | 93.14 240 | 92.79 234 | 94.20 276 | 95.88 269 | 88.67 284 | 97.66 230 | 97.07 275 | 93.81 145 | 91.71 253 | 97.65 182 | 77.96 286 | 98.81 183 | 91.47 225 | 91.92 227 | 95.12 285 |
|
lessismore_v0 | | | | | 94.45 274 | 94.93 291 | 88.44 288 | | 91.03 325 | | 86.77 284 | 97.64 184 | 76.23 294 | 98.42 224 | 90.31 242 | 85.64 292 | 96.51 255 |
|
TR-MVS | | | 94.94 170 | 94.20 176 | 97.17 141 | 97.75 153 | 94.14 203 | 97.59 234 | 97.02 279 | 92.28 211 | 95.75 143 | 97.64 184 | 83.88 254 | 98.96 163 | 89.77 251 | 96.15 172 | 98.40 157 |
|
Baseline_NR-MVSNet | | | 94.35 205 | 93.81 200 | 95.96 221 | 96.20 253 | 94.05 205 | 98.61 123 | 96.67 291 | 91.44 228 | 93.85 205 | 97.60 186 | 88.57 170 | 98.14 251 | 94.39 146 | 86.93 282 | 95.68 279 |
|
pmmvs4 | | | 94.69 185 | 93.99 191 | 96.81 161 | 95.74 273 | 95.94 119 | 97.40 243 | 97.67 228 | 90.42 249 | 93.37 217 | 97.59 187 | 89.08 144 | 98.20 249 | 92.97 184 | 91.67 230 | 96.30 265 |
|
K. test v3 | | | 92.55 244 | 91.91 245 | 94.48 271 | 95.64 277 | 89.24 275 | 99.07 45 | 94.88 311 | 94.04 132 | 86.78 283 | 97.59 187 | 77.64 290 | 97.64 273 | 92.08 207 | 89.43 249 | 96.57 247 |
|
PAPR | | | 96.84 95 | 96.24 106 | 98.65 64 | 98.72 97 | 96.92 78 | 97.36 249 | 98.57 117 | 93.33 168 | 96.67 121 | 97.57 189 | 94.30 67 | 99.56 110 | 91.05 232 | 98.59 108 | 99.47 77 |
|
pmmvs6 | | | 91.77 260 | 90.63 261 | 95.17 253 | 94.69 295 | 91.24 252 | 98.67 116 | 97.92 219 | 86.14 292 | 89.62 269 | 97.56 190 | 75.79 296 | 98.34 238 | 90.75 235 | 84.56 295 | 95.94 273 |
|
MS-PatchMatch | | | 93.84 228 | 93.63 212 | 94.46 273 | 96.18 254 | 89.45 272 | 97.76 222 | 98.27 164 | 92.23 212 | 92.13 250 | 97.49 191 | 79.50 279 | 98.69 188 | 89.75 253 | 99.38 78 | 95.25 284 |
|
semantic-postprocess | | | | | 94.85 261 | 97.98 141 | 90.56 262 | | 98.11 198 | 93.75 146 | 92.58 237 | 97.48 192 | 83.91 253 | 97.41 279 | 92.48 202 | 91.30 233 | 96.58 245 |
|
anonymousdsp | | | 95.42 146 | 94.91 154 | 96.94 155 | 95.10 288 | 95.90 125 | 99.14 37 | 98.41 146 | 93.75 146 | 93.16 222 | 97.46 193 | 87.50 203 | 98.41 231 | 95.63 116 | 94.03 195 | 96.50 256 |
|
PVSNet_BlendedMVS | | | 96.73 100 | 96.60 92 | 97.12 144 | 99.25 64 | 95.35 144 | 98.26 168 | 99.26 8 | 94.28 126 | 97.94 70 | 97.46 193 | 92.74 83 | 99.81 50 | 96.88 73 | 93.32 211 | 96.20 266 |
|
PMMVS | | | 96.60 103 | 96.33 101 | 97.41 133 | 97.90 145 | 93.93 207 | 97.35 250 | 98.41 146 | 92.84 186 | 97.76 78 | 97.45 195 | 91.10 116 | 99.20 133 | 96.26 94 | 97.91 132 | 99.11 113 |
|
canonicalmvs | | | 97.67 58 | 97.23 66 | 98.98 48 | 98.70 98 | 98.38 17 | 99.34 11 | 98.39 150 | 96.76 45 | 97.67 85 | 97.40 196 | 92.26 89 | 99.49 115 | 98.28 21 | 96.28 167 | 99.08 117 |
|
IterMVS | | | 94.09 220 | 93.85 199 | 94.80 264 | 97.99 139 | 90.35 264 | 97.18 260 | 98.12 193 | 93.68 156 | 92.46 243 | 97.34 197 | 84.05 251 | 97.41 279 | 92.51 201 | 91.33 232 | 96.62 240 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
VPA-MVSNet | | | 95.75 133 | 95.11 145 | 97.69 123 | 97.24 185 | 97.27 65 | 98.94 57 | 99.23 12 | 95.13 96 | 95.51 144 | 97.32 198 | 85.73 229 | 98.91 170 | 97.33 57 | 89.55 247 | 96.89 204 |
|
IterMVS-LS | | | 95.46 143 | 95.21 142 | 96.22 213 | 98.12 131 | 93.72 215 | 98.32 160 | 98.13 191 | 93.71 151 | 94.26 185 | 97.31 199 | 92.24 90 | 98.10 253 | 94.63 139 | 90.12 239 | 96.84 209 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Test_1112_low_res | | | 96.34 114 | 95.66 127 | 98.36 84 | 98.56 109 | 95.94 119 | 97.71 225 | 98.07 209 | 92.10 213 | 94.79 158 | 97.29 200 | 91.75 102 | 99.56 110 | 94.17 153 | 96.50 163 | 99.58 63 |
|
pmmvs5 | | | 93.65 231 | 92.97 231 | 95.68 232 | 95.49 282 | 92.37 235 | 98.20 172 | 97.28 268 | 89.66 269 | 92.58 237 | 97.26 201 | 82.14 262 | 98.09 255 | 93.18 178 | 90.95 236 | 96.58 245 |
|
MDTV_nov1_ep13 | | | | 95.40 129 | | 97.48 169 | 88.34 289 | 96.85 275 | 97.29 267 | 93.74 148 | 97.48 95 | 97.26 201 | 89.18 141 | 99.05 151 | 91.92 215 | 97.43 146 | |
|
Fast-Effi-MVS+ | | | 96.28 118 | 95.70 124 | 98.03 105 | 98.29 120 | 95.97 115 | 98.58 126 | 98.25 169 | 91.74 221 | 95.29 148 | 97.23 203 | 91.03 118 | 99.15 136 | 92.90 189 | 97.96 131 | 98.97 125 |
|
BH-w/o | | | 95.38 149 | 95.08 146 | 96.26 212 | 98.34 117 | 91.79 243 | 97.70 226 | 97.43 255 | 92.87 185 | 94.24 187 | 97.22 204 | 88.66 168 | 98.84 179 | 91.55 222 | 97.70 142 | 98.16 163 |
|
v1921920 | | | 94.20 212 | 93.47 223 | 96.40 203 | 95.98 264 | 94.08 204 | 98.52 136 | 98.15 188 | 91.33 235 | 94.25 186 | 97.20 205 | 86.41 219 | 98.42 224 | 90.04 248 | 89.39 250 | 96.69 230 |
|
v7 | | | 94.69 185 | 94.04 186 | 96.62 181 | 96.41 230 | 94.79 176 | 98.78 92 | 98.13 191 | 91.89 217 | 94.30 182 | 97.16 206 | 88.13 183 | 98.45 218 | 91.96 214 | 89.65 244 | 96.61 241 |
|
v2v482 | | | 94.69 185 | 94.03 187 | 96.65 176 | 96.17 255 | 94.79 176 | 98.67 116 | 98.08 208 | 92.72 188 | 94.00 200 | 97.16 206 | 87.69 198 | 98.45 218 | 92.91 188 | 88.87 258 | 96.72 221 |
|
v7n | | | 94.19 213 | 93.43 224 | 96.47 197 | 95.90 267 | 94.38 196 | 99.26 17 | 98.34 156 | 91.99 215 | 92.76 233 | 97.13 208 | 88.31 177 | 98.52 207 | 89.48 260 | 87.70 273 | 96.52 253 |
|
Patchmatch-test | | | 94.42 202 | 93.68 211 | 96.63 179 | 97.60 161 | 91.76 244 | 94.83 308 | 97.49 250 | 89.45 273 | 94.14 193 | 97.10 209 | 88.99 145 | 98.83 181 | 85.37 291 | 98.13 127 | 99.29 95 |
|
FMVSNet3 | | | 94.97 167 | 94.26 172 | 97.11 145 | 98.18 129 | 96.62 88 | 98.56 131 | 98.26 168 | 93.67 158 | 94.09 195 | 97.10 209 | 84.25 248 | 98.01 259 | 92.08 207 | 92.14 222 | 96.70 225 |
|
MVP-Stereo | | | 94.28 210 | 93.92 194 | 95.35 249 | 94.95 290 | 92.60 234 | 97.97 198 | 97.65 229 | 91.61 223 | 90.68 262 | 97.09 211 | 86.32 221 | 98.42 224 | 89.70 255 | 99.34 79 | 95.02 288 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
FMVSNet2 | | | 94.47 200 | 93.61 214 | 97.04 148 | 98.21 124 | 96.43 97 | 98.79 90 | 98.27 164 | 92.46 194 | 93.50 215 | 97.09 211 | 81.16 266 | 98.00 260 | 91.09 228 | 91.93 226 | 96.70 225 |
|
GBi-Net | | | 94.49 198 | 93.80 201 | 96.56 189 | 98.21 124 | 95.00 154 | 98.82 76 | 98.18 180 | 92.46 194 | 94.09 195 | 97.07 213 | 81.16 266 | 97.95 262 | 92.08 207 | 92.14 222 | 96.72 221 |
|
test1 | | | 94.49 198 | 93.80 201 | 96.56 189 | 98.21 124 | 95.00 154 | 98.82 76 | 98.18 180 | 92.46 194 | 94.09 195 | 97.07 213 | 81.16 266 | 97.95 262 | 92.08 207 | 92.14 222 | 96.72 221 |
|
FMVSNet1 | | | 93.19 239 | 92.07 242 | 96.56 189 | 97.54 166 | 95.00 154 | 98.82 76 | 98.18 180 | 90.38 250 | 92.27 246 | 97.07 213 | 73.68 305 | 97.95 262 | 89.36 262 | 91.30 233 | 96.72 221 |
|
v1192 | | | 94.32 206 | 93.58 216 | 96.53 192 | 96.10 259 | 94.45 192 | 98.50 141 | 98.17 185 | 91.54 225 | 94.19 190 | 97.06 216 | 86.95 211 | 98.43 223 | 90.14 243 | 89.57 245 | 96.70 225 |
|
v1neww | | | 94.83 173 | 94.22 173 | 96.68 171 | 96.39 231 | 94.85 161 | 98.87 67 | 98.11 198 | 92.45 199 | 94.45 166 | 97.06 216 | 88.82 158 | 98.54 200 | 92.93 186 | 88.91 256 | 96.65 236 |
|
v7new | | | 94.83 173 | 94.22 173 | 96.68 171 | 96.39 231 | 94.85 161 | 98.87 67 | 98.11 198 | 92.45 199 | 94.45 166 | 97.06 216 | 88.82 158 | 98.54 200 | 92.93 186 | 88.91 256 | 96.65 236 |
|
V42 | | | 94.78 178 | 94.14 180 | 96.70 167 | 96.33 242 | 95.22 148 | 98.97 55 | 98.09 206 | 92.32 209 | 94.31 180 | 97.06 216 | 88.39 176 | 98.55 199 | 92.90 189 | 88.87 258 | 96.34 263 |
|
v6 | | | 94.83 173 | 94.21 175 | 96.69 168 | 96.36 235 | 94.85 161 | 98.87 67 | 98.11 198 | 92.46 194 | 94.44 172 | 97.05 220 | 88.76 164 | 98.57 198 | 92.95 185 | 88.92 255 | 96.65 236 |
|
GA-MVS | | | 94.81 177 | 94.03 187 | 97.14 142 | 97.15 194 | 93.86 209 | 96.76 278 | 97.58 231 | 94.00 134 | 94.76 159 | 97.04 221 | 80.91 269 | 98.48 211 | 91.79 217 | 96.25 169 | 99.09 114 |
|
UniMVSNet (Re) | | | 95.78 132 | 95.19 143 | 97.58 128 | 96.99 201 | 97.47 59 | 98.79 90 | 99.18 15 | 95.60 79 | 93.92 202 | 97.04 221 | 91.68 103 | 98.48 211 | 95.80 108 | 87.66 274 | 96.79 213 |
|
v144192 | | | 94.39 204 | 93.70 209 | 96.48 196 | 96.06 261 | 94.35 197 | 98.58 126 | 98.16 187 | 91.45 227 | 94.33 178 | 97.02 223 | 87.50 203 | 98.45 218 | 91.08 229 | 89.11 252 | 96.63 239 |
|
v1144 | | | 94.59 194 | 93.92 194 | 96.60 183 | 96.21 252 | 94.78 178 | 98.59 124 | 98.14 190 | 91.86 220 | 94.21 189 | 97.02 223 | 87.97 186 | 98.41 231 | 91.72 219 | 89.57 245 | 96.61 241 |
|
v1240 | | | 94.06 223 | 93.29 227 | 96.34 208 | 96.03 263 | 93.90 208 | 98.44 146 | 98.17 185 | 91.18 243 | 94.13 194 | 97.01 225 | 86.05 225 | 98.42 224 | 89.13 265 | 89.50 248 | 96.70 225 |
|
v10 | | | 94.29 208 | 93.55 217 | 96.51 194 | 96.39 231 | 94.80 173 | 98.99 51 | 98.19 177 | 91.35 234 | 93.02 228 | 96.99 226 | 88.09 184 | 98.41 231 | 90.50 240 | 88.41 265 | 96.33 264 |
|
test_0402 | | | 91.32 263 | 90.27 265 | 94.48 271 | 96.60 220 | 91.12 253 | 98.50 141 | 97.22 272 | 86.10 293 | 88.30 278 | 96.98 227 | 77.65 289 | 97.99 261 | 78.13 307 | 92.94 217 | 94.34 301 |
|
v8 | | | 94.47 200 | 93.77 204 | 96.57 188 | 96.36 235 | 94.83 168 | 99.05 46 | 98.19 177 | 91.92 216 | 93.16 222 | 96.97 228 | 88.82 158 | 98.48 211 | 91.69 220 | 87.79 272 | 96.39 260 |
|
PatchmatchNet | | | 95.71 135 | 95.52 128 | 96.29 211 | 97.58 163 | 90.72 258 | 96.84 276 | 97.52 238 | 94.06 131 | 97.08 100 | 96.96 229 | 89.24 140 | 98.90 173 | 92.03 211 | 98.37 118 | 99.26 98 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
Patchmatch-test1 | | | 95.32 154 | 94.97 152 | 96.35 206 | 97.67 156 | 91.29 251 | 97.33 252 | 97.60 230 | 94.68 112 | 96.92 110 | 96.95 230 | 83.97 252 | 98.50 210 | 91.33 227 | 98.32 121 | 99.25 99 |
|
v148 | | | 94.29 208 | 93.76 206 | 95.91 223 | 96.10 259 | 92.93 230 | 98.58 126 | 97.97 217 | 92.59 192 | 93.47 216 | 96.95 230 | 88.53 173 | 98.32 240 | 92.56 198 | 87.06 281 | 96.49 257 |
|
gm-plane-assit | | | | | | 95.88 269 | 87.47 295 | | | 89.74 267 | | 96.94 232 | | 99.19 134 | 93.32 174 | | |
|
v1141 | | | 94.75 181 | 94.11 184 | 96.67 174 | 96.27 250 | 94.86 160 | 98.69 111 | 98.12 193 | 92.43 202 | 94.31 180 | 96.94 232 | 88.78 163 | 98.48 211 | 92.63 196 | 88.85 260 | 96.67 231 |
|
divwei89l23v2f112 | | | 94.76 179 | 94.12 183 | 96.67 174 | 96.28 248 | 94.85 161 | 98.69 111 | 98.12 193 | 92.44 201 | 94.29 183 | 96.94 232 | 88.85 155 | 98.48 211 | 92.67 194 | 88.79 262 | 96.67 231 |
|
v1 | | | 94.75 181 | 94.11 184 | 96.69 168 | 96.27 250 | 94.87 159 | 98.69 111 | 98.12 193 | 92.43 202 | 94.32 179 | 96.94 232 | 88.71 167 | 98.54 200 | 92.66 195 | 88.84 261 | 96.67 231 |
|
tpmrst | | | 95.63 139 | 95.69 125 | 95.44 241 | 97.54 166 | 88.54 287 | 96.97 265 | 97.56 232 | 93.50 163 | 97.52 94 | 96.93 236 | 89.49 133 | 99.16 135 | 95.25 128 | 96.42 164 | 98.64 147 |
|
v52 | | | 94.18 215 | 93.52 219 | 96.13 217 | 95.95 266 | 94.29 199 | 99.23 21 | 98.21 174 | 91.42 229 | 92.84 231 | 96.89 237 | 87.85 192 | 98.53 206 | 91.51 223 | 87.81 270 | 95.57 282 |
|
V4 | | | 94.18 215 | 93.52 219 | 96.13 217 | 95.89 268 | 94.31 198 | 99.23 21 | 98.22 173 | 91.42 229 | 92.82 232 | 96.89 237 | 87.93 188 | 98.52 207 | 91.51 223 | 87.81 270 | 95.58 281 |
|
LCM-MVSNet-Re | | | 95.22 156 | 95.32 137 | 94.91 258 | 98.18 129 | 87.85 294 | 98.75 97 | 95.66 305 | 95.11 97 | 88.96 275 | 96.85 239 | 90.26 129 | 97.65 272 | 95.65 115 | 98.44 115 | 99.22 102 |
|
WR-MVS_H | | | 95.05 162 | 94.46 164 | 96.81 161 | 96.86 208 | 95.82 128 | 99.24 20 | 99.24 10 | 93.87 142 | 92.53 239 | 96.84 240 | 90.37 125 | 98.24 248 | 93.24 175 | 87.93 269 | 96.38 261 |
|
EPMVS | | | 94.99 164 | 94.48 162 | 96.52 193 | 97.22 187 | 91.75 245 | 97.23 257 | 91.66 324 | 94.11 129 | 97.28 96 | 96.81 241 | 85.70 230 | 98.84 179 | 93.04 182 | 97.28 147 | 98.97 125 |
|
tpm2 | | | 94.19 213 | 93.76 206 | 95.46 239 | 97.23 186 | 89.04 279 | 97.31 254 | 96.85 286 | 87.08 288 | 96.21 134 | 96.79 242 | 83.75 257 | 98.74 187 | 92.43 203 | 96.23 170 | 98.59 149 |
|
tpmp4_e23 | | | 93.91 227 | 93.42 226 | 95.38 247 | 97.62 159 | 88.59 286 | 97.52 238 | 97.34 262 | 87.94 284 | 94.17 192 | 96.79 242 | 82.91 259 | 99.05 151 | 90.62 238 | 95.91 175 | 98.50 152 |
|
CostFormer | | | 94.95 168 | 94.73 158 | 95.60 234 | 97.28 183 | 89.06 278 | 97.53 237 | 96.89 283 | 89.66 269 | 96.82 116 | 96.72 244 | 86.05 225 | 98.95 167 | 95.53 118 | 96.13 173 | 98.79 137 |
|
test20.03 | | | 90.89 269 | 90.38 263 | 92.43 288 | 93.48 300 | 88.14 291 | 98.33 156 | 97.56 232 | 93.40 166 | 87.96 279 | 96.71 245 | 80.69 273 | 94.13 312 | 79.15 304 | 86.17 288 | 95.01 289 |
|
Effi-MVS+-dtu | | | 96.29 116 | 96.56 93 | 95.51 235 | 97.89 146 | 90.22 265 | 98.80 85 | 98.10 203 | 96.57 52 | 96.45 130 | 96.66 246 | 90.81 119 | 98.91 170 | 95.72 110 | 97.99 130 | 97.40 179 |
|
test0.0.03 1 | | | 94.08 221 | 93.51 221 | 95.80 228 | 95.53 281 | 92.89 231 | 97.38 245 | 95.97 301 | 95.11 97 | 92.51 241 | 96.66 246 | 87.71 195 | 96.94 285 | 87.03 280 | 93.67 201 | 97.57 175 |
|
ADS-MVSNet2 | | | 94.58 195 | 94.40 168 | 95.11 255 | 98.00 137 | 88.74 282 | 96.04 291 | 97.30 266 | 90.15 252 | 96.47 128 | 96.64 248 | 87.89 189 | 97.56 276 | 90.08 245 | 97.06 149 | 99.02 120 |
|
ADS-MVSNet | | | 95.00 163 | 94.45 166 | 96.63 179 | 98.00 137 | 91.91 241 | 96.04 291 | 97.74 226 | 90.15 252 | 96.47 128 | 96.64 248 | 87.89 189 | 98.96 163 | 90.08 245 | 97.06 149 | 99.02 120 |
|
dp | | | 94.15 218 | 93.90 196 | 94.90 259 | 97.31 182 | 86.82 299 | 96.97 265 | 97.19 273 | 91.22 242 | 96.02 140 | 96.61 250 | 85.51 232 | 99.02 158 | 90.00 249 | 94.30 184 | 98.85 132 |
|
v748 | | | 93.75 229 | 93.06 229 | 95.82 227 | 95.73 274 | 92.64 233 | 99.25 19 | 98.24 171 | 91.60 224 | 92.22 248 | 96.52 251 | 87.60 200 | 98.46 216 | 90.64 237 | 85.72 291 | 96.36 262 |
|
EG-PatchMatch MVS | | | 91.13 265 | 90.12 266 | 94.17 278 | 94.73 294 | 89.00 280 | 98.13 182 | 97.81 222 | 89.22 277 | 85.32 292 | 96.46 252 | 67.71 314 | 98.42 224 | 87.89 276 | 93.82 200 | 95.08 286 |
|
TESTMET0.1,1 | | | 94.18 215 | 93.69 210 | 95.63 233 | 96.92 203 | 89.12 277 | 96.91 269 | 94.78 312 | 93.17 173 | 94.88 153 | 96.45 253 | 78.52 283 | 98.92 169 | 93.09 179 | 98.50 112 | 98.85 132 |
|
DWT-MVSNet_test | | | 94.82 176 | 94.36 169 | 96.20 214 | 97.35 180 | 90.79 256 | 98.34 155 | 96.57 294 | 92.91 183 | 95.33 147 | 96.44 254 | 82.00 263 | 99.12 140 | 94.52 144 | 95.78 178 | 98.70 142 |
|
tpmvs | | | 94.60 192 | 94.36 169 | 95.33 250 | 97.46 171 | 88.60 285 | 96.88 274 | 97.68 227 | 91.29 238 | 93.80 207 | 96.42 255 | 88.58 169 | 99.24 130 | 91.06 230 | 96.04 174 | 98.17 162 |
|
Anonymous20231206 | | | 91.66 261 | 91.10 249 | 93.33 283 | 94.02 299 | 87.35 296 | 98.58 126 | 97.26 270 | 90.48 246 | 90.16 265 | 96.31 256 | 83.83 256 | 96.53 301 | 79.36 303 | 89.90 242 | 96.12 268 |
|
tpm | | | 94.13 219 | 93.80 201 | 95.12 254 | 96.50 225 | 87.91 293 | 97.44 240 | 95.89 304 | 92.62 190 | 96.37 132 | 96.30 257 | 84.13 250 | 98.30 244 | 93.24 175 | 91.66 231 | 99.14 111 |
|
CR-MVSNet | | | 94.76 179 | 94.15 179 | 96.59 184 | 97.00 199 | 93.43 220 | 94.96 304 | 97.56 232 | 92.46 194 | 96.93 108 | 96.24 258 | 88.15 181 | 97.88 269 | 87.38 277 | 96.65 157 | 98.46 154 |
|
Patchmtry | | | 93.22 238 | 92.35 239 | 95.84 226 | 96.77 211 | 93.09 229 | 94.66 310 | 97.56 232 | 87.37 287 | 92.90 230 | 96.24 258 | 88.15 181 | 97.90 265 | 87.37 278 | 90.10 240 | 96.53 252 |
|
tmp_tt | | | 68.90 300 | 66.97 300 | 74.68 315 | 50.78 334 | 59.95 330 | 87.13 322 | 83.47 333 | 38.80 329 | 62.21 323 | 96.23 260 | 64.70 318 | 76.91 332 | 88.91 266 | 30.49 329 | 87.19 317 |
|
cascas | | | 94.63 191 | 93.86 198 | 96.93 156 | 96.91 205 | 94.27 200 | 96.00 294 | 98.51 128 | 85.55 297 | 94.54 162 | 96.23 260 | 84.20 249 | 98.87 176 | 95.80 108 | 96.98 152 | 97.66 174 |
|
UnsupCasMVSNet_eth | | | 90.99 268 | 89.92 269 | 94.19 277 | 94.08 298 | 89.83 267 | 97.13 262 | 98.67 100 | 93.69 154 | 85.83 289 | 96.19 262 | 75.15 298 | 96.74 295 | 89.14 264 | 79.41 307 | 96.00 271 |
|
PatchFormer-LS_test | | | 95.47 142 | 95.27 140 | 96.08 219 | 97.59 162 | 90.66 259 | 98.10 187 | 97.34 262 | 93.98 136 | 96.08 136 | 96.15 263 | 87.65 199 | 99.12 140 | 95.27 127 | 95.24 181 | 98.44 156 |
|
MDA-MVSNet-bldmvs | | | 89.97 275 | 88.35 281 | 94.83 263 | 95.21 287 | 91.34 249 | 97.64 231 | 97.51 241 | 88.36 282 | 71.17 319 | 96.13 264 | 79.22 281 | 96.63 300 | 83.65 293 | 86.27 287 | 96.52 253 |
|
MIMVSNet | | | 93.26 237 | 92.21 241 | 96.41 202 | 97.73 155 | 93.13 228 | 95.65 299 | 97.03 278 | 91.27 240 | 94.04 198 | 96.06 265 | 75.33 297 | 97.19 282 | 86.56 282 | 96.23 170 | 98.92 130 |
|
tpm cat1 | | | 93.36 232 | 92.80 233 | 95.07 256 | 97.58 163 | 87.97 292 | 96.76 278 | 97.86 221 | 82.17 309 | 93.53 212 | 96.04 266 | 86.13 223 | 99.13 139 | 89.24 263 | 95.87 176 | 98.10 164 |
|
N_pmnet | | | 87.12 286 | 87.77 283 | 85.17 306 | 95.46 283 | 61.92 328 | 97.37 247 | 70.66 335 | 85.83 296 | 88.73 277 | 96.04 266 | 85.33 237 | 97.76 271 | 80.02 300 | 90.48 238 | 95.84 274 |
|
DI_MVS_plusplus_test | | | 94.74 183 | 93.62 213 | 98.09 100 | 95.34 285 | 95.92 122 | 98.09 188 | 97.34 262 | 94.66 115 | 85.89 287 | 95.91 268 | 80.49 275 | 99.38 123 | 96.66 82 | 98.22 123 | 98.97 125 |
|
test_normal | | | 94.72 184 | 93.59 215 | 98.11 99 | 95.30 286 | 95.95 118 | 97.91 205 | 97.39 260 | 94.64 116 | 85.70 290 | 95.88 269 | 80.52 274 | 99.36 124 | 96.69 81 | 98.30 122 | 99.01 123 |
|
MIMVSNet1 | | | 89.67 277 | 88.28 282 | 93.82 279 | 92.81 304 | 91.08 254 | 98.01 194 | 97.45 253 | 87.95 283 | 87.90 280 | 95.87 270 | 67.63 315 | 94.56 311 | 78.73 306 | 88.18 267 | 95.83 275 |
|
YYNet1 | | | 90.70 271 | 89.39 272 | 94.62 268 | 94.79 293 | 90.65 260 | 97.20 258 | 97.46 251 | 87.54 286 | 72.54 317 | 95.74 271 | 86.51 217 | 96.66 299 | 86.00 286 | 86.76 286 | 96.54 251 |
|
DSMNet-mixed | | | 92.52 245 | 92.58 236 | 92.33 289 | 94.15 297 | 82.65 307 | 98.30 163 | 94.26 317 | 89.08 278 | 92.65 235 | 95.73 272 | 85.01 240 | 95.76 306 | 86.24 284 | 97.76 140 | 98.59 149 |
|
IB-MVS | | 91.98 17 | 93.27 236 | 91.97 243 | 97.19 139 | 97.47 170 | 93.41 222 | 97.09 263 | 95.99 300 | 93.32 169 | 92.47 242 | 95.73 272 | 78.06 285 | 99.53 113 | 94.59 142 | 82.98 296 | 98.62 148 |
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 |
test-LLR | | | 95.10 161 | 94.87 155 | 95.80 228 | 96.77 211 | 89.70 269 | 96.91 269 | 95.21 307 | 95.11 97 | 94.83 156 | 95.72 274 | 87.71 195 | 98.97 160 | 93.06 180 | 98.50 112 | 98.72 140 |
|
test-mter | | | 94.08 221 | 93.51 221 | 95.80 228 | 96.77 211 | 89.70 269 | 96.91 269 | 95.21 307 | 92.89 184 | 94.83 156 | 95.72 274 | 77.69 287 | 98.97 160 | 93.06 180 | 98.50 112 | 98.72 140 |
|
MDA-MVSNet_test_wron | | | 90.71 270 | 89.38 273 | 94.68 266 | 94.83 292 | 90.78 257 | 97.19 259 | 97.46 251 | 87.60 285 | 72.41 318 | 95.72 274 | 86.51 217 | 96.71 298 | 85.92 287 | 86.80 285 | 96.56 249 |
|
FMVSNet5 | | | 91.81 259 | 90.92 253 | 94.49 270 | 97.21 188 | 92.09 238 | 98.00 196 | 97.55 236 | 89.31 276 | 90.86 260 | 95.61 277 | 74.48 302 | 95.32 308 | 85.57 289 | 89.70 243 | 96.07 270 |
|
PVSNet_0 | | 88.72 19 | 91.28 264 | 90.03 267 | 95.00 257 | 97.99 139 | 87.29 297 | 94.84 307 | 98.50 133 | 92.06 214 | 89.86 267 | 95.19 278 | 79.81 278 | 99.39 122 | 92.27 204 | 69.79 320 | 98.33 159 |
|
DeepMVS_CX | | | | | 86.78 302 | 97.09 197 | 72.30 321 | | 95.17 310 | 75.92 316 | 84.34 303 | 95.19 278 | 70.58 310 | 95.35 307 | 79.98 302 | 89.04 254 | 92.68 311 |
|
testus | | | 88.91 280 | 89.08 275 | 88.40 298 | 91.39 306 | 76.05 315 | 96.56 284 | 96.48 295 | 89.38 275 | 89.39 272 | 95.17 280 | 70.94 309 | 93.56 315 | 77.04 309 | 95.41 180 | 95.61 280 |
|
patchmatchnet-post | | | | | | | | | | | | 95.10 281 | 89.42 135 | 98.89 174 | | | |
|
Patchmatch-RL test | | | 91.49 262 | 90.85 254 | 93.41 282 | 91.37 307 | 84.40 301 | 92.81 316 | 95.93 303 | 91.87 219 | 87.25 281 | 94.87 282 | 88.99 145 | 96.53 301 | 92.54 200 | 82.00 298 | 99.30 93 |
|
LP | | | 91.12 266 | 89.99 268 | 94.53 269 | 96.35 237 | 88.70 283 | 93.86 315 | 97.35 261 | 84.88 300 | 90.98 258 | 94.77 283 | 84.40 247 | 97.43 278 | 75.41 313 | 91.89 228 | 97.47 176 |
|
OpenMVS_ROB | | 86.42 20 | 89.00 279 | 87.43 285 | 93.69 280 | 93.08 302 | 89.42 273 | 97.91 205 | 96.89 283 | 78.58 314 | 85.86 288 | 94.69 284 | 69.48 311 | 98.29 246 | 77.13 308 | 93.29 213 | 93.36 310 |
|
Test4 | | | 92.21 248 | 90.34 264 | 97.82 115 | 92.83 303 | 95.87 127 | 97.94 201 | 98.05 215 | 94.50 121 | 82.12 306 | 94.48 285 | 59.54 321 | 98.54 200 | 95.39 122 | 98.22 123 | 99.06 119 |
|
FPMVS | | | 77.62 296 | 77.14 294 | 79.05 311 | 79.25 325 | 60.97 329 | 95.79 297 | 95.94 302 | 65.96 320 | 67.93 321 | 94.40 286 | 37.73 329 | 88.88 325 | 68.83 318 | 88.46 264 | 87.29 316 |
|
testpf | | | 88.74 281 | 89.09 274 | 87.69 299 | 95.78 272 | 83.16 306 | 84.05 326 | 94.13 320 | 85.22 299 | 90.30 264 | 94.39 287 | 74.92 300 | 95.80 305 | 89.77 251 | 93.28 214 | 84.10 320 |
|
GG-mvs-BLEND | | | | | 96.59 184 | 96.34 238 | 94.98 157 | 96.51 288 | 88.58 328 | | 93.10 227 | 94.34 288 | 80.34 277 | 98.05 257 | 89.53 258 | 96.99 151 | 96.74 218 |
|
test2356 | | | 88.68 282 | 88.61 278 | 88.87 297 | 89.90 312 | 78.23 312 | 95.11 302 | 96.66 293 | 88.66 281 | 89.06 274 | 94.33 289 | 73.14 307 | 92.56 319 | 75.56 312 | 95.11 182 | 95.81 276 |
|
new_pmnet | | | 90.06 274 | 89.00 277 | 93.22 286 | 94.18 296 | 88.32 290 | 96.42 289 | 96.89 283 | 86.19 291 | 85.67 291 | 93.62 290 | 77.18 292 | 97.10 283 | 81.61 298 | 89.29 251 | 94.23 302 |
|
PM-MVS | | | 87.77 284 | 86.55 286 | 91.40 293 | 91.03 309 | 83.36 305 | 96.92 267 | 95.18 309 | 91.28 239 | 86.48 286 | 93.42 291 | 53.27 322 | 96.74 295 | 89.43 261 | 81.97 299 | 94.11 304 |
|
v16 | | | 92.08 251 | 90.94 251 | 95.49 237 | 96.38 234 | 94.84 166 | 98.81 82 | 97.51 241 | 89.94 260 | 85.25 295 | 93.28 292 | 88.86 153 | 96.91 287 | 88.70 269 | 79.78 304 | 94.72 292 |
|
v18 | | | 92.10 250 | 90.97 250 | 95.50 236 | 96.34 238 | 94.85 161 | 98.82 76 | 97.52 238 | 89.99 257 | 85.31 294 | 93.26 293 | 88.90 152 | 96.92 286 | 88.82 267 | 79.77 305 | 94.73 291 |
|
v17 | | | 92.08 251 | 90.94 251 | 95.48 238 | 96.34 238 | 94.83 168 | 98.81 82 | 97.52 238 | 89.95 259 | 85.32 292 | 93.24 294 | 88.91 151 | 96.91 287 | 88.76 268 | 79.63 306 | 94.71 293 |
|
pmmvs-eth3d | | | 90.36 273 | 89.05 276 | 94.32 275 | 91.10 308 | 92.12 237 | 97.63 233 | 96.95 282 | 88.86 279 | 84.91 302 | 93.13 295 | 78.32 284 | 96.74 295 | 88.70 269 | 81.81 300 | 94.09 305 |
|
V14 | | | 91.93 254 | 90.76 256 | 95.42 246 | 96.33 242 | 94.81 172 | 98.77 93 | 97.51 241 | 89.86 263 | 85.09 297 | 93.13 295 | 88.80 162 | 96.83 291 | 88.32 272 | 79.06 310 | 94.60 298 |
|
v15 | | | 91.94 253 | 90.77 255 | 95.43 243 | 96.31 246 | 94.83 168 | 98.77 93 | 97.50 244 | 89.92 261 | 85.13 296 | 93.08 297 | 88.76 164 | 96.86 289 | 88.40 271 | 79.10 308 | 94.61 297 |
|
V9 | | | 91.91 255 | 90.73 257 | 95.45 240 | 96.32 245 | 94.80 173 | 98.77 93 | 97.50 244 | 89.81 264 | 85.03 299 | 93.08 297 | 88.76 164 | 96.86 289 | 88.24 273 | 79.03 311 | 94.69 294 |
|
v11 | | | 91.85 258 | 90.68 260 | 95.36 248 | 96.34 238 | 94.74 180 | 98.80 85 | 97.43 255 | 89.60 271 | 85.09 297 | 93.03 299 | 88.53 173 | 96.75 294 | 87.37 278 | 79.96 303 | 94.58 299 |
|
v12 | | | 91.89 256 | 90.70 258 | 95.43 243 | 96.31 246 | 94.80 173 | 98.76 96 | 97.50 244 | 89.76 265 | 84.95 300 | 93.00 300 | 88.82 158 | 96.82 293 | 88.23 274 | 79.00 312 | 94.68 296 |
|
v13 | | | 91.88 257 | 90.69 259 | 95.43 243 | 96.33 242 | 94.78 178 | 98.75 97 | 97.50 244 | 89.68 268 | 84.93 301 | 92.98 301 | 88.84 156 | 96.83 291 | 88.14 275 | 79.09 309 | 94.69 294 |
|
test1235678 | | | 86.26 288 | 85.81 287 | 87.62 300 | 86.97 317 | 75.00 319 | 96.55 286 | 96.32 298 | 86.08 294 | 81.32 309 | 92.98 301 | 73.10 308 | 92.05 320 | 71.64 316 | 87.32 277 | 95.81 276 |
|
1111 | | | 84.94 289 | 84.30 290 | 86.86 301 | 87.59 315 | 75.10 317 | 96.63 281 | 96.43 296 | 82.53 306 | 80.75 310 | 92.91 303 | 68.94 312 | 93.79 313 | 68.24 319 | 84.66 294 | 91.70 312 |
|
.test1245 | | | 73.05 298 | 76.31 296 | 63.27 319 | 87.59 315 | 75.10 317 | 96.63 281 | 96.43 296 | 82.53 306 | 80.75 310 | 92.91 303 | 68.94 312 | 93.79 313 | 68.24 319 | 12.72 331 | 20.91 329 |
|
new-patchmatchnet | | | 88.50 283 | 87.45 284 | 91.67 292 | 90.31 310 | 85.89 300 | 97.16 261 | 97.33 265 | 89.47 272 | 83.63 304 | 92.77 305 | 76.38 293 | 95.06 310 | 82.70 295 | 77.29 314 | 94.06 306 |
|
pmmvs3 | | | 86.67 287 | 84.86 289 | 92.11 291 | 88.16 314 | 87.19 298 | 96.63 281 | 94.75 313 | 79.88 313 | 87.22 282 | 92.75 306 | 66.56 316 | 95.20 309 | 81.24 299 | 76.56 316 | 93.96 307 |
|
Anonymous20231211 | | | 83.69 290 | 81.50 292 | 90.26 294 | 89.23 313 | 80.10 311 | 97.97 198 | 97.06 277 | 72.79 319 | 82.05 307 | 92.57 307 | 50.28 323 | 96.32 304 | 76.15 311 | 75.38 317 | 94.37 300 |
|
ambc | | | | | 89.49 296 | 86.66 318 | 75.78 316 | 92.66 317 | 96.72 288 | | 86.55 285 | 92.50 308 | 46.01 325 | 97.90 265 | 90.32 241 | 82.09 297 | 94.80 290 |
|
testing_2 | | | 90.61 272 | 88.50 279 | 96.95 154 | 90.08 311 | 95.57 134 | 97.69 227 | 98.06 212 | 93.02 178 | 76.55 313 | 92.48 309 | 61.18 320 | 98.44 221 | 95.45 121 | 91.98 225 | 96.84 209 |
|
test12356 | | | 83.47 291 | 83.37 291 | 83.78 307 | 84.43 320 | 70.09 324 | 95.12 301 | 95.60 306 | 82.98 304 | 78.89 312 | 92.43 310 | 64.99 317 | 91.41 322 | 70.36 317 | 85.55 293 | 89.82 314 |
|
PatchT | | | 93.06 241 | 91.97 243 | 96.35 206 | 96.69 217 | 92.67 232 | 94.48 311 | 97.08 274 | 86.62 289 | 97.08 100 | 92.23 311 | 87.94 187 | 97.90 265 | 78.89 305 | 96.69 155 | 98.49 153 |
|
RPMNet | | | 92.52 245 | 91.17 248 | 96.59 184 | 97.00 199 | 93.43 220 | 94.96 304 | 97.26 270 | 82.27 308 | 96.93 108 | 92.12 312 | 86.98 210 | 97.88 269 | 76.32 310 | 96.65 157 | 98.46 154 |
|
UnsupCasMVSNet_bld | | | 87.17 285 | 85.12 288 | 93.31 284 | 91.94 305 | 88.77 281 | 94.92 306 | 98.30 161 | 84.30 303 | 82.30 305 | 90.04 313 | 63.96 319 | 97.25 281 | 85.85 288 | 74.47 319 | 93.93 308 |
|
LCM-MVSNet | | | 78.70 293 | 76.24 297 | 86.08 303 | 77.26 329 | 71.99 322 | 94.34 312 | 96.72 288 | 61.62 323 | 76.53 314 | 89.33 314 | 33.91 332 | 92.78 318 | 81.85 297 | 74.60 318 | 93.46 309 |
|
PMMVS2 | | | 77.95 295 | 75.44 298 | 85.46 304 | 82.54 321 | 74.95 320 | 94.23 313 | 93.08 322 | 72.80 318 | 74.68 315 | 87.38 315 | 36.36 330 | 91.56 321 | 73.95 314 | 63.94 321 | 89.87 313 |
|
JIA-IIPM | | | 93.35 233 | 92.49 237 | 95.92 222 | 96.48 227 | 90.65 260 | 95.01 303 | 96.96 281 | 85.93 295 | 96.08 136 | 87.33 316 | 87.70 197 | 98.78 186 | 91.35 226 | 95.58 179 | 98.34 158 |
|
testmv | | | 78.74 292 | 77.35 293 | 82.89 309 | 78.16 328 | 69.30 325 | 95.87 295 | 94.65 314 | 81.11 310 | 70.98 320 | 87.11 317 | 46.31 324 | 90.42 323 | 65.28 322 | 76.72 315 | 88.95 315 |
|
PMVS | | 61.03 23 | 65.95 302 | 63.57 304 | 73.09 316 | 57.90 333 | 51.22 334 | 85.05 325 | 93.93 321 | 54.45 325 | 44.32 329 | 83.57 318 | 13.22 335 | 89.15 324 | 58.68 326 | 81.00 302 | 78.91 324 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVS-HIRNet | | | 89.46 278 | 88.40 280 | 92.64 287 | 97.58 163 | 82.15 308 | 94.16 314 | 93.05 323 | 75.73 317 | 90.90 259 | 82.52 319 | 79.42 280 | 98.33 239 | 83.53 294 | 98.68 102 | 97.43 177 |
|
gg-mvs-nofinetune | | | 92.21 248 | 90.58 262 | 97.13 143 | 96.75 214 | 95.09 152 | 95.85 296 | 89.40 327 | 85.43 298 | 94.50 164 | 81.98 320 | 80.80 272 | 98.40 237 | 92.16 205 | 98.33 120 | 97.88 168 |
|
PNet_i23d | | | 67.70 301 | 65.07 302 | 75.60 313 | 78.61 326 | 59.61 331 | 89.14 321 | 88.24 329 | 61.83 322 | 52.37 326 | 80.89 321 | 18.91 334 | 84.91 327 | 62.70 324 | 52.93 323 | 82.28 321 |
|
Gipuma | | | 78.40 294 | 76.75 295 | 83.38 308 | 95.54 280 | 80.43 310 | 79.42 327 | 97.40 258 | 64.67 321 | 73.46 316 | 80.82 322 | 45.65 326 | 93.14 317 | 66.32 321 | 87.43 275 | 76.56 325 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
no-one | | | 74.41 297 | 70.76 299 | 85.35 305 | 79.88 324 | 76.83 313 | 94.68 309 | 94.22 318 | 80.33 312 | 63.81 322 | 79.73 323 | 35.45 331 | 93.36 316 | 71.78 315 | 36.99 328 | 85.86 319 |
|
ANet_high | | | 69.08 299 | 65.37 301 | 80.22 310 | 65.99 332 | 71.96 323 | 90.91 320 | 90.09 326 | 82.62 305 | 49.93 328 | 78.39 324 | 29.36 333 | 81.75 328 | 62.49 325 | 38.52 327 | 86.95 318 |
|
E-PMN | | | 64.94 303 | 64.25 303 | 67.02 317 | 82.28 322 | 59.36 332 | 91.83 319 | 85.63 331 | 52.69 326 | 60.22 324 | 77.28 325 | 41.06 328 | 80.12 330 | 46.15 328 | 41.14 325 | 61.57 327 |
|
EMVS | | | 64.07 304 | 63.26 305 | 66.53 318 | 81.73 323 | 58.81 333 | 91.85 318 | 84.75 332 | 51.93 328 | 59.09 325 | 75.13 326 | 43.32 327 | 79.09 331 | 42.03 329 | 39.47 326 | 61.69 326 |
|
MVE | | 62.14 22 | 63.28 306 | 59.38 306 | 74.99 314 | 74.33 330 | 65.47 327 | 85.55 324 | 80.50 334 | 52.02 327 | 51.10 327 | 75.00 327 | 10.91 339 | 80.50 329 | 51.60 327 | 53.40 322 | 78.99 323 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuykxyi23d | | | 63.73 305 | 58.86 307 | 78.35 312 | 67.62 331 | 67.90 326 | 86.56 323 | 87.81 330 | 58.26 324 | 42.49 330 | 70.28 328 | 11.55 337 | 85.05 326 | 63.66 323 | 41.50 324 | 82.11 322 |
|
X-MVStestdata | | | 94.06 223 | 92.30 240 | 99.34 13 | 99.70 15 | 98.35 22 | 99.29 14 | 98.88 46 | 97.40 15 | 98.46 45 | 43.50 329 | 95.90 29 | 99.89 25 | 97.85 33 | 99.74 32 | 99.78 7 |
|
testmvs | | | 21.48 310 | 24.95 311 | 11.09 323 | 14.89 335 | 6.47 337 | 96.56 284 | 9.87 337 | 7.55 331 | 17.93 331 | 39.02 330 | 9.43 340 | 5.90 335 | 16.56 332 | 12.72 331 | 20.91 329 |
|
test123 | | | 20.95 311 | 23.72 312 | 12.64 322 | 13.54 336 | 8.19 336 | 96.55 286 | 6.13 338 | 7.48 332 | 16.74 332 | 37.98 331 | 12.97 336 | 6.05 334 | 16.69 331 | 5.43 333 | 23.68 328 |
|
test_post | | | | | | | | | | | | 31.83 332 | 88.83 157 | 98.91 170 | | | |
|
test_post1 | | | | | | | | 96.68 280 | | | | 30.43 333 | 87.85 192 | 98.69 188 | 92.59 197 | | |
|
wuyk23d | | | 30.17 308 | 30.18 310 | 30.16 321 | 78.61 326 | 43.29 335 | 66.79 328 | 14.21 336 | 17.31 330 | 14.82 333 | 11.93 334 | 11.55 337 | 41.43 333 | 37.08 330 | 19.30 330 | 5.76 331 |
|
pcd_1.5k_mvsjas | | | 7.88 313 | 10.50 314 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 94.51 60 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
pcd1.5k->3k | | | 39.42 307 | 41.78 308 | 32.35 320 | 96.17 255 | 0.00 338 | 0.00 329 | 98.54 121 | 0.00 333 | 0.00 334 | 0.00 335 | 87.78 194 | 0.00 336 | 0.00 333 | 93.56 205 | 97.06 187 |
|
sosnet-low-res | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
sosnet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
uncertanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
Regformer | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
uanet | | | 0.00 314 | 0.00 315 | 0.00 324 | 0.00 337 | 0.00 338 | 0.00 329 | 0.00 339 | 0.00 333 | 0.00 334 | 0.00 335 | 0.00 341 | 0.00 336 | 0.00 333 | 0.00 334 | 0.00 332 |
|
sam_mvs1 | | | | | | | | | | | | | 89.45 134 | | | | |
|
sam_mvs | | | | | | | | | | | | | 88.99 145 | | | | |
|
MTGPA | | | | | | | | | 98.74 75 | | | | | | | | |
|
MTMP | | | | | | | | | 94.14 319 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 96.39 93 | 99.57 54 | 99.69 35 |
|
agg_prior2 | | | | | | | | | | | | | | | 95.87 105 | 99.57 54 | 99.68 41 |
|
agg_prior | | | | | | 99.30 52 | 98.38 17 | | 98.72 81 | | 97.57 92 | | | 99.81 50 | | | |
|
test_prior4 | | | | | | | 98.01 41 | 97.86 214 | | | | | | | | | |
|
test_prior | | | | | 99.19 28 | 99.31 47 | 98.22 30 | | 98.84 53 | | | | | 99.70 91 | | | 99.65 50 |
|
旧先验2 | | | | | | | | 97.57 236 | | 91.30 237 | 98.67 36 | | | 99.80 57 | 95.70 114 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 97.64 231 | | | | | | | | | |
|
æ— å…ˆéªŒ | | | | | | | | 97.58 235 | 98.72 81 | 91.38 231 | | | | 99.87 34 | 93.36 172 | | 99.60 59 |
|
原ACMM2 | | | | | | | | 97.67 229 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.89 25 | 91.65 221 | | |
|
segment_acmp | | | | | | | | | | | | | 96.85 3 | | | | |
|
testdata1 | | | | | | | | 97.32 253 | | 96.34 57 | | | | | | | |
|
test12 | | | | | 99.18 32 | 99.16 76 | 98.19 32 | | 98.53 124 | | 98.07 59 | | 95.13 49 | 99.72 86 | | 99.56 60 | 99.63 55 |
|
plane_prior7 | | | | | | 97.42 175 | 94.63 183 | | | | | | | | | | |
|
plane_prior6 | | | | | | 97.35 180 | 94.61 186 | | | | | | 87.09 207 | | | | |
|
plane_prior5 | | | | | | | | | 98.56 118 | | | | | 99.03 156 | 96.07 96 | 94.27 185 | 96.92 196 |
|
plane_prior3 | | | | | | | 94.61 186 | | | 97.02 39 | 95.34 145 | | | | | | |
|
plane_prior2 | | | | | | | | 98.80 85 | | 97.28 22 | | | | | | | |
|
plane_prior1 | | | | | | 97.37 179 | | | | | | | | | | | |
|
plane_prior | | | | | | | 94.60 188 | 98.44 146 | | 96.74 46 | | | | | | 94.22 187 | |
|
n2 | | | | | | | | | 0.00 339 | | | | | | | | |
|
nn | | | | | | | | | 0.00 339 | | | | | | | | |
|
door-mid | | | | | | | | | 94.37 316 | | | | | | | | |
|
test11 | | | | | | | | | 98.66 103 | | | | | | | | |
|
door | | | | | | | | | 94.64 315 | | | | | | | | |
|
HQP5-MVS | | | | | | | 94.25 201 | | | | | | | | | | |
|
HQP-NCC | | | | | | 97.20 189 | | 98.05 190 | | 96.43 54 | 94.45 166 | | | | | | |
|
ACMP_Plane | | | | | | 97.20 189 | | 98.05 190 | | 96.43 54 | 94.45 166 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 95.30 124 | | |
|
HQP4-MVS | | | | | | | | | | | 94.45 166 | | | 98.96 163 | | | 96.87 206 |
|
HQP3-MVS | | | | | | | | | 98.46 138 | | | | | | | 94.18 189 | |
|
HQP2-MVS | | | | | | | | | | | | | 86.75 212 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 84.26 302 | 96.89 273 | | 90.97 244 | 97.90 73 | | 89.89 132 | | 93.91 159 | | 99.18 107 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 92.97 216 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 93.61 204 | |
|
Test By Simon | | | | | | | | | | | | | 94.64 57 | | | | |
|