LTVRE_ROB | | 75.46 1 | 84.22 5 | 84.98 5 | 81.94 19 | 84.82 63 | 75.40 26 | 91.60 1 | 87.80 5 | 73.52 18 | 88.90 13 | 93.06 6 | 71.39 59 | 81.53 93 | 81.53 3 | 92.15 69 | 88.91 48 |
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 |
TDRefinement | | | 86.32 2 | 86.33 2 | 86.29 1 | 88.64 30 | 81.19 6 | 88.84 2 | 90.72 1 | 78.27 7 | 87.95 17 | 92.53 13 | 79.37 11 | 84.79 47 | 74.51 37 | 96.15 4 | 92.88 9 |
|
CP-MVS | | | 84.12 7 | 84.55 8 | 82.80 9 | 89.42 18 | 79.74 7 | 88.19 3 | 84.43 40 | 71.96 28 | 84.70 60 | 90.56 52 | 77.12 17 | 86.18 21 | 79.24 17 | 95.36 14 | 82.49 153 |
|
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 84.22 5 | 84.84 6 | 82.35 17 | 89.23 22 | 76.66 22 | 87.65 4 | 85.89 18 | 71.03 31 | 85.85 44 | 90.58 51 | 78.77 13 | 85.78 30 | 79.37 15 | 95.17 18 | 84.62 104 |
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 |
abl_6 | | | 84.92 3 | 85.70 3 | 82.57 14 | 86.72 41 | 79.27 8 | 87.56 5 | 86.08 16 | 77.48 9 | 88.12 16 | 91.53 30 | 81.18 6 | 84.31 55 | 78.12 23 | 94.47 35 | 84.15 118 |
|
LS3D | | | 80.99 38 | 80.85 44 | 81.41 24 | 78.37 138 | 71.37 47 | 87.45 6 | 85.87 19 | 77.48 9 | 81.98 85 | 89.95 74 | 69.14 72 | 85.26 37 | 66.15 108 | 91.24 83 | 87.61 67 |
|
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 72.78 3 | 83.75 10 | 84.11 13 | 82.68 11 | 82.97 88 | 74.39 32 | 87.18 7 | 88.18 4 | 78.98 5 | 86.11 41 | 91.47 32 | 79.70 10 | 85.76 31 | 66.91 101 | 95.46 13 | 87.89 64 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
HSP-MVS | | | 79.69 47 | 79.17 56 | 81.27 29 | 89.70 12 | 77.46 19 | 87.16 8 | 80.58 114 | 64.94 74 | 81.05 102 | 88.38 104 | 57.10 201 | 87.10 7 | 79.75 7 | 83.87 203 | 79.24 209 |
|
mPP-MVS | | | 84.01 9 | 84.39 9 | 82.88 5 | 90.65 4 | 81.38 5 | 87.08 9 | 82.79 68 | 72.41 24 | 85.11 55 | 90.85 44 | 76.65 20 | 84.89 44 | 79.30 16 | 94.63 32 | 82.35 155 |
|
region2R | | | 83.54 13 | 83.86 17 | 82.58 13 | 89.82 10 | 77.53 16 | 87.06 10 | 84.23 46 | 70.19 37 | 83.86 70 | 90.72 49 | 75.20 30 | 86.27 18 | 79.41 14 | 94.25 43 | 83.95 122 |
|
HFP-MVS | | | 83.39 16 | 84.03 14 | 81.48 22 | 89.25 20 | 75.69 24 | 87.01 11 | 84.27 43 | 70.23 35 | 84.47 64 | 90.43 57 | 76.79 18 | 85.94 27 | 79.58 10 | 94.23 44 | 82.82 144 |
|
ACMMPR | | | 83.62 11 | 83.93 15 | 82.69 10 | 89.78 11 | 77.51 18 | 87.01 11 | 84.19 47 | 70.23 35 | 84.49 63 | 90.67 50 | 75.15 31 | 86.37 15 | 79.58 10 | 94.26 42 | 84.18 117 |
|
XVS | | | 83.51 14 | 83.73 18 | 82.85 7 | 89.43 16 | 77.61 14 | 86.80 13 | 84.66 36 | 72.71 22 | 82.87 78 | 90.39 62 | 73.86 41 | 86.31 16 | 78.84 19 | 94.03 46 | 84.64 102 |
|
X-MVStestdata | | | 76.81 73 | 74.79 99 | 82.85 7 | 89.43 16 | 77.61 14 | 86.80 13 | 84.66 36 | 72.71 22 | 82.87 78 | 9.95 365 | 73.86 41 | 86.31 16 | 78.84 19 | 94.03 46 | 84.64 102 |
|
TSAR-MVS + MP. | | | 79.05 53 | 78.81 57 | 79.74 43 | 88.94 26 | 67.52 77 | 86.61 15 | 81.38 93 | 51.71 217 | 77.15 145 | 91.42 34 | 65.49 105 | 87.20 5 | 79.44 13 | 87.17 156 | 84.51 110 |
|
APDe-MVS | | | 82.88 22 | 84.14 12 | 79.08 51 | 84.80 65 | 66.72 80 | 86.54 16 | 85.11 27 | 72.00 27 | 86.65 33 | 91.75 24 | 78.20 16 | 87.04 8 | 77.93 24 | 94.32 41 | 83.47 131 |
|
CPTT-MVS | | | 81.51 33 | 81.76 38 | 80.76 35 | 89.20 23 | 78.75 10 | 86.48 17 | 82.03 78 | 68.80 42 | 80.92 105 | 88.52 100 | 72.00 54 | 82.39 80 | 74.80 33 | 93.04 57 | 81.14 177 |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 83.19 17 | 83.54 21 | 82.14 18 | 90.54 5 | 79.00 9 | 86.42 18 | 83.59 57 | 71.31 29 | 81.26 97 | 90.96 41 | 74.57 37 | 84.69 48 | 78.41 21 | 94.78 27 | 82.74 147 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
HPM-MVS_fast | | | 84.59 4 | 85.10 4 | 83.06 4 | 88.60 31 | 75.83 23 | 86.27 19 | 86.89 11 | 73.69 17 | 86.17 39 | 91.70 25 | 78.23 15 | 85.20 40 | 79.45 12 | 94.91 26 | 88.15 62 |
|
GST-MVS | | | 82.79 23 | 83.27 26 | 81.34 26 | 88.99 25 | 73.29 41 | 85.94 20 | 85.13 26 | 68.58 46 | 84.14 68 | 90.21 69 | 73.37 44 | 86.41 13 | 79.09 18 | 93.98 49 | 84.30 116 |
|
SteuartSystems-ACMMP | | | 83.07 19 | 83.64 19 | 81.35 25 | 85.14 59 | 71.00 51 | 85.53 21 | 84.78 33 | 70.91 32 | 85.64 45 | 90.41 61 | 75.55 28 | 87.69 3 | 79.75 7 | 95.08 21 | 85.36 91 |
Skip Steuart: Steuart Systems R&D Blog. |
APD-MVS_3200maxsize | | | 83.57 12 | 84.33 10 | 81.31 27 | 82.83 90 | 73.53 40 | 85.50 22 | 87.45 8 | 74.11 15 | 86.45 35 | 90.52 55 | 80.02 9 | 84.48 51 | 77.73 25 | 94.34 40 | 85.93 84 |
|
HPM-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 84.12 7 | 84.63 7 | 82.60 12 | 88.21 34 | 74.40 31 | 85.24 23 | 87.21 9 | 70.69 34 | 85.14 53 | 90.42 60 | 78.99 12 | 86.62 11 | 80.83 6 | 94.93 25 | 86.79 75 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
SMA-MVS | | | 82.12 28 | 82.68 34 | 80.43 37 | 88.90 28 | 69.52 62 | 85.12 24 | 84.76 34 | 63.53 91 | 84.23 67 | 91.47 32 | 72.02 53 | 87.16 6 | 79.74 9 | 94.36 38 | 84.61 105 |
|
MTAPA | | | 83.19 17 | 83.87 16 | 81.13 30 | 91.16 2 | 78.16 12 | 84.87 25 | 80.63 111 | 72.08 25 | 84.93 56 | 90.79 45 | 74.65 35 | 84.42 52 | 80.98 4 | 94.75 28 | 80.82 184 |
|
MTMP | | | | | | | | 84.83 26 | 19.26 371 | | | | | | | | |
|
LCM-MVSNet | | | 86.90 1 | 88.67 1 | 81.57 20 | 91.50 1 | 63.30 106 | 84.80 27 | 87.77 7 | 86.18 1 | 96.26 2 | 96.06 1 | 90.32 1 | 84.49 50 | 68.08 84 | 97.05 3 | 96.93 1 |
|
UA-Net | | | 81.56 32 | 82.28 36 | 79.40 48 | 88.91 27 | 69.16 68 | 84.67 28 | 80.01 127 | 75.34 12 | 79.80 117 | 94.91 2 | 69.79 69 | 80.25 137 | 72.63 46 | 94.46 36 | 88.78 52 |
|
#test# | | | 82.40 26 | 82.71 33 | 81.48 22 | 89.25 20 | 75.69 24 | 84.47 29 | 84.27 43 | 64.45 78 | 84.47 64 | 90.43 57 | 76.79 18 | 85.94 27 | 76.01 32 | 94.23 44 | 82.82 144 |
|
3Dnovator+ | | 73.19 2 | 81.08 36 | 80.48 46 | 82.87 6 | 81.41 107 | 72.03 43 | 84.38 30 | 86.23 15 | 77.28 11 | 80.65 108 | 90.18 70 | 59.80 156 | 87.58 4 | 73.06 44 | 91.34 81 | 89.01 43 |
|
MVSFormer | | | 69.93 169 | 69.03 181 | 72.63 146 | 74.93 179 | 59.19 131 | 83.98 31 | 75.72 182 | 52.27 210 | 63.53 285 | 76.74 259 | 43.19 259 | 80.56 130 | 72.28 52 | 78.67 269 | 78.14 221 |
|
test_djsdf | | | 78.88 57 | 78.27 63 | 80.70 36 | 81.42 106 | 71.24 49 | 83.98 31 | 75.72 182 | 52.27 210 | 87.37 25 | 92.25 16 | 68.04 84 | 80.56 130 | 72.28 52 | 91.15 85 | 90.32 32 |
|
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 81.13 35 | 81.73 39 | 79.36 49 | 84.47 71 | 70.53 56 | 83.85 33 | 83.70 54 | 69.43 41 | 83.67 72 | 88.96 95 | 75.89 26 | 86.41 13 | 72.62 47 | 92.95 58 | 81.14 177 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ACMMP_Plus | | | 82.33 27 | 83.28 25 | 79.46 47 | 89.28 19 | 69.09 70 | 83.62 34 | 84.98 28 | 64.77 75 | 83.97 69 | 91.02 39 | 75.53 29 | 85.93 29 | 82.00 2 | 94.36 38 | 83.35 137 |
|
HPM-MVS++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 79.89 46 | 79.80 51 | 80.18 40 | 89.02 24 | 78.44 11 | 83.49 35 | 80.18 124 | 64.71 77 | 78.11 138 | 88.39 103 | 65.46 106 | 83.14 70 | 77.64 27 | 91.20 84 | 78.94 212 |
|
SD-MVS | | | 80.28 44 | 81.55 42 | 76.47 76 | 83.57 80 | 67.83 76 | 83.39 36 | 85.35 25 | 64.42 81 | 86.14 40 | 87.07 118 | 74.02 40 | 80.97 119 | 77.70 26 | 92.32 68 | 80.62 189 |
|
ACMM | | 69.25 9 | 82.11 29 | 83.31 24 | 78.49 59 | 88.17 35 | 73.96 34 | 83.11 37 | 84.52 39 | 66.40 58 | 87.45 23 | 89.16 87 | 81.02 7 | 80.52 133 | 74.27 39 | 95.73 9 | 80.98 181 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ESAPD | | | 82.00 30 | 83.02 29 | 78.95 54 | 85.36 56 | 67.25 79 | 82.91 38 | 84.98 28 | 73.52 18 | 85.43 51 | 90.03 72 | 76.37 21 | 86.97 10 | 74.56 36 | 94.02 48 | 82.62 149 |
|
HQP_MVS | | | 78.77 58 | 78.78 59 | 78.72 56 | 85.18 57 | 65.18 91 | 82.74 39 | 85.49 21 | 65.45 64 | 78.23 136 | 89.11 89 | 60.83 147 | 86.15 22 | 71.09 56 | 90.94 90 | 84.82 99 |
|
plane_prior2 | | | | | | | | 82.74 39 | | 65.45 64 | | | | | | | |
|
zzz-MVS | | | 83.01 21 | 83.63 20 | 81.13 30 | 91.16 2 | 78.16 12 | 82.72 41 | 80.63 111 | 72.08 25 | 84.93 56 | 90.79 45 | 74.65 35 | 84.42 52 | 80.98 4 | 94.75 28 | 80.82 184 |
|
ACMP | | 69.50 8 | 82.64 24 | 83.38 23 | 80.40 38 | 86.50 43 | 69.44 64 | 82.30 42 | 86.08 16 | 66.80 54 | 86.70 32 | 89.99 73 | 81.64 5 | 85.95 26 | 74.35 38 | 96.11 5 | 85.81 86 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 70.70 6 | 81.70 31 | 83.15 28 | 77.36 71 | 90.35 6 | 82.82 3 | 82.15 43 | 79.22 135 | 74.08 16 | 87.16 27 | 91.97 19 | 84.80 2 | 76.97 186 | 64.98 118 | 93.61 51 | 72.28 265 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
DeepC-MVS | | 72.44 4 | 81.00 37 | 80.83 45 | 81.50 21 | 86.70 42 | 70.03 61 | 82.06 44 | 87.00 10 | 59.89 124 | 80.91 106 | 90.53 53 | 72.19 49 | 88.56 1 | 73.67 42 | 94.52 34 | 85.92 85 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PGM-MVS | | | 83.07 19 | 83.25 27 | 82.54 15 | 89.57 14 | 77.21 20 | 82.04 45 | 85.40 23 | 67.96 48 | 84.91 58 | 90.88 42 | 75.59 27 | 86.57 12 | 78.16 22 | 94.71 30 | 83.82 123 |
|
LPG-MVS_test | | | 83.47 15 | 84.33 10 | 80.90 33 | 87.00 38 | 70.41 57 | 82.04 45 | 86.35 12 | 69.77 39 | 87.75 18 | 91.13 36 | 81.83 3 | 86.20 19 | 77.13 28 | 95.96 7 | 86.08 80 |
|
F-COLMAP | | | 75.29 92 | 73.99 110 | 79.18 50 | 81.73 103 | 71.90 44 | 81.86 47 | 82.98 65 | 59.86 125 | 72.27 211 | 84.00 181 | 64.56 114 | 83.07 72 | 51.48 202 | 87.19 155 | 82.56 152 |
|
MP-MVS-pluss | | | 82.54 25 | 83.46 22 | 79.76 42 | 88.88 29 | 68.44 72 | 81.57 48 | 86.33 14 | 63.17 96 | 85.38 52 | 91.26 35 | 76.33 22 | 84.67 49 | 83.30 1 | 94.96 24 | 86.17 79 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
PAPM_NR | | | 73.91 112 | 74.16 108 | 73.16 129 | 81.90 101 | 53.50 163 | 81.28 49 | 81.40 92 | 66.17 59 | 73.30 197 | 83.31 190 | 59.96 152 | 83.10 71 | 58.45 155 | 81.66 231 | 82.87 142 |
|
API-MVS | | | 70.97 159 | 71.51 160 | 69.37 182 | 75.20 176 | 55.94 147 | 80.99 50 | 76.84 173 | 62.48 102 | 71.24 223 | 77.51 254 | 61.51 137 | 80.96 123 | 52.04 198 | 85.76 170 | 71.22 274 |
|
OMC-MVS | | | 79.41 51 | 78.79 58 | 81.28 28 | 80.62 112 | 70.71 55 | 80.91 51 | 84.76 34 | 62.54 101 | 81.77 87 | 86.65 135 | 71.46 57 | 83.53 64 | 67.95 91 | 92.44 65 | 89.60 34 |
|
casdiffmvs1 | | | 72.89 135 | 72.85 136 | 73.04 132 | 77.69 149 | 53.36 165 | 80.89 52 | 80.76 109 | 44.66 279 | 72.86 200 | 88.56 99 | 66.45 97 | 80.91 124 | 61.58 135 | 82.17 218 | 84.84 98 |
|
mvs_tets | | | 78.93 56 | 78.67 60 | 79.72 44 | 84.81 64 | 73.93 35 | 80.65 53 | 76.50 176 | 51.98 215 | 87.40 24 | 91.86 21 | 76.09 25 | 78.53 162 | 68.58 79 | 90.20 105 | 86.69 77 |
|
ACMH+ | | 66.64 10 | 81.20 34 | 82.48 35 | 77.35 72 | 81.16 110 | 62.39 110 | 80.51 54 | 87.80 5 | 73.02 21 | 87.57 21 | 91.08 38 | 80.28 8 | 82.44 79 | 64.82 119 | 96.10 6 | 87.21 72 |
|
EPP-MVSNet | | | 73.86 113 | 73.38 121 | 75.31 91 | 78.19 140 | 53.35 166 | 80.45 55 | 77.32 170 | 65.11 72 | 76.47 161 | 86.80 124 | 49.47 232 | 83.77 59 | 53.89 192 | 92.72 63 | 88.81 51 |
|
jajsoiax | | | 78.51 61 | 78.16 64 | 79.59 46 | 84.65 67 | 73.83 37 | 80.42 56 | 76.12 178 | 51.33 222 | 87.19 26 | 91.51 31 | 73.79 43 | 78.44 166 | 68.27 82 | 90.13 109 | 86.49 78 |
|
PHI-MVS | | | 74.92 99 | 74.36 106 | 76.61 73 | 76.40 163 | 62.32 111 | 80.38 57 | 83.15 63 | 54.16 192 | 73.23 198 | 80.75 219 | 62.19 130 | 83.86 58 | 68.02 86 | 90.92 93 | 83.65 127 |
|
QAPM | | | 69.18 180 | 69.26 177 | 68.94 191 | 71.61 250 | 52.58 169 | 80.37 58 | 78.79 145 | 49.63 241 | 73.51 193 | 85.14 164 | 53.66 215 | 79.12 150 | 55.11 182 | 75.54 287 | 75.11 244 |
|
OurMVSNet-221017-0 | | | 78.57 60 | 78.53 62 | 78.67 57 | 80.48 113 | 64.16 98 | 80.24 59 | 82.06 77 | 61.89 105 | 88.77 14 | 93.32 4 | 57.15 199 | 82.60 78 | 70.08 68 | 92.80 59 | 89.25 38 |
|
XVG-ACMP-BASELINE | | | 80.54 40 | 81.06 43 | 78.98 53 | 87.01 37 | 72.91 42 | 80.23 60 | 85.56 20 | 66.56 57 | 85.64 45 | 89.57 78 | 69.12 73 | 80.55 132 | 72.51 48 | 93.37 53 | 83.48 130 |
|
anonymousdsp | | | 78.60 59 | 77.80 67 | 81.00 32 | 78.01 143 | 74.34 33 | 80.09 61 | 76.12 178 | 50.51 235 | 89.19 12 | 90.88 42 | 71.45 58 | 77.78 181 | 73.38 43 | 90.60 100 | 90.90 26 |
|
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 69.55 173 | 72.83 137 | 59.70 278 | 63.63 314 | 53.97 160 | 80.08 62 | 75.93 180 | 64.24 83 | 73.49 194 | 88.93 96 | 57.89 192 | 62.46 302 | 59.75 148 | 91.55 77 | 62.67 330 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
plane_prior | | | | | | | 65.18 91 | 80.06 63 | | 61.88 106 | | | | | | 89.91 114 | |
|
DeepC-MVS_fast | | 69.89 7 | 77.17 71 | 76.33 83 | 79.70 45 | 83.90 79 | 67.94 74 | 80.06 63 | 83.75 53 | 56.73 156 | 74.88 177 | 85.32 161 | 65.54 104 | 87.79 2 | 65.61 114 | 91.14 86 | 83.35 137 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
NCCC | | | 78.25 65 | 78.04 65 | 78.89 55 | 85.61 53 | 69.45 63 | 79.80 65 | 80.99 107 | 65.77 61 | 75.55 169 | 86.25 148 | 67.42 88 | 85.42 33 | 70.10 67 | 90.88 96 | 81.81 167 |
|
casdiffmvs | | | 72.24 148 | 71.83 152 | 73.47 119 | 75.01 178 | 54.46 157 | 79.73 66 | 82.60 72 | 45.66 267 | 70.90 228 | 87.73 113 | 63.41 118 | 82.32 83 | 65.09 117 | 76.36 281 | 83.64 128 |
|
IS-MVSNet | | | 75.10 95 | 75.42 95 | 74.15 103 | 79.23 126 | 48.05 205 | 79.43 67 | 78.04 161 | 70.09 38 | 79.17 124 | 88.02 111 | 53.04 216 | 83.60 62 | 58.05 157 | 93.76 50 | 90.79 28 |
|
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 74.22 110 | 74.56 101 | 73.20 128 | 81.95 100 | 60.97 119 | 79.43 67 | 80.90 108 | 65.57 63 | 72.54 208 | 81.76 212 | 70.98 62 | 85.26 37 | 47.88 232 | 90.00 111 | 73.37 253 |
|
OPM-MVS | | | 80.99 38 | 81.63 41 | 79.07 52 | 86.86 40 | 69.39 65 | 79.41 69 | 84.00 52 | 65.64 62 | 85.54 49 | 89.28 81 | 76.32 23 | 83.47 65 | 74.03 40 | 93.57 52 | 84.35 115 |
|
v7n | | | 79.37 52 | 80.41 47 | 76.28 80 | 78.67 137 | 55.81 148 | 79.22 70 | 82.51 74 | 70.72 33 | 87.54 22 | 92.44 14 | 68.00 85 | 81.34 103 | 72.84 45 | 91.72 71 | 91.69 12 |
|
DP-MVS | | | 78.44 64 | 79.29 55 | 75.90 85 | 81.86 102 | 65.33 89 | 79.05 71 | 84.63 38 | 74.83 14 | 80.41 111 | 86.27 146 | 71.68 55 | 83.45 66 | 62.45 133 | 92.40 66 | 78.92 213 |
|
ACMH | | 63.62 14 | 77.50 69 | 80.11 48 | 69.68 181 | 79.61 119 | 56.28 146 | 78.81 72 | 83.62 56 | 63.41 94 | 87.14 28 | 90.23 68 | 76.11 24 | 73.32 221 | 67.58 93 | 94.44 37 | 79.44 207 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
MSLP-MVS++ | | | 74.48 108 | 75.78 89 | 70.59 168 | 84.66 66 | 62.40 109 | 78.65 73 | 84.24 45 | 60.55 120 | 77.71 141 | 81.98 208 | 63.12 120 | 77.64 182 | 62.95 130 | 88.14 135 | 71.73 270 |
|
AllTest | | | 77.66 67 | 77.43 69 | 78.35 61 | 79.19 128 | 70.81 52 | 78.60 74 | 88.64 2 | 65.37 67 | 80.09 115 | 88.17 107 | 70.33 64 | 78.43 167 | 55.60 177 | 90.90 94 | 85.81 86 |
|
PS-MVSNAJss | | | 77.54 68 | 77.35 70 | 78.13 65 | 84.88 62 | 66.37 83 | 78.55 75 | 79.59 132 | 53.48 201 | 86.29 38 | 92.43 15 | 62.39 127 | 80.25 137 | 67.90 92 | 90.61 99 | 87.77 65 |
|
v52 | | | 78.96 54 | 79.79 52 | 76.46 77 | 73.03 234 | 54.90 151 | 78.48 76 | 83.48 58 | 64.43 79 | 91.19 4 | 91.54 28 | 72.08 50 | 81.11 112 | 76.45 30 | 87.47 145 | 93.38 7 |
|
V4 | | | 78.96 54 | 79.79 52 | 76.46 77 | 73.02 235 | 54.90 151 | 78.48 76 | 83.47 59 | 64.43 79 | 91.20 3 | 91.54 28 | 72.08 50 | 81.11 112 | 76.45 30 | 87.46 147 | 93.38 7 |
|
Effi-MVS+-dtu | | | 75.43 88 | 72.28 147 | 84.91 2 | 77.05 154 | 83.58 2 | 78.47 78 | 77.70 164 | 57.68 140 | 74.89 176 | 78.13 251 | 64.80 111 | 84.26 56 | 56.46 170 | 85.32 184 | 86.88 74 |
|
3Dnovator | | 65.95 11 | 71.50 155 | 71.22 163 | 72.34 153 | 73.16 224 | 63.09 107 | 78.37 79 | 78.32 153 | 57.67 142 | 72.22 213 | 84.61 173 | 54.77 210 | 78.47 164 | 60.82 141 | 81.07 243 | 75.45 240 |
|
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 62.51 15 | 68.76 188 | 68.75 188 | 68.78 198 | 70.56 258 | 53.91 161 | 78.29 80 | 77.35 169 | 48.85 245 | 70.22 237 | 83.52 185 | 52.65 219 | 76.93 187 | 55.31 181 | 81.99 222 | 75.49 239 |
|
WR-MVS_H | | | 80.22 45 | 82.17 37 | 74.39 99 | 89.46 15 | 42.69 253 | 78.24 81 | 82.24 75 | 78.21 8 | 89.57 11 | 92.10 18 | 68.05 83 | 85.59 32 | 66.04 110 | 95.62 11 | 94.88 5 |
|
114514_t | | | 73.40 121 | 73.33 124 | 73.64 113 | 84.15 78 | 57.11 143 | 78.20 82 | 80.02 126 | 43.76 284 | 72.55 207 | 86.07 155 | 64.00 116 | 83.35 69 | 60.14 144 | 91.03 89 | 80.45 192 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 62.01 16 | 71.79 153 | 70.28 170 | 76.33 79 | 80.31 115 | 68.63 71 | 78.18 83 | 81.24 96 | 54.57 186 | 67.09 266 | 80.63 221 | 59.44 159 | 81.74 91 | 46.91 239 | 84.17 198 | 78.63 214 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CNVR-MVS | | | 78.49 62 | 78.59 61 | 78.16 63 | 85.86 51 | 67.40 78 | 78.12 84 | 81.50 86 | 63.92 85 | 77.51 143 | 86.56 139 | 68.43 80 | 84.82 46 | 73.83 41 | 91.61 74 | 82.26 158 |
|
TAPA-MVS | | 65.27 12 | 75.16 94 | 74.29 107 | 77.77 68 | 74.86 182 | 68.08 73 | 77.89 85 | 84.04 51 | 55.15 172 | 76.19 164 | 83.39 186 | 66.91 91 | 80.11 142 | 60.04 145 | 90.14 108 | 85.13 94 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
test_prior4 | | | | | | | 70.14 59 | 77.57 86 | | | | | | | | | |
|
EPNet | | | 69.10 181 | 67.32 201 | 74.46 96 | 68.33 281 | 61.27 118 | 77.56 87 | 63.57 256 | 60.95 114 | 56.62 321 | 82.75 197 | 51.53 226 | 81.24 109 | 54.36 191 | 90.20 105 | 80.88 183 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
RPSCF | | | 75.76 83 | 74.37 105 | 79.93 41 | 74.81 183 | 77.53 16 | 77.53 88 | 79.30 134 | 59.44 126 | 78.88 126 | 89.80 76 | 71.26 60 | 73.09 223 | 57.45 160 | 80.89 246 | 89.17 41 |
|
CSCG | | | 74.12 111 | 74.39 104 | 73.33 124 | 79.35 123 | 61.66 116 | 77.45 89 | 81.98 79 | 62.47 103 | 79.06 125 | 80.19 226 | 61.83 132 | 78.79 157 | 59.83 147 | 87.35 150 | 79.54 206 |
|
HQP-NCC | | | | | | 82.37 93 | | 77.32 90 | | 59.08 127 | 71.58 216 | | | | | | |
|
ACMP_Plane | | | | | | 82.37 93 | | 77.32 90 | | 59.08 127 | 71.58 216 | | | | | | |
|
HQP-MVS | | | 75.24 93 | 75.01 98 | 75.94 84 | 82.37 93 | 58.80 136 | 77.32 90 | 84.12 48 | 59.08 127 | 71.58 216 | 85.96 157 | 58.09 181 | 85.30 36 | 67.38 97 | 89.16 121 | 83.73 126 |
|
DTE-MVSNet | | | 80.35 43 | 82.89 31 | 72.74 142 | 89.84 8 | 37.34 295 | 77.16 93 | 81.81 82 | 80.45 2 | 90.92 5 | 92.95 7 | 74.57 37 | 86.12 25 | 63.65 127 | 94.68 31 | 94.76 6 |
|
PS-CasMVS | | | 80.41 42 | 82.86 32 | 73.07 131 | 89.93 7 | 39.21 277 | 77.15 94 | 81.28 94 | 79.74 4 | 90.87 6 | 92.73 11 | 75.03 33 | 84.93 43 | 63.83 126 | 95.19 17 | 95.07 3 |
|
XVG-OURS-SEG-HR | | | 79.62 48 | 79.99 49 | 78.49 59 | 86.46 44 | 74.79 30 | 77.15 94 | 85.39 24 | 66.73 55 | 80.39 112 | 88.85 97 | 74.43 39 | 78.33 173 | 74.73 35 | 85.79 169 | 82.35 155 |
|
PEN-MVS | | | 80.46 41 | 82.91 30 | 73.11 130 | 89.83 9 | 39.02 280 | 77.06 96 | 82.61 71 | 80.04 3 | 90.60 8 | 92.85 9 | 74.93 34 | 85.21 39 | 63.15 129 | 95.15 19 | 95.09 2 |
|
v13 | | | 76.23 77 | 77.02 75 | 73.86 108 | 74.61 192 | 48.80 190 | 76.91 97 | 81.10 101 | 62.66 99 | 87.02 29 | 91.01 40 | 59.76 157 | 81.41 98 | 71.29 55 | 88.78 127 | 91.38 13 |
|
CP-MVSNet | | | 79.48 50 | 81.65 40 | 72.98 135 | 89.66 13 | 39.06 279 | 76.76 98 | 80.46 116 | 78.91 6 | 90.32 9 | 91.70 25 | 68.49 78 | 84.89 44 | 63.40 128 | 95.12 20 | 95.01 4 |
|
v12 | | | 76.03 79 | 76.79 76 | 73.76 110 | 74.45 194 | 48.60 196 | 76.59 99 | 81.11 98 | 62.22 104 | 86.79 31 | 90.74 48 | 59.51 158 | 81.40 100 | 71.01 58 | 88.67 129 | 91.29 15 |
|
agg_prior3 | | | 76.32 76 | 76.33 83 | 76.28 80 | 85.86 51 | 70.13 60 | 76.50 100 | 78.26 156 | 53.41 203 | 75.78 165 | 86.49 141 | 66.58 96 | 81.57 92 | 72.50 49 | 91.56 75 | 77.15 229 |
|
SixPastTwentyTwo | | | 75.77 82 | 76.34 82 | 74.06 104 | 81.69 104 | 54.84 153 | 76.47 101 | 75.49 184 | 64.10 84 | 87.73 20 | 92.24 17 | 50.45 230 | 81.30 105 | 67.41 95 | 91.46 78 | 86.04 82 |
|
TEST9 | | | | | | 85.47 54 | 69.32 66 | 76.42 102 | 78.69 146 | 53.73 199 | 76.97 146 | 86.74 129 | 66.84 92 | 81.10 114 | | | |
|
train_agg | | | 76.38 75 | 76.55 78 | 75.86 86 | 85.47 54 | 69.32 66 | 76.42 102 | 78.69 146 | 54.00 194 | 76.97 146 | 86.74 129 | 66.60 94 | 81.10 114 | 72.50 49 | 91.56 75 | 77.15 229 |
|
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 74.85 104 | 74.56 101 | 75.72 87 | 81.63 105 | 64.64 95 | 76.35 104 | 79.06 140 | 62.85 98 | 73.33 196 | 88.41 102 | 62.54 125 | 79.59 148 | 63.94 125 | 82.92 212 | 82.94 141 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
DeepPCF-MVS | | 71.07 5 | 78.48 63 | 77.14 73 | 82.52 16 | 84.39 75 | 77.04 21 | 76.35 104 | 84.05 50 | 56.66 157 | 80.27 113 | 85.31 162 | 68.56 77 | 87.03 9 | 67.39 96 | 91.26 82 | 83.50 129 |
|
XVG-OURS | | | 79.51 49 | 79.82 50 | 78.58 58 | 86.11 46 | 74.96 29 | 76.33 106 | 84.95 30 | 66.89 51 | 82.75 80 | 88.99 93 | 66.82 93 | 78.37 171 | 74.80 33 | 90.76 98 | 82.40 154 |
|
test_8 | | | | | | 85.09 60 | 67.89 75 | 76.26 107 | 78.66 148 | 54.00 194 | 76.89 151 | 86.72 131 | 66.60 94 | 80.89 126 | | | |
|
V9 | | | 75.82 81 | 76.53 79 | 73.66 111 | 74.28 198 | 48.37 197 | 76.26 107 | 81.10 101 | 61.73 107 | 86.59 34 | 90.43 57 | 59.16 164 | 81.42 97 | 70.71 61 | 88.56 130 | 91.21 18 |
|
v11 | | | 75.76 83 | 76.51 80 | 73.48 118 | 74.28 198 | 47.81 209 | 76.16 109 | 81.28 94 | 61.56 108 | 86.39 36 | 90.38 63 | 59.32 162 | 81.41 98 | 70.85 59 | 88.41 132 | 91.23 16 |
|
agg_prior1 | | | 75.89 80 | 76.41 81 | 74.31 100 | 84.44 73 | 66.02 85 | 76.12 110 | 78.62 149 | 54.40 188 | 76.95 148 | 86.85 123 | 66.44 98 | 80.34 135 | 72.45 51 | 91.42 79 | 76.57 234 |
|
CDPH-MVS | | | 77.33 70 | 77.06 74 | 78.14 64 | 84.21 76 | 63.98 100 | 76.07 111 | 83.45 60 | 54.20 190 | 77.68 142 | 87.18 115 | 69.98 67 | 85.37 34 | 68.01 87 | 92.72 63 | 85.08 96 |
|
CNLPA | | | 73.44 119 | 73.03 133 | 74.66 94 | 78.27 139 | 75.29 27 | 75.99 112 | 78.49 151 | 65.39 66 | 75.67 167 | 83.22 195 | 61.23 142 | 66.77 287 | 53.70 194 | 85.33 183 | 81.92 166 |
|
V14 | | | 75.58 86 | 76.26 85 | 73.55 116 | 74.10 206 | 48.13 202 | 75.91 113 | 81.07 104 | 61.19 111 | 86.34 37 | 90.11 71 | 58.80 168 | 81.40 100 | 70.40 63 | 88.43 131 | 91.12 19 |
|
v748 | | | 76.93 72 | 77.95 66 | 73.87 106 | 73.94 207 | 52.44 170 | 75.90 114 | 79.98 128 | 65.34 69 | 86.97 30 | 91.77 23 | 67.40 89 | 78.40 169 | 70.23 65 | 90.01 110 | 90.76 30 |
|
UGNet | | | 70.20 166 | 69.05 179 | 73.65 112 | 76.24 165 | 63.64 102 | 75.87 115 | 72.53 206 | 61.48 109 | 60.93 300 | 86.14 152 | 52.37 220 | 77.12 185 | 50.67 209 | 85.21 186 | 80.17 201 |
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 |
v10 | | | 75.69 85 | 76.20 86 | 74.16 102 | 74.44 196 | 48.69 192 | 75.84 116 | 82.93 67 | 59.02 130 | 85.92 43 | 89.17 86 | 58.56 173 | 82.74 76 | 70.73 60 | 89.14 123 | 91.05 20 |
|
MVS_0304 | | | 74.55 107 | 73.47 118 | 77.80 67 | 77.41 153 | 63.88 101 | 75.75 117 | 83.67 55 | 63.55 90 | 66.12 269 | 82.16 206 | 60.20 151 | 86.15 22 | 65.37 115 | 86.98 158 | 83.38 134 |
|
test_prior3 | | | 76.71 74 | 77.19 71 | 75.27 92 | 82.15 98 | 59.85 127 | 75.57 118 | 84.33 41 | 58.92 131 | 76.53 159 | 86.78 126 | 67.83 86 | 83.39 67 | 69.81 70 | 92.76 61 | 82.58 150 |
|
test_prior2 | | | | | | | | 75.57 118 | | 58.92 131 | 76.53 159 | 86.78 126 | 67.83 86 | | 69.81 70 | 92.76 61 | |
|
v15 | | | 75.37 89 | 76.01 87 | 73.44 120 | 73.91 210 | 47.87 208 | 75.55 120 | 81.04 105 | 60.76 116 | 86.11 41 | 89.76 77 | 58.53 174 | 81.40 100 | 70.11 66 | 88.32 133 | 91.04 22 |
|
v17 | | | 75.03 97 | 75.59 92 | 73.36 121 | 73.56 212 | 47.66 213 | 75.48 121 | 81.45 89 | 60.58 118 | 85.55 48 | 89.02 91 | 58.36 176 | 81.47 94 | 69.69 73 | 86.59 162 | 90.96 23 |
|
v16 | | | 74.89 102 | 75.41 96 | 73.35 122 | 73.54 213 | 47.62 214 | 75.47 122 | 81.45 89 | 60.58 118 | 85.46 50 | 88.97 94 | 58.27 177 | 81.47 94 | 69.66 74 | 85.25 185 | 90.95 24 |
|
mvs-test1 | | | 73.81 114 | 70.69 168 | 83.18 3 | 77.05 154 | 81.39 4 | 75.39 123 | 77.70 164 | 57.68 140 | 71.19 225 | 74.72 283 | 64.80 111 | 83.66 61 | 56.46 170 | 81.19 242 | 84.50 111 |
|
PAPR | | | 69.20 179 | 68.66 190 | 70.82 166 | 75.15 177 | 47.77 210 | 75.31 124 | 81.11 98 | 49.62 242 | 66.33 268 | 79.27 239 | 61.53 136 | 82.96 73 | 48.12 230 | 81.50 233 | 81.74 168 |
|
v8 | | | 75.07 96 | 75.64 91 | 73.35 122 | 73.42 216 | 47.46 218 | 75.20 125 | 81.45 89 | 60.05 122 | 85.64 45 | 89.26 82 | 58.08 183 | 81.80 90 | 69.71 72 | 87.97 139 | 90.79 28 |
|
tttt0517 | | | 69.46 175 | 67.79 198 | 74.46 96 | 75.34 174 | 52.72 168 | 75.05 126 | 63.27 258 | 54.69 183 | 78.87 127 | 84.37 176 | 26.63 349 | 81.15 110 | 63.95 124 | 87.93 140 | 89.51 35 |
|
v18 | | | 74.60 106 | 75.06 97 | 73.22 127 | 73.29 222 | 47.36 222 | 75.02 127 | 81.47 88 | 60.01 123 | 85.13 54 | 88.44 101 | 57.93 190 | 81.47 94 | 69.26 76 | 85.02 189 | 90.84 27 |
|
TSAR-MVS + GP. | | | 73.08 125 | 71.60 158 | 77.54 70 | 78.99 134 | 70.73 54 | 74.96 128 | 69.38 232 | 60.73 117 | 74.39 185 | 78.44 248 | 57.72 193 | 82.78 75 | 60.16 143 | 89.60 116 | 79.11 211 |
|
MAR-MVS | | | 67.72 198 | 66.16 208 | 72.40 152 | 74.45 194 | 64.99 94 | 74.87 129 | 77.50 168 | 48.67 247 | 65.78 273 | 68.58 334 | 57.01 203 | 77.79 180 | 46.68 242 | 81.92 223 | 74.42 248 |
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 |
无先验 | | | | | | | | 74.82 130 | 70.94 224 | 47.75 257 | | | | 76.85 189 | 54.47 187 | | 72.09 267 |
|
CANet | | | 73.00 128 | 71.84 151 | 76.48 75 | 75.82 171 | 61.28 117 | 74.81 131 | 80.37 118 | 63.17 96 | 62.43 289 | 80.50 223 | 61.10 144 | 85.16 42 | 64.00 123 | 84.34 197 | 83.01 140 |
|
PVSNet_Blended_VisFu | | | 70.04 167 | 68.88 184 | 73.53 117 | 82.71 91 | 63.62 103 | 74.81 131 | 81.95 81 | 48.53 248 | 67.16 265 | 79.18 242 | 51.42 227 | 78.38 170 | 54.39 190 | 79.72 261 | 78.60 215 |
|
MCST-MVS | | | 73.42 120 | 73.34 123 | 73.63 114 | 81.28 108 | 59.17 133 | 74.80 133 | 83.13 64 | 45.50 270 | 72.84 201 | 83.78 184 | 65.15 108 | 80.99 118 | 64.54 120 | 89.09 124 | 80.73 187 |
|
原ACMM2 | | | | | | | | 74.78 134 | | | | | | | | | |
|
Anonymous20231211 | | | 75.54 87 | 77.19 71 | 70.59 168 | 77.67 150 | 45.70 239 | 74.73 135 | 80.19 123 | 68.80 42 | 82.95 77 | 92.91 8 | 66.26 99 | 76.76 191 | 58.41 156 | 92.77 60 | 89.30 37 |
|
Effi-MVS+ | | | 72.10 149 | 72.28 147 | 71.58 160 | 74.21 204 | 50.33 178 | 74.72 136 | 82.73 69 | 62.62 100 | 70.77 229 | 76.83 258 | 69.96 68 | 80.97 119 | 60.20 142 | 78.43 271 | 83.45 133 |
|
K. test v3 | | | 73.67 115 | 73.61 117 | 73.87 106 | 79.78 117 | 55.62 149 | 74.69 137 | 62.04 266 | 66.16 60 | 84.76 59 | 93.23 5 | 49.47 232 | 80.97 119 | 65.66 112 | 86.67 161 | 85.02 97 |
|
MG-MVS | | | 70.47 165 | 71.34 162 | 67.85 206 | 79.26 125 | 40.42 271 | 74.67 138 | 75.15 190 | 58.41 133 | 68.74 249 | 88.14 110 | 56.08 208 | 83.69 60 | 59.90 146 | 81.71 230 | 79.43 208 |
|
DP-MVS Recon | | | 73.57 118 | 72.69 141 | 76.23 82 | 82.85 89 | 63.39 104 | 74.32 139 | 82.96 66 | 57.75 139 | 70.35 235 | 81.98 208 | 64.34 115 | 84.41 54 | 49.69 216 | 89.95 113 | 80.89 182 |
|
ambc | | | | | 70.10 177 | 77.74 147 | 50.21 180 | 74.28 140 | 77.93 162 | | 79.26 123 | 88.29 106 | 54.11 214 | 79.77 145 | 64.43 121 | 91.10 87 | 80.30 194 |
|
nrg030 | | | 74.87 103 | 75.99 88 | 71.52 162 | 74.90 181 | 49.88 185 | 74.10 141 | 82.58 73 | 54.55 187 | 83.50 74 | 89.21 85 | 71.51 56 | 75.74 200 | 61.24 138 | 92.34 67 | 88.94 47 |
|
canonicalmvs | | | 72.29 147 | 73.38 121 | 69.04 189 | 74.23 200 | 47.37 221 | 73.93 142 | 83.18 62 | 54.36 189 | 76.61 156 | 81.64 214 | 72.03 52 | 75.34 203 | 57.12 163 | 87.28 153 | 84.40 113 |
|
CANet_DTU | | | 64.04 225 | 63.83 219 | 64.66 230 | 68.39 278 | 42.97 251 | 73.45 143 | 74.50 193 | 52.05 214 | 54.78 329 | 75.44 278 | 43.99 254 | 70.42 255 | 53.49 196 | 78.41 272 | 80.59 190 |
|
PCF-MVS | | 63.80 13 | 72.70 139 | 71.69 154 | 75.72 87 | 78.10 141 | 60.01 126 | 73.04 144 | 81.50 86 | 45.34 273 | 79.66 118 | 84.35 177 | 65.15 108 | 82.65 77 | 48.70 224 | 89.38 120 | 84.50 111 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
Regformer-3 | | | 72.86 137 | 72.28 147 | 74.62 95 | 74.74 185 | 60.18 124 | 72.91 145 | 71.76 211 | 64.74 76 | 78.42 132 | 72.07 307 | 67.00 90 | 76.28 195 | 67.97 90 | 80.91 244 | 87.39 69 |
|
Regformer-4 | | | 74.64 105 | 73.67 114 | 77.55 69 | 74.74 185 | 64.49 97 | 72.91 145 | 75.42 187 | 67.45 49 | 80.24 114 | 72.07 307 | 68.98 74 | 80.19 141 | 70.29 64 | 80.91 244 | 87.98 63 |
|
testing_2 | | | 72.01 151 | 72.36 145 | 70.95 165 | 70.79 252 | 48.70 191 | 72.81 147 | 78.09 160 | 48.79 246 | 84.46 66 | 89.15 88 | 57.90 191 | 78.55 161 | 61.55 136 | 87.74 141 | 85.61 90 |
|
Regformer-1 | | | 74.28 109 | 73.63 116 | 76.21 83 | 74.22 201 | 64.12 99 | 72.77 148 | 75.46 186 | 66.86 53 | 79.27 122 | 72.08 304 | 69.29 71 | 78.74 158 | 68.73 78 | 84.02 201 | 85.77 89 |
|
Regformer-2 | | | 75.32 91 | 74.47 103 | 77.88 66 | 74.22 201 | 66.65 81 | 72.77 148 | 77.54 166 | 68.47 47 | 80.44 110 | 72.08 304 | 70.60 63 | 80.97 119 | 70.08 68 | 84.02 201 | 86.01 83 |
|
v7 | | | 73.59 117 | 73.69 113 | 73.28 126 | 74.42 197 | 48.68 193 | 72.74 150 | 81.98 79 | 54.76 182 | 82.07 84 | 85.05 167 | 58.53 174 | 82.22 86 | 67.99 88 | 85.66 173 | 88.95 46 |
|
test_0402 | | | 78.17 66 | 79.48 54 | 74.24 101 | 83.50 81 | 59.15 134 | 72.52 151 | 74.60 192 | 75.34 12 | 88.69 15 | 91.81 22 | 75.06 32 | 82.37 81 | 65.10 116 | 88.68 128 | 81.20 174 |
|
EU-MVSNet | | | 60.82 251 | 60.80 248 | 60.86 270 | 68.37 279 | 41.16 261 | 72.27 152 | 68.27 237 | 26.96 358 | 69.08 243 | 75.71 271 | 32.09 309 | 67.44 279 | 55.59 179 | 78.90 266 | 73.97 249 |
|
EI-MVSNet-Vis-set | | | 72.78 138 | 71.87 150 | 75.54 89 | 74.77 184 | 59.02 135 | 72.24 153 | 71.56 214 | 63.92 85 | 78.59 128 | 71.59 314 | 66.22 100 | 78.60 160 | 67.58 93 | 80.32 252 | 89.00 44 |
|
v1192 | | | 73.40 121 | 73.42 119 | 73.32 125 | 74.65 191 | 48.67 194 | 72.21 154 | 81.73 83 | 52.76 208 | 81.85 86 | 84.56 174 | 57.12 200 | 82.24 85 | 68.58 79 | 87.33 151 | 89.06 42 |
|
v1neww | | | 72.93 132 | 73.07 130 | 72.48 148 | 73.41 218 | 47.46 218 | 72.17 155 | 80.26 120 | 55.63 164 | 81.63 93 | 85.07 165 | 57.97 187 | 81.28 106 | 66.55 106 | 84.98 191 | 88.70 53 |
|
v7new | | | 72.93 132 | 73.07 130 | 72.48 148 | 73.41 218 | 47.46 218 | 72.17 155 | 80.26 120 | 55.63 164 | 81.63 93 | 85.07 165 | 57.97 187 | 81.28 106 | 66.55 106 | 84.98 191 | 88.70 53 |
|
v6 | | | 72.93 132 | 73.08 129 | 72.48 148 | 73.42 216 | 47.47 217 | 72.17 155 | 80.25 122 | 55.63 164 | 81.65 92 | 85.04 168 | 57.95 189 | 81.28 106 | 66.56 105 | 85.01 190 | 88.70 53 |
|
EI-MVSNet-UG-set | | | 72.63 140 | 71.68 155 | 75.47 90 | 74.67 188 | 58.64 139 | 72.02 158 | 71.50 215 | 63.53 91 | 78.58 130 | 71.39 317 | 65.98 101 | 78.53 162 | 67.30 99 | 80.18 253 | 89.23 39 |
|
v1144 | | | 73.29 124 | 73.39 120 | 73.01 133 | 74.12 205 | 48.11 203 | 72.01 159 | 81.08 103 | 53.83 198 | 81.77 87 | 84.68 172 | 58.07 184 | 81.91 88 | 68.10 83 | 86.86 159 | 88.99 45 |
|
GBi-Net | | | 68.30 192 | 68.79 186 | 66.81 215 | 73.14 225 | 40.68 266 | 71.96 160 | 73.03 198 | 54.81 177 | 74.72 180 | 90.36 65 | 48.63 237 | 75.20 205 | 47.12 236 | 85.37 180 | 84.54 107 |
|
test1 | | | 68.30 192 | 68.79 186 | 66.81 215 | 73.14 225 | 40.68 266 | 71.96 160 | 73.03 198 | 54.81 177 | 74.72 180 | 90.36 65 | 48.63 237 | 75.20 205 | 47.12 236 | 85.37 180 | 84.54 107 |
|
FMVSNet1 | | | 71.06 157 | 72.48 143 | 66.81 215 | 77.65 151 | 40.68 266 | 71.96 160 | 73.03 198 | 61.14 112 | 79.45 121 | 90.36 65 | 60.44 149 | 75.20 205 | 50.20 213 | 88.05 136 | 84.54 107 |
|
v1921920 | | | 72.96 131 | 72.98 134 | 72.89 138 | 74.67 188 | 47.58 215 | 71.92 163 | 80.69 110 | 51.70 218 | 81.69 91 | 83.89 182 | 56.58 205 | 82.25 84 | 68.34 81 | 87.36 149 | 88.82 50 |
|
v144192 | | | 72.99 129 | 73.06 132 | 72.77 140 | 74.58 193 | 47.48 216 | 71.90 164 | 80.44 117 | 51.57 219 | 81.46 96 | 84.11 180 | 58.04 185 | 82.12 87 | 67.98 89 | 87.47 145 | 88.70 53 |
|
v1240 | | | 73.06 126 | 73.14 126 | 72.84 139 | 74.74 185 | 47.27 224 | 71.88 165 | 81.11 98 | 51.80 216 | 82.28 83 | 84.21 178 | 56.22 207 | 82.34 82 | 68.82 77 | 87.17 156 | 88.91 48 |
|
FC-MVSNet-test | | | 73.32 123 | 74.78 100 | 68.93 192 | 79.21 127 | 36.57 297 | 71.82 166 | 79.54 133 | 57.63 144 | 82.57 81 | 90.38 63 | 59.38 161 | 78.99 152 | 57.91 158 | 94.56 33 | 91.23 16 |
|
wuykxyi23d | | | 75.33 90 | 76.75 77 | 71.04 164 | 78.83 135 | 85.01 1 | 71.78 167 | 61.00 269 | 53.47 202 | 96.33 1 | 93.38 3 | 73.07 45 | 68.04 275 | 65.65 113 | 97.28 2 | 60.07 336 |
|
tpmp4_e23 | | | 57.57 278 | 55.46 292 | 63.93 237 | 66.48 296 | 41.56 260 | 71.68 168 | 60.65 271 | 35.64 325 | 55.35 328 | 76.25 262 | 29.53 336 | 75.41 202 | 34.40 318 | 69.12 322 | 74.83 245 |
|
IterMVS-LS | | | 73.01 127 | 73.12 128 | 72.66 144 | 73.79 211 | 49.90 182 | 71.63 169 | 78.44 152 | 58.22 134 | 80.51 109 | 86.63 136 | 58.15 180 | 79.62 146 | 62.51 131 | 88.20 134 | 88.48 60 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
v1141 | | | 72.59 143 | 72.73 138 | 72.19 155 | 73.10 229 | 47.00 228 | 71.48 170 | 79.11 137 | 55.01 173 | 81.23 99 | 84.94 170 | 57.45 196 | 80.89 126 | 66.58 103 | 85.65 174 | 88.68 57 |
|
divwei89l23v2f112 | | | 72.60 141 | 72.73 138 | 72.19 155 | 73.10 229 | 47.00 228 | 71.48 170 | 79.11 137 | 55.01 173 | 81.23 99 | 84.95 169 | 57.45 196 | 80.89 126 | 66.58 103 | 85.67 171 | 88.68 57 |
|
v1 | | | 72.60 141 | 72.73 138 | 72.19 155 | 73.12 228 | 47.01 227 | 71.48 170 | 79.10 139 | 55.01 173 | 81.24 98 | 84.92 171 | 57.46 195 | 80.90 125 | 66.59 102 | 85.67 171 | 88.68 57 |
|
EG-PatchMatch MVS | | | 70.70 162 | 70.88 165 | 70.16 175 | 82.64 92 | 58.80 136 | 71.48 170 | 73.64 196 | 54.98 176 | 76.55 157 | 81.77 211 | 61.10 144 | 78.94 153 | 54.87 184 | 80.84 247 | 72.74 260 |
|
LF4IMVS | | | 67.50 199 | 67.31 202 | 68.08 204 | 58.86 339 | 61.93 112 | 71.43 174 | 75.90 181 | 44.67 278 | 72.42 210 | 80.20 225 | 57.16 198 | 70.44 254 | 58.99 152 | 86.12 166 | 71.88 268 |
|
v2v482 | | | 72.55 146 | 72.58 142 | 72.43 151 | 72.92 240 | 46.72 232 | 71.41 175 | 79.13 136 | 55.27 168 | 81.17 101 | 85.25 163 | 55.41 209 | 81.13 111 | 67.25 100 | 85.46 179 | 89.43 36 |
|
Fast-Effi-MVS+-dtu | | | 70.00 168 | 68.74 189 | 73.77 109 | 73.47 215 | 64.53 96 | 71.36 176 | 78.14 159 | 55.81 162 | 68.84 248 | 74.71 284 | 65.36 107 | 75.75 199 | 52.00 199 | 79.00 265 | 81.03 179 |
|
新几何2 | | | | | | | | 71.33 177 | | | | | | | | | |
|
EI-MVSNet | | | 69.61 172 | 69.01 182 | 71.41 163 | 73.94 207 | 49.90 182 | 71.31 178 | 71.32 217 | 58.22 134 | 75.40 172 | 70.44 318 | 58.16 179 | 75.85 196 | 62.51 131 | 79.81 258 | 88.48 60 |
|
CVMVSNet | | | 59.21 265 | 58.44 271 | 61.51 262 | 73.94 207 | 47.76 211 | 71.31 178 | 64.56 252 | 26.91 359 | 60.34 302 | 70.44 318 | 36.24 293 | 67.65 277 | 53.57 195 | 68.66 325 | 69.12 298 |
|
thisisatest0530 | | | 67.05 204 | 65.16 210 | 72.73 143 | 73.10 229 | 50.55 177 | 71.26 180 | 63.91 254 | 50.22 237 | 74.46 184 | 80.75 219 | 26.81 348 | 80.25 137 | 59.43 149 | 86.50 163 | 87.37 70 |
|
diffmvs | | | 69.55 173 | 70.18 171 | 67.66 209 | 63.63 314 | 45.24 241 | 71.26 180 | 76.21 177 | 55.79 163 | 67.89 252 | 86.41 144 | 61.00 146 | 73.76 220 | 68.03 85 | 81.40 234 | 83.98 120 |
|
旧先验2 | | | | | | | | 71.17 182 | | 45.11 275 | 78.54 131 | | | 61.28 307 | 59.19 151 | | |
|
diffmvs1 | | | 70.85 160 | 71.63 156 | 68.50 201 | 64.78 309 | 46.14 236 | 71.03 183 | 77.76 163 | 57.00 152 | 72.44 209 | 87.61 114 | 61.32 138 | 74.11 217 | 69.58 75 | 83.16 211 | 85.26 92 |
|
FIs | | | 72.56 144 | 73.80 111 | 68.84 197 | 78.74 136 | 37.74 291 | 71.02 184 | 79.83 129 | 56.12 159 | 80.88 107 | 89.45 79 | 58.18 178 | 78.28 174 | 56.63 166 | 93.36 54 | 90.51 31 |
|
TranMVSNet+NR-MVSNet | | | 76.13 78 | 77.66 68 | 71.56 161 | 84.61 69 | 42.57 254 | 70.98 185 | 78.29 155 | 68.67 45 | 83.04 76 | 89.26 82 | 72.99 47 | 80.75 129 | 55.58 180 | 95.47 12 | 91.35 14 |
|
CR-MVSNet | | | 58.96 266 | 58.49 270 | 60.36 275 | 66.37 297 | 48.24 200 | 70.93 186 | 56.40 294 | 32.87 341 | 61.35 293 | 86.66 133 | 33.19 302 | 63.22 299 | 48.50 227 | 70.17 315 | 69.62 293 |
|
RPMNet | | | 61.25 248 | 61.55 242 | 60.36 275 | 66.37 297 | 48.24 200 | 70.93 186 | 54.45 304 | 54.66 184 | 61.35 293 | 86.77 128 | 33.29 301 | 63.22 299 | 55.93 175 | 70.17 315 | 69.62 293 |
|
LFMVS | | | 67.06 203 | 67.89 197 | 64.56 231 | 78.02 142 | 38.25 286 | 70.81 188 | 59.60 274 | 65.18 71 | 71.06 226 | 86.56 139 | 43.85 255 | 75.22 204 | 46.35 243 | 89.63 115 | 80.21 196 |
|
Test4 | | | 69.04 183 | 68.95 183 | 69.32 186 | 69.52 266 | 48.10 204 | 70.69 189 | 78.25 157 | 45.90 266 | 80.99 103 | 82.24 204 | 51.91 221 | 78.11 179 | 58.46 154 | 82.58 215 | 81.74 168 |
|
MVS_111021_LR | | | 72.10 149 | 71.82 153 | 72.95 136 | 79.53 121 | 73.90 36 | 70.45 190 | 66.64 242 | 56.87 153 | 76.81 153 | 81.76 212 | 68.78 75 | 71.76 245 | 61.81 134 | 83.74 205 | 73.18 255 |
|
UniMVSNet (Re) | | | 75.00 98 | 75.48 94 | 73.56 115 | 83.14 85 | 47.92 207 | 70.41 191 | 81.04 105 | 63.67 88 | 79.54 119 | 86.37 145 | 62.83 121 | 81.82 89 | 57.10 164 | 95.25 16 | 90.94 25 |
|
DI_MVS_plusplus_test | | | 69.01 184 | 69.04 180 | 68.93 192 | 69.54 265 | 46.74 231 | 70.14 192 | 75.49 184 | 46.64 262 | 78.30 134 | 83.18 196 | 58.80 168 | 78.86 154 | 57.14 162 | 82.15 219 | 81.18 175 |
|
TinyColmap | | | 67.98 194 | 69.28 176 | 64.08 235 | 67.98 285 | 46.82 230 | 70.04 193 | 75.26 188 | 53.05 205 | 77.36 144 | 86.79 125 | 59.39 160 | 72.59 235 | 45.64 246 | 88.01 138 | 72.83 258 |
|
VDDNet | | | 71.60 154 | 73.13 127 | 67.02 214 | 86.29 45 | 41.11 262 | 69.97 194 | 66.50 243 | 68.72 44 | 74.74 179 | 91.70 25 | 59.90 153 | 75.81 198 | 48.58 226 | 91.72 71 | 84.15 118 |
|
EPNet_dtu | | | 58.93 267 | 58.52 269 | 60.16 277 | 67.91 286 | 47.70 212 | 69.97 194 | 58.02 279 | 49.73 240 | 47.28 350 | 73.02 301 | 38.14 286 | 62.34 303 | 36.57 308 | 85.99 168 | 70.43 281 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MVS_Test | | | 69.84 170 | 70.71 167 | 67.24 212 | 67.49 290 | 43.25 249 | 69.87 196 | 81.22 97 | 52.69 209 | 71.57 219 | 86.68 132 | 62.09 131 | 74.51 213 | 66.05 109 | 78.74 267 | 83.96 121 |
|
alignmvs | | | 70.54 164 | 71.00 164 | 69.15 188 | 73.50 214 | 48.04 206 | 69.85 197 | 79.62 130 | 53.94 197 | 76.54 158 | 82.00 207 | 59.00 166 | 74.68 211 | 57.32 161 | 87.21 154 | 84.72 101 |
|
test_normal | | | 68.88 185 | 68.88 184 | 68.88 195 | 69.43 268 | 47.03 226 | 69.85 197 | 74.83 191 | 46.06 265 | 78.30 134 | 83.29 191 | 58.76 172 | 78.23 175 | 57.51 159 | 81.90 224 | 81.36 173 |
|
GG-mvs-BLEND | | | | | 52.24 308 | 60.64 329 | 29.21 346 | 69.73 199 | 42.41 350 | | 45.47 353 | 52.33 359 | 20.43 362 | 68.16 273 | 25.52 348 | 65.42 333 | 59.36 340 |
|
pmmvs-eth3d | | | 64.41 221 | 63.27 225 | 67.82 207 | 75.81 172 | 60.18 124 | 69.49 200 | 62.05 265 | 38.81 309 | 74.13 187 | 82.23 205 | 43.76 256 | 68.65 270 | 42.53 270 | 80.63 251 | 74.63 246 |
|
DU-MVS | | | 74.91 100 | 75.57 93 | 72.93 137 | 83.50 81 | 45.79 237 | 69.47 201 | 80.14 125 | 65.22 70 | 81.74 89 | 87.08 116 | 61.82 133 | 81.07 116 | 56.21 173 | 94.98 22 | 91.93 10 |
|
PAPM | | | 61.79 244 | 60.37 250 | 66.05 222 | 76.09 168 | 41.87 257 | 69.30 202 | 76.79 175 | 40.64 303 | 53.80 335 | 79.62 235 | 44.38 252 | 82.92 74 | 29.64 337 | 73.11 301 | 73.36 254 |
|
UniMVSNet_NR-MVSNet | | | 74.90 101 | 75.65 90 | 72.64 145 | 83.04 86 | 45.79 237 | 69.26 203 | 78.81 144 | 66.66 56 | 81.74 89 | 86.88 122 | 63.26 119 | 81.07 116 | 56.21 173 | 94.98 22 | 91.05 20 |
|
MVP-Stereo | | | 61.56 245 | 59.22 256 | 68.58 200 | 79.28 124 | 60.44 122 | 69.20 204 | 71.57 213 | 43.58 287 | 56.42 322 | 78.37 249 | 39.57 280 | 76.46 194 | 34.86 316 | 60.16 343 | 68.86 300 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
gg-mvs-nofinetune | | | 55.75 286 | 56.75 282 | 52.72 307 | 62.87 316 | 28.04 349 | 68.92 205 | 41.36 357 | 71.09 30 | 50.80 341 | 92.63 12 | 20.74 361 | 66.86 284 | 29.97 335 | 72.41 303 | 63.25 326 |
|
Baseline_NR-MVSNet | | | 70.62 163 | 73.19 125 | 62.92 247 | 76.97 157 | 34.44 316 | 68.84 206 | 70.88 225 | 60.25 121 | 79.50 120 | 90.53 53 | 61.82 133 | 69.11 260 | 54.67 186 | 95.27 15 | 85.22 93 |
|
v148 | | | 69.38 177 | 69.39 175 | 69.36 183 | 69.14 270 | 44.56 244 | 68.83 207 | 72.70 204 | 54.79 180 | 78.59 128 | 84.12 179 | 54.69 211 | 76.74 192 | 59.40 150 | 82.20 217 | 86.79 75 |
|
FMVSNet2 | | | 67.48 200 | 68.21 194 | 65.29 226 | 73.14 225 | 38.94 281 | 68.81 208 | 71.21 223 | 54.81 177 | 76.73 155 | 86.48 142 | 48.63 237 | 74.60 212 | 47.98 231 | 86.11 167 | 82.35 155 |
|
MVS_111021_HR | | | 72.98 130 | 72.97 135 | 72.99 134 | 80.82 111 | 65.47 88 | 68.81 208 | 72.77 203 | 57.67 142 | 75.76 166 | 82.38 203 | 71.01 61 | 77.17 184 | 61.38 137 | 86.15 165 | 76.32 235 |
|
Anonymous20240529 | | | 72.56 144 | 73.79 112 | 68.86 196 | 76.89 160 | 45.21 242 | 68.80 210 | 77.25 172 | 67.16 50 | 76.89 151 | 90.44 56 | 65.95 102 | 74.19 216 | 50.75 208 | 90.00 111 | 87.18 73 |
|
CLD-MVS | | | 72.88 136 | 72.36 145 | 74.43 98 | 77.03 156 | 54.30 158 | 68.77 211 | 83.43 61 | 52.12 212 | 76.79 154 | 74.44 287 | 69.54 70 | 83.91 57 | 55.88 176 | 93.25 56 | 85.09 95 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
1314 | | | 59.83 259 | 58.86 266 | 62.74 252 | 65.71 303 | 44.78 243 | 68.59 212 | 72.63 205 | 33.54 340 | 61.05 297 | 67.29 339 | 43.62 257 | 71.26 248 | 49.49 218 | 67.84 328 | 72.19 266 |
|
1121 | | | 69.23 178 | 68.26 193 | 72.12 158 | 88.36 33 | 71.40 46 | 68.59 212 | 62.06 264 | 43.80 283 | 74.75 178 | 86.18 149 | 52.92 217 | 76.85 189 | 54.47 187 | 83.27 209 | 68.12 302 |
|
MVS | | | 60.62 254 | 59.97 252 | 62.58 254 | 68.13 283 | 47.28 223 | 68.59 212 | 73.96 195 | 32.19 342 | 59.94 307 | 68.86 332 | 50.48 229 | 77.64 182 | 41.85 275 | 75.74 284 | 62.83 328 |
|
OpenMVS_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 54.93 17 | 63.23 229 | 63.28 224 | 63.07 246 | 69.81 262 | 45.34 240 | 68.52 215 | 67.14 239 | 43.74 285 | 70.61 233 | 79.22 240 | 47.90 241 | 72.66 231 | 48.75 223 | 73.84 299 | 71.21 275 |
|
PM-MVS | | | 64.49 218 | 63.61 222 | 67.14 213 | 76.68 161 | 75.15 28 | 68.49 216 | 42.85 349 | 51.17 225 | 77.85 139 | 80.51 222 | 45.76 244 | 66.31 290 | 52.83 197 | 76.35 282 | 59.96 338 |
|
BH-untuned | | | 69.39 176 | 69.46 174 | 69.18 187 | 77.96 144 | 56.88 144 | 68.47 217 | 77.53 167 | 56.77 155 | 77.79 140 | 79.63 234 | 60.30 150 | 80.20 140 | 46.04 244 | 80.65 249 | 70.47 280 |
|
testdata1 | | | | | | | | 68.34 218 | | 57.24 146 | | | | | | | |
|
tpm2 | | | 56.12 283 | 54.64 295 | 60.55 272 | 66.24 300 | 36.01 302 | 68.14 219 | 56.77 293 | 33.60 339 | 58.25 314 | 75.52 275 | 30.25 330 | 74.33 215 | 33.27 323 | 69.76 320 | 71.32 272 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 48.73 20 | 61.54 246 | 60.89 247 | 63.52 241 | 61.08 326 | 51.55 172 | 68.07 220 | 68.00 238 | 33.88 333 | 65.87 271 | 81.25 216 | 37.91 289 | 67.71 276 | 49.32 219 | 82.60 214 | 71.31 273 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test222 | | | | | | 87.30 36 | 69.15 69 | 67.85 221 | 59.59 275 | 41.06 298 | 73.05 199 | 85.72 160 | 48.03 240 | | | 80.65 249 | 66.92 309 |
|
VDD-MVS | | | 70.81 161 | 71.44 161 | 68.91 194 | 79.07 133 | 46.51 233 | 67.82 222 | 70.83 226 | 61.23 110 | 74.07 189 | 88.69 98 | 59.86 154 | 75.62 201 | 51.11 205 | 90.28 104 | 84.61 105 |
|
ab-mvs | | | 64.11 224 | 65.13 213 | 61.05 267 | 71.99 248 | 38.03 290 | 67.59 223 | 68.79 234 | 49.08 244 | 65.32 274 | 86.26 147 | 58.02 186 | 66.85 285 | 39.33 286 | 79.79 260 | 78.27 219 |
|
CostFormer | | | 57.35 280 | 56.14 286 | 60.97 268 | 63.76 313 | 38.43 283 | 67.50 224 | 60.22 272 | 37.14 318 | 59.12 310 | 76.34 261 | 32.78 304 | 71.99 242 | 39.12 288 | 69.27 321 | 72.47 262 |
|
Patchmtry | | | 60.91 250 | 63.01 228 | 54.62 303 | 66.10 301 | 26.27 354 | 67.47 225 | 56.40 294 | 54.05 193 | 72.04 214 | 86.66 133 | 33.19 302 | 60.17 309 | 43.69 254 | 87.45 148 | 77.42 227 |
|
USDC | | | 62.80 238 | 63.10 227 | 61.89 259 | 65.19 305 | 43.30 248 | 67.42 226 | 74.20 194 | 35.80 324 | 72.25 212 | 84.48 175 | 45.67 245 | 71.95 243 | 37.95 299 | 84.97 193 | 70.42 282 |
|
xiu_mvs_v1_base_debu | | | 67.87 195 | 67.07 203 | 70.26 172 | 79.13 130 | 61.90 113 | 67.34 227 | 71.25 220 | 47.98 252 | 67.70 254 | 74.19 292 | 61.31 139 | 72.62 232 | 56.51 167 | 78.26 273 | 76.27 236 |
|
xiu_mvs_v1_base | | | 67.87 195 | 67.07 203 | 70.26 172 | 79.13 130 | 61.90 113 | 67.34 227 | 71.25 220 | 47.98 252 | 67.70 254 | 74.19 292 | 61.31 139 | 72.62 232 | 56.51 167 | 78.26 273 | 76.27 236 |
|
xiu_mvs_v1_base_debi | | | 67.87 195 | 67.07 203 | 70.26 172 | 79.13 130 | 61.90 113 | 67.34 227 | 71.25 220 | 47.98 252 | 67.70 254 | 74.19 292 | 61.31 139 | 72.62 232 | 56.51 167 | 78.26 273 | 76.27 236 |
|
Vis-MVSNet (Re-imp) | | | 62.74 239 | 63.21 226 | 61.34 265 | 72.19 243 | 31.56 341 | 67.31 230 | 53.87 306 | 53.60 200 | 69.88 238 | 83.37 188 | 40.52 276 | 70.98 249 | 41.40 278 | 86.78 160 | 81.48 172 |
|
jason | | | 64.47 219 | 62.84 233 | 69.34 185 | 76.91 159 | 59.20 130 | 67.15 231 | 65.67 245 | 35.29 326 | 65.16 275 | 76.74 259 | 44.67 250 | 70.68 250 | 54.74 185 | 79.28 264 | 78.14 221 |
jason: jason. |
pmmvs6 | | | 71.82 152 | 73.66 115 | 66.31 221 | 75.94 170 | 42.01 256 | 66.99 232 | 72.53 206 | 63.45 93 | 76.43 162 | 92.78 10 | 72.95 48 | 69.69 258 | 51.41 203 | 90.46 102 | 87.22 71 |
|
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 54.60 291 | 54.27 297 | 55.59 300 | 65.17 307 | 39.08 278 | 66.92 233 | 51.80 322 | 39.89 305 | 58.39 312 | 73.12 300 | 31.69 314 | 58.33 313 | 43.01 259 | 58.38 352 | 69.38 296 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MSDG | | | 67.47 201 | 67.48 200 | 67.46 210 | 70.70 255 | 54.69 155 | 66.90 234 | 78.17 158 | 60.88 115 | 70.41 234 | 74.76 281 | 61.22 143 | 73.18 222 | 47.38 235 | 76.87 279 | 74.49 247 |
|
TAMVS | | | 65.31 210 | 63.75 220 | 69.97 180 | 82.23 97 | 59.76 129 | 66.78 235 | 63.37 257 | 45.20 274 | 69.79 239 | 79.37 238 | 47.42 243 | 72.17 237 | 34.48 317 | 85.15 188 | 77.99 225 |
|
test_post1 | | | | | | | | 66.63 236 | | | | 2.08 367 | 30.66 328 | 59.33 311 | 40.34 285 | | |
|
FMVSNet3 | | | 65.00 214 | 65.16 210 | 64.52 232 | 69.47 267 | 37.56 294 | 66.63 236 | 70.38 228 | 51.55 220 | 74.72 180 | 83.27 192 | 37.89 290 | 74.44 214 | 47.12 236 | 85.37 180 | 81.57 171 |
|
mvs_anonymous | | | 65.08 213 | 65.49 209 | 63.83 238 | 63.79 312 | 37.60 293 | 66.52 238 | 69.82 231 | 43.44 288 | 73.46 195 | 86.08 154 | 58.79 171 | 71.75 246 | 51.90 200 | 75.63 286 | 82.15 159 |
|
wuyk23d | | | 61.97 241 | 66.25 207 | 49.12 318 | 58.19 345 | 60.77 121 | 66.32 239 | 52.97 312 | 55.93 161 | 90.62 7 | 86.91 121 | 73.07 45 | 35.98 359 | 20.63 358 | 91.63 73 | 50.62 350 |
|
tpm cat1 | | | 54.02 295 | 52.63 303 | 58.19 286 | 64.85 308 | 39.86 274 | 66.26 240 | 57.28 286 | 32.16 343 | 56.90 319 | 70.39 320 | 32.75 305 | 65.30 292 | 34.29 319 | 58.79 348 | 69.41 295 |
|
view600 | | | 62.88 234 | 62.90 229 | 62.82 248 | 72.97 236 | 33.66 322 | 66.10 241 | 55.01 299 | 57.05 147 | 72.66 203 | 82.56 199 | 31.60 315 | 72.78 226 | 42.64 266 | 85.55 175 | 82.02 160 |
|
view800 | | | 62.88 234 | 62.90 229 | 62.82 248 | 72.97 236 | 33.66 322 | 66.10 241 | 55.01 299 | 57.05 147 | 72.66 203 | 82.56 199 | 31.60 315 | 72.78 226 | 42.64 266 | 85.55 175 | 82.02 160 |
|
conf0.05thres1000 | | | 62.88 234 | 62.90 229 | 62.82 248 | 72.97 236 | 33.66 322 | 66.10 241 | 55.01 299 | 57.05 147 | 72.66 203 | 82.56 199 | 31.60 315 | 72.78 226 | 42.64 266 | 85.55 175 | 82.02 160 |
|
tfpn | | | 62.88 234 | 62.90 229 | 62.82 248 | 72.97 236 | 33.66 322 | 66.10 241 | 55.01 299 | 57.05 147 | 72.66 203 | 82.56 199 | 31.60 315 | 72.78 226 | 42.64 266 | 85.55 175 | 82.02 160 |
|
Fast-Effi-MVS+ | | | 68.81 187 | 68.30 192 | 70.35 171 | 74.66 190 | 48.61 195 | 66.06 245 | 78.32 153 | 50.62 234 | 71.48 222 | 75.54 273 | 68.75 76 | 79.59 148 | 50.55 211 | 78.73 268 | 82.86 143 |
|
V42 | | | 71.06 157 | 70.83 166 | 71.72 159 | 67.25 291 | 47.14 225 | 65.94 246 | 80.35 119 | 51.35 221 | 83.40 75 | 83.23 193 | 59.25 163 | 78.80 156 | 65.91 111 | 80.81 248 | 89.23 39 |
|
tpmvs | | | 55.84 285 | 55.45 293 | 57.01 292 | 60.33 330 | 33.20 328 | 65.89 247 | 59.29 276 | 47.52 259 | 56.04 323 | 73.60 295 | 31.05 325 | 68.06 274 | 40.64 282 | 64.64 334 | 69.77 287 |
|
lupinMVS | | | 63.36 227 | 61.49 243 | 68.97 190 | 74.93 179 | 59.19 131 | 65.80 248 | 64.52 253 | 34.68 331 | 63.53 285 | 74.25 290 | 43.19 259 | 70.62 251 | 53.88 193 | 78.67 269 | 77.10 231 |
|
TransMVSNet (Re) | | | 69.62 171 | 71.63 156 | 63.57 240 | 76.51 162 | 35.93 304 | 65.75 249 | 71.29 219 | 61.05 113 | 75.02 174 | 89.90 75 | 65.88 103 | 70.41 256 | 49.79 215 | 89.48 118 | 84.38 114 |
|
NR-MVSNet | | | 73.62 116 | 74.05 109 | 72.33 154 | 83.50 81 | 43.71 247 | 65.65 250 | 77.32 170 | 64.32 82 | 75.59 168 | 87.08 116 | 62.45 126 | 81.34 103 | 54.90 183 | 95.63 10 | 91.93 10 |
|
BH-w/o | | | 64.81 215 | 64.29 217 | 66.36 220 | 76.08 169 | 54.71 154 | 65.61 251 | 75.23 189 | 50.10 239 | 71.05 227 | 71.86 313 | 54.33 213 | 79.02 151 | 38.20 297 | 76.14 283 | 65.36 318 |
|
PVSNet_BlendedMVS | | | 65.38 209 | 64.30 216 | 68.61 199 | 69.81 262 | 49.36 186 | 65.60 252 | 78.96 141 | 45.50 270 | 59.98 305 | 78.61 247 | 51.82 223 | 78.20 177 | 44.30 250 | 84.11 199 | 78.27 219 |
|
tfpn111 | | | 61.91 242 | 61.65 239 | 62.68 253 | 72.14 244 | 35.01 310 | 65.42 253 | 56.99 289 | 55.23 169 | 70.71 230 | 79.90 228 | 32.07 310 | 72.85 225 | 38.80 290 | 83.61 206 | 80.18 197 |
|
conf200view11 | | | 61.42 247 | 61.09 245 | 62.43 256 | 72.14 244 | 35.01 310 | 65.42 253 | 56.99 289 | 55.23 169 | 70.71 230 | 79.90 228 | 32.07 310 | 72.09 238 | 35.61 312 | 81.73 226 | 80.18 197 |
|
thres100view900 | | | 61.17 249 | 61.09 245 | 61.39 264 | 72.14 244 | 35.01 310 | 65.42 253 | 56.99 289 | 55.23 169 | 70.71 230 | 79.90 228 | 32.07 310 | 72.09 238 | 35.61 312 | 81.73 226 | 77.08 232 |
|
CDS-MVSNet | | | 64.33 222 | 62.66 235 | 69.35 184 | 80.44 114 | 58.28 140 | 65.26 256 | 65.66 246 | 44.36 280 | 67.30 264 | 75.54 273 | 43.27 258 | 71.77 244 | 37.68 300 | 84.44 196 | 78.01 224 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
Patchmatch-test1 | | | 57.81 277 | 58.04 272 | 57.13 291 | 70.17 261 | 41.07 263 | 65.19 257 | 53.38 310 | 43.34 291 | 61.00 298 | 71.94 311 | 45.20 247 | 62.69 301 | 41.81 276 | 70.31 314 | 67.63 305 |
|
HY-MVS | | 49.31 19 | 57.96 276 | 57.59 275 | 59.10 281 | 66.85 295 | 36.17 301 | 65.13 258 | 65.39 249 | 39.24 307 | 54.69 331 | 78.14 250 | 44.28 253 | 67.18 282 | 33.75 322 | 70.79 311 | 73.95 250 |
|
thres600view7 | | | 61.82 243 | 61.38 244 | 63.12 245 | 71.81 249 | 34.93 313 | 64.64 259 | 56.99 289 | 54.78 181 | 70.33 236 | 79.74 233 | 32.07 310 | 72.42 236 | 38.61 293 | 83.46 207 | 82.02 160 |
|
BH-RMVSNet | | | 68.69 190 | 68.20 195 | 70.14 176 | 76.40 163 | 53.90 162 | 64.62 260 | 73.48 197 | 58.01 136 | 73.91 191 | 81.78 210 | 59.09 165 | 78.22 176 | 48.59 225 | 77.96 276 | 78.31 218 |
|
pm-mvs1 | | | 68.40 191 | 69.85 173 | 64.04 236 | 73.10 229 | 39.94 273 | 64.61 261 | 70.50 227 | 55.52 167 | 73.97 190 | 89.33 80 | 63.91 117 | 68.38 272 | 49.68 217 | 88.02 137 | 83.81 124 |
|
pmmvs4 | | | 60.78 252 | 59.04 258 | 66.00 223 | 73.06 233 | 57.67 142 | 64.53 262 | 60.22 272 | 36.91 319 | 65.96 270 | 77.27 255 | 39.66 279 | 68.54 271 | 38.87 289 | 74.89 293 | 71.80 269 |
|
WR-MVS | | | 71.20 156 | 72.48 143 | 67.36 211 | 84.98 61 | 35.70 306 | 64.43 263 | 68.66 235 | 65.05 73 | 81.49 95 | 86.43 143 | 57.57 194 | 76.48 193 | 50.36 212 | 93.32 55 | 89.90 33 |
|
tpmrst | | | 50.15 311 | 51.38 308 | 46.45 326 | 56.05 352 | 24.77 357 | 64.40 264 | 49.98 327 | 36.14 321 | 53.32 336 | 69.59 325 | 35.16 296 | 48.69 328 | 39.24 287 | 58.51 351 | 65.89 315 |
|
VPA-MVSNet | | | 68.71 189 | 70.37 169 | 63.72 239 | 76.13 167 | 38.06 289 | 64.10 265 | 71.48 216 | 56.60 158 | 74.10 188 | 88.31 105 | 64.78 113 | 69.72 257 | 47.69 234 | 90.15 107 | 83.37 136 |
|
MIMVSNet1 | | | 66.57 205 | 69.23 178 | 58.59 284 | 81.26 109 | 37.73 292 | 64.06 266 | 57.62 282 | 57.02 151 | 78.40 133 | 90.75 47 | 62.65 122 | 58.10 315 | 41.77 277 | 89.58 117 | 79.95 202 |
|
IterMVS | | | 63.12 230 | 62.48 236 | 65.02 229 | 66.34 299 | 52.86 167 | 63.81 267 | 62.25 260 | 46.57 263 | 71.51 221 | 80.40 224 | 44.60 251 | 66.82 286 | 51.38 204 | 75.47 288 | 75.38 242 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DELS-MVS | | | 68.83 186 | 68.31 191 | 70.38 170 | 70.55 259 | 48.31 198 | 63.78 268 | 82.13 76 | 54.00 194 | 68.96 245 | 75.17 279 | 58.95 167 | 80.06 143 | 58.55 153 | 82.74 213 | 82.76 146 |
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 |
xiu_mvs_v2_base | | | 64.43 220 | 63.96 218 | 65.85 225 | 77.72 148 | 51.32 174 | 63.63 269 | 72.31 209 | 45.06 277 | 61.70 290 | 69.66 324 | 62.56 123 | 73.93 219 | 49.06 221 | 73.91 297 | 72.31 264 |
|
tfpnnormal | | | 66.48 206 | 67.93 196 | 62.16 258 | 73.40 220 | 36.65 296 | 63.45 270 | 64.99 251 | 55.97 160 | 72.82 202 | 87.80 112 | 57.06 202 | 69.10 261 | 48.31 229 | 87.54 143 | 80.72 188 |
|
TR-MVS | | | 64.59 216 | 63.54 223 | 67.73 208 | 75.75 173 | 50.83 176 | 63.39 271 | 70.29 229 | 49.33 243 | 71.55 220 | 74.55 285 | 50.94 228 | 78.46 165 | 40.43 284 | 75.69 285 | 73.89 251 |
|
PS-MVSNAJ | | | 64.27 223 | 63.73 221 | 65.90 224 | 77.82 146 | 51.42 173 | 63.33 272 | 72.33 208 | 45.09 276 | 61.60 291 | 68.04 335 | 62.39 127 | 73.95 218 | 49.07 220 | 73.87 298 | 72.34 263 |
|
tfpn200view9 | | | 60.35 256 | 59.97 252 | 61.51 262 | 70.78 253 | 35.35 308 | 63.27 273 | 57.47 283 | 53.00 206 | 68.31 250 | 77.09 256 | 32.45 307 | 72.09 238 | 35.61 312 | 81.73 226 | 77.08 232 |
|
thres400 | | | 60.77 253 | 59.97 252 | 63.15 244 | 70.78 253 | 35.35 308 | 63.27 273 | 57.47 283 | 53.00 206 | 68.31 250 | 77.09 256 | 32.45 307 | 72.09 238 | 35.61 312 | 81.73 226 | 82.02 160 |
|
0601test | | | 65.11 211 | 65.09 214 | 65.18 227 | 70.59 256 | 40.86 264 | 63.22 275 | 72.79 201 | 57.91 137 | 68.88 246 | 79.07 245 | 42.85 262 | 74.89 209 | 45.50 247 | 84.97 193 | 79.81 203 |
|
Anonymous20240521 | | | 65.11 211 | 65.09 214 | 65.18 227 | 70.59 256 | 40.86 264 | 63.22 275 | 72.79 201 | 57.91 137 | 68.88 246 | 79.07 245 | 42.85 262 | 74.89 209 | 45.50 247 | 84.97 193 | 79.81 203 |
|
tfpn1000 | | | 58.28 274 | 58.86 266 | 56.53 297 | 68.05 284 | 32.26 332 | 62.58 277 | 51.67 323 | 51.25 224 | 67.38 263 | 75.95 264 | 27.24 347 | 68.83 268 | 43.51 257 | 82.11 221 | 68.49 301 |
|
FPMVS | | | 59.43 262 | 60.07 251 | 57.51 290 | 77.62 152 | 71.52 45 | 62.33 278 | 50.92 324 | 57.40 145 | 69.40 241 | 80.00 227 | 39.14 281 | 61.92 305 | 37.47 303 | 66.36 331 | 39.09 360 |
|
DWT-MVSNet_test | | | 53.04 299 | 51.12 310 | 58.77 283 | 61.23 324 | 38.67 282 | 62.16 279 | 57.74 280 | 38.24 311 | 51.76 339 | 59.07 353 | 21.36 360 | 67.40 280 | 44.80 249 | 63.76 336 | 70.25 283 |
|
conf0.01 | | | 59.26 263 | 58.88 260 | 60.40 273 | 68.66 271 | 31.96 335 | 62.04 280 | 51.95 316 | 50.99 226 | 67.57 257 | 75.91 265 | 28.59 340 | 69.07 262 | 42.77 260 | 81.40 234 | 80.18 197 |
|
conf0.002 | | | 59.26 263 | 58.88 260 | 60.40 273 | 68.66 271 | 31.96 335 | 62.04 280 | 51.95 316 | 50.99 226 | 67.57 257 | 75.91 265 | 28.59 340 | 69.07 262 | 42.77 260 | 81.40 234 | 80.18 197 |
|
thresconf0.02 | | | 58.38 270 | 58.88 260 | 56.91 293 | 68.66 271 | 31.96 335 | 62.04 280 | 51.95 316 | 50.99 226 | 67.57 257 | 75.91 265 | 28.59 340 | 69.07 262 | 42.77 260 | 81.40 234 | 69.70 288 |
|
tfpn_n400 | | | 58.38 270 | 58.88 260 | 56.91 293 | 68.66 271 | 31.96 335 | 62.04 280 | 51.95 316 | 50.99 226 | 67.57 257 | 75.91 265 | 28.59 340 | 69.07 262 | 42.77 260 | 81.40 234 | 69.70 288 |
|
tfpnconf | | | 58.38 270 | 58.88 260 | 56.91 293 | 68.66 271 | 31.96 335 | 62.04 280 | 51.95 316 | 50.99 226 | 67.57 257 | 75.91 265 | 28.59 340 | 69.07 262 | 42.77 260 | 81.40 234 | 69.70 288 |
|
tfpnview11 | | | 58.38 270 | 58.88 260 | 56.91 293 | 68.66 271 | 31.96 335 | 62.04 280 | 51.95 316 | 50.99 226 | 67.57 257 | 75.91 265 | 28.59 340 | 69.07 262 | 42.77 260 | 81.40 234 | 69.70 288 |
|
PatchMatch-RL | | | 58.68 269 | 57.72 274 | 61.57 261 | 76.21 166 | 73.59 39 | 61.83 286 | 49.00 331 | 47.30 260 | 61.08 295 | 68.97 329 | 50.16 231 | 59.01 312 | 36.06 311 | 68.84 323 | 52.10 349 |
|
cascas | | | 64.59 216 | 62.77 234 | 70.05 178 | 75.27 175 | 50.02 181 | 61.79 287 | 71.61 212 | 42.46 292 | 63.68 283 | 68.89 331 | 49.33 234 | 80.35 134 | 47.82 233 | 84.05 200 | 79.78 205 |
|
tfpn_ndepth | | | 56.91 281 | 57.30 278 | 55.71 299 | 67.22 293 | 33.26 327 | 61.72 288 | 53.98 305 | 48.49 249 | 64.16 279 | 71.94 311 | 27.65 346 | 68.71 269 | 40.49 283 | 80.08 254 | 65.17 320 |
|
PatchFormer-LS_test | | | 53.94 297 | 52.64 302 | 57.85 287 | 61.87 321 | 39.59 276 | 61.60 289 | 57.63 281 | 40.65 302 | 54.52 332 | 58.64 354 | 29.07 339 | 64.18 295 | 46.78 241 | 62.98 339 | 69.78 286 |
|
LCM-MVSNet-Re | | | 69.10 181 | 71.57 159 | 61.70 260 | 70.37 260 | 34.30 317 | 61.45 290 | 79.62 130 | 56.81 154 | 89.59 10 | 88.16 109 | 68.44 79 | 72.94 224 | 42.30 271 | 87.33 151 | 77.85 226 |
|
1112_ss | | | 59.48 261 | 58.99 259 | 60.96 269 | 77.84 145 | 42.39 255 | 61.42 291 | 68.45 236 | 37.96 314 | 59.93 308 | 67.46 337 | 45.11 248 | 65.07 293 | 40.89 281 | 71.81 306 | 75.41 241 |
|
IB-MVS | | 49.67 18 | 59.69 260 | 56.96 280 | 67.90 205 | 68.19 282 | 50.30 179 | 61.42 291 | 65.18 250 | 47.57 258 | 55.83 325 | 67.15 340 | 23.77 358 | 79.60 147 | 43.56 256 | 79.97 256 | 73.79 252 |
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 |
PVSNet_Blended | | | 62.90 233 | 61.64 240 | 66.69 218 | 69.81 262 | 49.36 186 | 61.23 293 | 78.96 141 | 42.04 294 | 59.98 305 | 68.86 332 | 51.82 223 | 78.20 177 | 44.30 250 | 77.77 278 | 72.52 261 |
|
GA-MVS | | | 62.91 232 | 61.66 238 | 66.66 219 | 67.09 294 | 44.49 245 | 61.18 294 | 69.36 233 | 51.33 222 | 69.33 242 | 74.47 286 | 36.83 291 | 74.94 208 | 50.60 210 | 74.72 294 | 80.57 191 |
|
MS-PatchMatch | | | 55.59 288 | 54.89 294 | 57.68 288 | 69.18 269 | 49.05 189 | 61.00 295 | 62.93 259 | 35.98 322 | 58.36 313 | 68.93 330 | 36.71 292 | 66.59 288 | 37.62 302 | 63.30 337 | 57.39 342 |
|
MVSTER | | | 63.29 228 | 61.60 241 | 68.36 202 | 59.77 334 | 46.21 235 | 60.62 296 | 71.32 217 | 41.83 295 | 75.40 172 | 79.12 243 | 30.25 330 | 75.85 196 | 56.30 172 | 79.81 258 | 83.03 139 |
|
thisisatest0515 | | | 60.48 255 | 57.86 273 | 68.34 203 | 67.25 291 | 46.42 234 | 60.58 297 | 62.14 261 | 40.82 300 | 63.58 284 | 69.12 327 | 26.28 351 | 78.34 172 | 48.83 222 | 82.13 220 | 80.26 195 |
|
tpm | | | 50.60 309 | 52.42 305 | 45.14 331 | 65.18 306 | 26.29 353 | 60.30 298 | 43.50 346 | 37.41 316 | 57.01 317 | 79.09 244 | 30.20 332 | 42.32 350 | 32.77 325 | 66.36 331 | 66.81 312 |
|
VPNet | | | 65.58 208 | 67.56 199 | 59.65 279 | 79.72 118 | 30.17 343 | 60.27 299 | 62.14 261 | 54.19 191 | 71.24 223 | 86.63 136 | 58.80 168 | 67.62 278 | 44.17 253 | 90.87 97 | 81.18 175 |
|
MIMVSNet | | | 54.39 292 | 56.12 287 | 49.20 316 | 72.57 241 | 30.91 342 | 59.98 300 | 48.43 333 | 41.66 296 | 55.94 324 | 83.86 183 | 41.19 271 | 50.42 324 | 26.05 344 | 75.38 290 | 66.27 314 |
|
HyFIR lowres test | | | 63.01 231 | 60.47 249 | 70.61 167 | 83.04 86 | 54.10 159 | 59.93 301 | 72.24 210 | 33.67 337 | 69.00 244 | 75.63 272 | 38.69 283 | 76.93 187 | 36.60 307 | 75.45 289 | 80.81 186 |
|
Patchmatch-RL test | | | 59.95 258 | 59.12 257 | 62.44 255 | 72.46 242 | 54.61 156 | 59.63 302 | 47.51 336 | 41.05 299 | 74.58 183 | 74.30 289 | 31.06 324 | 65.31 291 | 51.61 201 | 79.85 257 | 67.39 306 |
|
PatchT | | | 53.35 298 | 56.47 284 | 43.99 336 | 64.19 311 | 17.46 363 | 59.15 303 | 43.10 347 | 52.11 213 | 54.74 330 | 86.95 120 | 29.97 333 | 49.98 327 | 43.62 255 | 74.40 295 | 64.53 325 |
|
JIA-IIPM | | | 54.03 294 | 51.62 307 | 61.25 266 | 59.14 338 | 55.21 150 | 59.10 304 | 47.72 335 | 50.85 233 | 50.31 345 | 85.81 159 | 20.10 363 | 63.97 296 | 36.16 310 | 55.41 357 | 64.55 324 |
|
Anonymous202405211 | | | 66.02 207 | 66.89 206 | 63.43 243 | 74.22 201 | 38.14 287 | 59.00 305 | 66.13 244 | 63.33 95 | 69.76 240 | 85.95 158 | 51.88 222 | 70.50 253 | 44.23 252 | 87.52 144 | 81.64 170 |
|
MDTV_nov1_ep13 | | | | 54.05 298 | | 65.54 304 | 29.30 345 | 59.00 305 | 55.22 296 | 35.96 323 | 52.44 337 | 75.98 263 | 30.77 327 | 59.62 310 | 38.21 296 | 73.33 300 | |
|
thres200 | | | 57.55 279 | 57.02 279 | 59.17 280 | 67.89 287 | 34.93 313 | 58.91 307 | 57.25 287 | 50.24 236 | 64.01 280 | 71.46 316 | 32.49 306 | 71.39 247 | 31.31 328 | 79.57 262 | 71.19 276 |
|
testpf | | | 45.32 323 | 48.47 317 | 35.88 349 | 53.56 364 | 26.84 351 | 58.86 308 | 42.95 348 | 47.78 256 | 46.18 352 | 63.70 344 | 13.73 369 | 50.29 325 | 50.81 207 | 58.61 350 | 30.51 363 |
|
ANet_high | | | 67.08 202 | 69.94 172 | 58.51 285 | 57.55 347 | 27.09 350 | 58.43 309 | 76.80 174 | 63.56 89 | 82.40 82 | 91.93 20 | 59.82 155 | 64.98 294 | 50.10 214 | 88.86 126 | 83.46 132 |
|
ppachtmachnet_test | | | 60.26 257 | 59.61 255 | 62.20 257 | 67.70 288 | 44.33 246 | 58.18 310 | 60.96 270 | 40.75 301 | 65.80 272 | 72.57 302 | 41.23 270 | 63.92 297 | 46.87 240 | 82.42 216 | 78.33 217 |
|
Test_1112_low_res | | | 58.78 268 | 58.69 268 | 59.04 282 | 79.41 122 | 38.13 288 | 57.62 311 | 66.98 241 | 34.74 329 | 59.62 309 | 77.56 253 | 42.92 261 | 63.65 298 | 38.66 292 | 70.73 312 | 75.35 243 |
|
VNet | | | 64.01 226 | 65.15 212 | 60.57 271 | 73.28 223 | 35.61 307 | 57.60 312 | 67.08 240 | 54.61 185 | 66.76 267 | 83.37 188 | 56.28 206 | 66.87 283 | 42.19 272 | 85.20 187 | 79.23 210 |
|
DSMNet-mixed | | | 43.18 332 | 44.66 332 | 38.75 346 | 54.75 360 | 28.88 347 | 57.06 313 | 27.42 369 | 13.47 364 | 47.27 351 | 77.67 252 | 38.83 282 | 39.29 357 | 25.32 349 | 60.12 344 | 48.08 352 |
|
FMVSNet5 | | | 55.08 290 | 55.54 291 | 53.71 304 | 65.80 302 | 33.50 326 | 56.22 314 | 52.50 314 | 43.72 286 | 61.06 296 | 83.38 187 | 25.46 355 | 54.87 318 | 30.11 334 | 81.64 232 | 72.75 259 |
|
MVS-HIRNet | | | 45.53 322 | 47.29 322 | 40.24 344 | 62.29 320 | 26.82 352 | 56.02 315 | 37.41 363 | 29.74 354 | 43.69 361 | 81.27 215 | 33.96 299 | 55.48 317 | 24.46 350 | 56.79 353 | 38.43 361 |
|
pmmvs3 | | | 46.71 320 | 45.09 329 | 51.55 310 | 56.76 350 | 48.25 199 | 55.78 316 | 39.53 362 | 24.13 362 | 50.35 344 | 63.40 345 | 15.90 368 | 51.08 323 | 29.29 339 | 70.69 313 | 55.33 345 |
|
LP | | | 53.02 300 | 52.27 306 | 55.27 301 | 55.76 356 | 40.55 269 | 55.64 317 | 55.07 297 | 42.46 292 | 56.95 318 | 73.21 299 | 33.67 300 | 54.18 322 | 38.41 295 | 59.29 347 | 71.08 277 |
|
pmmvs5 | | | 52.49 305 | 52.58 304 | 52.21 309 | 54.99 359 | 32.38 331 | 55.45 318 | 53.84 307 | 32.15 344 | 55.49 327 | 74.81 280 | 38.08 287 | 57.37 316 | 34.02 320 | 74.40 295 | 66.88 310 |
|
our_test_3 | | | 56.46 282 | 56.51 283 | 56.30 298 | 67.70 288 | 39.66 275 | 55.36 319 | 52.34 315 | 40.57 304 | 63.85 281 | 69.91 323 | 40.04 278 | 58.22 314 | 43.49 258 | 75.29 292 | 71.03 279 |
|
EPMVS | | | 45.74 321 | 46.53 324 | 43.39 337 | 54.14 363 | 22.33 360 | 55.02 320 | 35.00 365 | 34.69 330 | 51.09 340 | 70.20 322 | 25.92 353 | 42.04 352 | 37.19 304 | 55.50 356 | 65.78 316 |
|
dp | | | 44.09 331 | 44.88 331 | 41.72 342 | 58.53 342 | 23.18 359 | 54.70 321 | 42.38 352 | 34.80 328 | 44.25 359 | 65.61 342 | 24.48 357 | 44.80 340 | 29.77 336 | 49.42 360 | 57.18 343 |
|
CHOSEN 1792x2688 | | | 58.09 275 | 56.30 285 | 63.45 242 | 79.95 116 | 50.93 175 | 54.07 322 | 65.59 247 | 28.56 355 | 61.53 292 | 74.33 288 | 41.09 272 | 66.52 289 | 33.91 321 | 67.69 329 | 72.92 257 |
|
MDTV_nov1_ep13_2view | | | | | | | 18.41 362 | 53.74 323 | | 31.57 349 | 44.89 355 | | 29.90 334 | | 32.93 324 | | 71.48 271 |
|
test-LLR | | | 50.43 310 | 50.69 313 | 49.64 314 | 60.76 327 | 41.87 257 | 53.18 324 | 45.48 344 | 43.41 289 | 49.41 346 | 60.47 351 | 29.22 337 | 44.73 341 | 42.09 273 | 72.14 304 | 62.33 332 |
|
TESTMET0.1,1 | | | 45.17 324 | 44.93 330 | 45.89 328 | 56.02 353 | 38.31 284 | 53.18 324 | 41.94 355 | 27.85 356 | 44.86 356 | 56.47 356 | 17.93 365 | 41.50 355 | 38.08 298 | 68.06 326 | 57.85 341 |
|
test-mter | | | 48.56 315 | 48.20 320 | 49.64 314 | 60.76 327 | 41.87 257 | 53.18 324 | 45.48 344 | 31.91 348 | 49.41 346 | 60.47 351 | 18.34 364 | 44.73 341 | 42.09 273 | 72.14 304 | 62.33 332 |
|
Anonymous20231206 | | | 54.13 293 | 55.82 288 | 49.04 319 | 70.89 251 | 35.96 303 | 51.73 327 | 50.87 325 | 34.86 327 | 62.49 288 | 79.22 240 | 42.52 265 | 44.29 344 | 27.95 342 | 81.88 225 | 66.88 310 |
|
XXY-MVS | | | 55.19 289 | 57.40 277 | 48.56 321 | 64.45 310 | 34.84 315 | 51.54 328 | 53.59 308 | 38.99 308 | 63.79 282 | 79.43 236 | 56.59 204 | 45.57 335 | 36.92 306 | 71.29 308 | 65.25 319 |
|
test20.03 | | | 55.74 287 | 57.51 276 | 50.42 311 | 59.89 333 | 32.09 333 | 50.63 329 | 49.01 330 | 50.11 238 | 65.07 276 | 83.23 193 | 45.61 246 | 48.11 330 | 30.22 333 | 83.82 204 | 71.07 278 |
|
testmv | | | 52.91 301 | 54.31 296 | 48.71 320 | 72.13 247 | 36.18 300 | 50.26 330 | 47.78 334 | 44.15 281 | 64.61 277 | 79.78 232 | 38.18 285 | 50.20 326 | 21.96 355 | 69.93 317 | 59.75 339 |
|
UnsupCasMVSNet_eth | | | 52.26 306 | 53.29 301 | 49.16 317 | 55.08 358 | 33.67 321 | 50.03 331 | 58.79 278 | 37.67 315 | 63.43 287 | 74.75 282 | 41.82 268 | 45.83 334 | 38.59 294 | 59.42 346 | 67.98 304 |
|
testgi | | | 54.00 296 | 56.86 281 | 45.45 329 | 58.20 344 | 25.81 355 | 49.05 332 | 49.50 329 | 45.43 272 | 67.84 253 | 81.17 217 | 51.81 225 | 43.20 348 | 29.30 338 | 79.41 263 | 67.34 308 |
|
Patchmatch-test | | | 47.93 317 | 49.96 315 | 41.84 340 | 57.42 348 | 24.26 358 | 48.75 333 | 41.49 356 | 39.30 306 | 56.79 320 | 73.48 296 | 30.48 329 | 33.87 362 | 29.29 339 | 72.61 302 | 67.39 306 |
|
UnsupCasMVSNet_bld | | | 50.01 312 | 51.03 312 | 46.95 322 | 58.61 341 | 32.64 330 | 48.31 334 | 53.27 311 | 34.27 332 | 60.47 301 | 71.53 315 | 41.40 269 | 47.07 332 | 30.68 330 | 60.78 342 | 61.13 334 |
|
PVSNet | | 43.83 21 | 51.56 308 | 51.17 309 | 52.73 306 | 68.34 280 | 38.27 285 | 48.22 335 | 53.56 309 | 36.41 320 | 54.29 333 | 64.94 343 | 34.60 297 | 54.20 321 | 30.34 332 | 69.87 318 | 65.71 317 |
|
MDA-MVSNet-bldmvs | | | 62.34 240 | 61.73 237 | 64.16 233 | 61.64 323 | 49.90 182 | 48.11 336 | 57.24 288 | 53.31 204 | 80.95 104 | 79.39 237 | 49.00 235 | 61.55 306 | 45.92 245 | 80.05 255 | 81.03 179 |
|
PMMVS | | | 44.69 328 | 43.95 334 | 46.92 323 | 50.05 366 | 53.47 164 | 48.08 337 | 42.40 351 | 22.36 363 | 44.01 360 | 53.05 358 | 42.60 264 | 45.49 336 | 31.69 327 | 61.36 341 | 41.79 358 |
|
ADS-MVSNet2 | | | 48.76 314 | 47.25 323 | 53.29 305 | 55.90 354 | 40.54 270 | 47.34 338 | 54.99 303 | 31.41 350 | 50.48 342 | 72.06 309 | 31.23 321 | 54.26 320 | 25.93 345 | 55.93 354 | 65.07 321 |
|
ADS-MVSNet | | | 44.62 329 | 45.58 327 | 41.73 341 | 55.90 354 | 20.83 361 | 47.34 338 | 39.94 361 | 31.41 350 | 50.48 342 | 72.06 309 | 31.23 321 | 39.31 356 | 25.93 345 | 55.93 354 | 65.07 321 |
|
WTY-MVS | | | 49.39 313 | 50.31 314 | 46.62 325 | 61.22 325 | 32.00 334 | 46.61 340 | 49.77 328 | 33.87 334 | 54.12 334 | 69.55 326 | 41.96 267 | 45.40 337 | 31.28 329 | 64.42 335 | 62.47 331 |
|
test0.0.03 1 | | | 47.72 318 | 48.31 319 | 45.93 327 | 55.53 357 | 29.39 344 | 46.40 341 | 41.21 358 | 43.41 289 | 55.81 326 | 67.65 336 | 29.22 337 | 43.77 347 | 25.73 347 | 69.87 318 | 64.62 323 |
|
test123 | | | 4.43 348 | 5.78 349 | 0.39 358 | 0.97 372 | 0.28 372 | 46.33 342 | 0.45 374 | 0.31 367 | 0.62 369 | 1.50 369 | 0.61 375 | 0.11 370 | 0.56 366 | 0.63 366 | 0.77 368 |
|
test1235678 | | | 48.41 316 | 49.60 316 | 44.83 333 | 68.52 277 | 33.81 320 | 46.33 342 | 45.89 341 | 38.72 310 | 58.46 311 | 72.08 304 | 29.85 335 | 47.82 331 | 19.67 359 | 66.91 330 | 52.88 347 |
|
sss | | | 47.59 319 | 48.32 318 | 45.40 330 | 56.73 351 | 33.96 318 | 45.17 344 | 48.51 332 | 32.11 346 | 52.37 338 | 65.79 341 | 40.39 277 | 41.91 353 | 31.85 326 | 61.97 340 | 60.35 335 |
|
1111 | | | 45.08 326 | 47.96 321 | 36.43 348 | 59.56 336 | 14.82 365 | 43.56 345 | 45.65 342 | 45.60 268 | 60.04 303 | 75.47 276 | 9.31 371 | 34.46 360 | 23.66 351 | 68.76 324 | 60.02 337 |
|
.test1245 | | | 34.47 343 | 40.38 339 | 16.73 354 | 59.56 336 | 14.82 365 | 43.56 345 | 45.65 342 | 45.60 268 | 60.04 303 | 75.47 276 | 9.31 371 | 34.46 360 | 23.66 351 | 0.55 367 | 0.90 366 |
|
testmvs | | | 4.06 349 | 5.28 350 | 0.41 357 | 0.64 373 | 0.16 373 | 42.54 347 | 0.31 375 | 0.26 368 | 0.50 370 | 1.40 370 | 0.77 374 | 0.17 369 | 0.56 366 | 0.55 367 | 0.90 366 |
|
testus | | | 45.03 327 | 46.49 325 | 40.65 343 | 62.53 318 | 25.24 356 | 42.54 347 | 46.23 340 | 31.16 352 | 57.69 315 | 62.90 346 | 34.60 297 | 42.33 349 | 17.72 361 | 63.01 338 | 54.37 346 |
|
PVSNet_0 | | 36.71 22 | 41.12 334 | 40.78 337 | 42.14 338 | 59.97 331 | 40.13 272 | 40.97 349 | 42.24 354 | 30.81 353 | 44.86 356 | 49.41 362 | 40.70 275 | 45.12 339 | 23.15 353 | 34.96 362 | 41.16 359 |
|
YYNet1 | | | 52.58 303 | 53.50 299 | 49.85 312 | 54.15 362 | 36.45 299 | 40.53 350 | 46.55 339 | 38.09 313 | 75.52 170 | 73.31 298 | 41.08 273 | 43.88 345 | 41.10 279 | 71.14 310 | 69.21 297 |
|
MDA-MVSNet_test_wron | | | 52.57 304 | 53.49 300 | 49.81 313 | 54.24 361 | 36.47 298 | 40.48 351 | 46.58 338 | 38.13 312 | 75.47 171 | 73.32 297 | 41.05 274 | 43.85 346 | 40.98 280 | 71.20 309 | 69.10 299 |
|
PNet_i23d | | | 36.76 339 | 36.63 343 | 37.12 347 | 58.19 345 | 33.00 329 | 39.86 352 | 32.55 366 | 48.44 250 | 39.64 362 | 51.31 360 | 6.89 373 | 41.83 354 | 22.29 354 | 30.55 363 | 36.54 362 |
|
test2356 | | | 40.85 335 | 40.47 338 | 41.98 339 | 58.78 340 | 28.65 348 | 39.45 353 | 40.98 360 | 31.95 347 | 48.47 348 | 56.63 355 | 12.54 370 | 44.41 343 | 15.84 363 | 59.58 345 | 52.88 347 |
|
new_pmnet | | | 37.55 338 | 39.80 341 | 30.79 351 | 56.83 349 | 16.46 364 | 39.35 354 | 30.65 367 | 25.59 360 | 45.26 354 | 61.60 349 | 24.54 356 | 28.02 365 | 21.60 356 | 52.80 359 | 47.90 353 |
|
no-one | | | 56.11 284 | 55.62 290 | 57.60 289 | 62.68 317 | 49.23 188 | 39.12 355 | 58.99 277 | 33.72 335 | 60.98 299 | 80.90 218 | 36.07 294 | 60.36 308 | 30.68 330 | 97.40 1 | 63.22 327 |
|
E-PMN | | | 45.17 324 | 45.36 328 | 44.60 334 | 50.07 365 | 42.75 252 | 38.66 356 | 42.29 353 | 46.39 264 | 39.55 363 | 51.15 361 | 26.00 352 | 45.37 338 | 37.68 300 | 76.41 280 | 45.69 356 |
|
EMVS | | | 44.61 330 | 44.45 333 | 45.10 332 | 48.91 367 | 43.00 250 | 37.92 357 | 41.10 359 | 46.75 261 | 38.00 365 | 48.43 363 | 26.42 350 | 46.27 333 | 37.11 305 | 75.38 290 | 46.03 355 |
|
N_pmnet | | | 52.06 307 | 51.11 311 | 54.92 302 | 59.64 335 | 71.03 50 | 37.42 358 | 61.62 268 | 33.68 336 | 57.12 316 | 72.10 303 | 37.94 288 | 31.03 363 | 29.13 341 | 71.35 307 | 62.70 329 |
|
new-patchmatchnet | | | 52.89 302 | 55.76 289 | 44.26 335 | 59.94 332 | 6.31 369 | 37.36 359 | 50.76 326 | 41.10 297 | 64.28 278 | 79.82 231 | 44.77 249 | 48.43 329 | 36.24 309 | 87.61 142 | 78.03 223 |
|
test12356 | | | 38.35 336 | 40.80 336 | 31.01 350 | 58.31 343 | 9.09 368 | 36.67 360 | 46.65 337 | 33.65 338 | 44.39 358 | 60.94 350 | 17.56 366 | 39.23 358 | 16.01 362 | 53.03 358 | 44.72 357 |
|
CHOSEN 280x420 | | | 41.62 333 | 39.89 340 | 46.80 324 | 61.81 322 | 51.59 171 | 33.56 361 | 35.74 364 | 27.48 357 | 37.64 366 | 53.53 357 | 23.24 359 | 42.09 351 | 27.39 343 | 58.64 349 | 46.72 354 |
|
PMMVS2 | | | 37.74 337 | 40.87 335 | 28.36 353 | 42.41 369 | 5.35 370 | 24.61 362 | 27.75 368 | 32.15 344 | 47.85 349 | 70.27 321 | 35.85 295 | 29.51 364 | 19.08 360 | 67.85 327 | 50.22 351 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 27.91 23 | 36.69 340 | 35.64 344 | 39.84 345 | 43.37 368 | 35.85 305 | 19.49 363 | 24.61 370 | 24.68 361 | 39.05 364 | 62.63 348 | 38.67 284 | 27.10 366 | 21.04 357 | 47.25 361 | 56.56 344 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | 11.98 345 | 14.73 346 | 3.72 356 | 2.28 371 | 4.62 371 | 19.44 364 | 14.50 372 | 0.47 366 | 21.55 367 | 9.58 366 | 25.78 354 | 4.57 368 | 11.61 364 | 27.37 364 | 1.96 365 |
|
test_part1 | | | | | 0.00 359 | | 0.00 374 | 0.00 365 | 84.94 31 | | | | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
v1.0 | | | 34.83 342 | 46.44 326 | 0.00 359 | 85.90 47 | 0.00 374 | 0.00 365 | 84.94 31 | 73.27 20 | 84.61 61 | 89.25 84 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
cdsmvs_eth3d_5k | | | 17.71 344 | 23.62 345 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 70.17 230 | 0.00 369 | 0.00 371 | 74.25 290 | 68.16 82 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
pcd_1.5k_mvsjas | | | 5.20 347 | 6.93 348 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 62.39 127 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
pcd1.5k->3k | | | 35.00 341 | 36.93 342 | 29.21 352 | 84.62 68 | 0.00 374 | 0.00 365 | 78.90 143 | 0.00 369 | 0.00 371 | 0.00 371 | 78.26 14 | 0.00 371 | 0.00 368 | 90.55 101 | 87.62 66 |
|
sosnet-low-res | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
sosnet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
uncertanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
Regformer | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
ab-mvs-re | | | 5.62 346 | 7.50 347 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 67.46 337 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
uanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 374 | 0.00 365 | 0.00 376 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 376 | 0.00 371 | 0.00 368 | 0.00 369 | 0.00 369 |
|
GSMVS | | | | | | | | | | | | | | | | | 70.05 284 |
|
test_part2 | | | | | | 85.90 47 | 66.44 82 | | | | 84.61 61 | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 31.41 319 | | | | 70.05 284 |
|
sam_mvs | | | | | | | | | | | | | 31.21 323 | | | | |
|
semantic-postprocess | | | | | 72.49 147 | 73.34 221 | 58.20 141 | | 65.55 248 | 48.10 251 | 76.91 150 | 82.64 198 | 42.25 266 | 78.84 155 | 61.20 139 | 77.89 277 | 80.44 193 |
|
MTGPA | ![Method available as binary. binary](img/icon_binary.png) | | | | | | | | 80.63 111 | | | | | | | | |
|
test_post | | | | | | | | | | | | 1.99 368 | 30.91 326 | 54.76 319 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 68.99 328 | 31.32 320 | 69.38 259 | | | |
|
gm-plane-assit | | | | | | 62.51 319 | 33.91 319 | | | 37.25 317 | | 62.71 347 | | 72.74 230 | 38.70 291 | | |
|
test9_res | | | | | | | | | | | | | | | 72.12 54 | 91.37 80 | 77.40 228 |
|
agg_prior2 | | | | | | | | | | | | | | | 70.70 62 | 90.93 92 | 78.55 216 |
|
agg_prior | | | | | | 84.44 73 | 66.02 85 | | 78.62 149 | | 76.95 148 | | | 80.34 135 | | | |
|
TestCases | | | | | 78.35 61 | 79.19 128 | 70.81 52 | | 88.64 2 | 65.37 67 | 80.09 115 | 88.17 107 | 70.33 64 | 78.43 167 | 55.60 177 | 90.90 94 | 85.81 86 |
|
test_prior | | | | | 75.27 92 | 82.15 98 | 59.85 127 | | 84.33 41 | | | | | 83.39 67 | | | 82.58 150 |
|
新几何1 | | | | | 69.99 179 | 88.37 32 | 71.34 48 | | 62.08 263 | 43.85 282 | 74.99 175 | 86.11 153 | 52.85 218 | 70.57 252 | 50.99 206 | 83.23 210 | 68.05 303 |
|
旧先验1 | | | | | | 84.55 70 | 60.36 123 | | 63.69 255 | | | 87.05 119 | 54.65 212 | | | 83.34 208 | 69.66 292 |
|
原ACMM1 | | | | | 73.90 105 | 85.90 47 | 65.15 93 | | 81.67 84 | 50.97 232 | 74.25 186 | 86.16 151 | 61.60 135 | 83.54 63 | 56.75 165 | 91.08 88 | 73.00 256 |
|
testdata2 | | | | | | | | | | | | | | 67.30 281 | 48.34 228 | | |
|
segment_acmp | | | | | | | | | | | | | 68.30 81 | | | | |
|
testdata | | | | | 64.13 234 | 85.87 50 | 63.34 105 | | 61.80 267 | 47.83 255 | 76.42 163 | 86.60 138 | 48.83 236 | 62.31 304 | 54.46 189 | 81.26 241 | 66.74 313 |
|
test12 | | | | | 76.51 74 | 82.28 96 | 60.94 120 | | 81.64 85 | | 73.60 192 | | 64.88 110 | 85.19 41 | | 90.42 103 | 83.38 134 |
|
plane_prior7 | | | | | | 85.18 57 | 66.21 84 | | | | | | | | | | |
|
plane_prior6 | | | | | | 84.18 77 | 65.31 90 | | | | | | 60.83 147 | | | | |
|
plane_prior5 | | | | | | | | | 85.49 21 | | | | | 86.15 22 | 71.09 56 | 90.94 90 | 84.82 99 |
|
plane_prior4 | | | | | | | | | | | | 89.11 89 | | | | | |
|
plane_prior3 | | | | | | | 65.67 87 | | | 63.82 87 | 78.23 136 | | | | | | |
|
plane_prior1 | | | | | | 84.46 72 | | | | | | | | | | | |
|
n2 | | | | | | | | | 0.00 376 | | | | | | | | |
|
nn | | | | | | | | | 0.00 376 | | | | | | | | |
|
door-mid | | | | | | | | | 55.02 298 | | | | | | | | |
|
lessismore_v0 | | | | | 72.75 141 | 79.60 120 | 56.83 145 | | 57.37 285 | | 83.80 71 | 89.01 92 | 47.45 242 | 78.74 158 | 64.39 122 | 86.49 164 | 82.69 148 |
|
LGP-MVS_train | | | | | 80.90 33 | 87.00 38 | 70.41 57 | | 86.35 12 | 69.77 39 | 87.75 18 | 91.13 36 | 81.83 3 | 86.20 19 | 77.13 28 | 95.96 7 | 86.08 80 |
|
test11 | | | | | | | | | 82.71 70 | | | | | | | | |
|
door | | | | | | | | | 52.91 313 | | | | | | | | |
|
HQP5-MVS | | | | | | | 58.80 136 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 67.38 97 | | |
|
HQP4-MVS | | | | | | | | | | | 71.59 215 | | | 85.31 35 | | | 83.74 125 |
|
HQP3-MVS | | | | | | | | | 84.12 48 | | | | | | | 89.16 121 | |
|
HQP2-MVS | | | | | | | | | | | | | 58.09 181 | | | | |
|
NP-MVS | | | | | | 83.34 84 | 63.07 108 | | | | | 85.97 156 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 89.47 119 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 91.96 70 | |
|
Test By Simon | | | | | | | | | | | | | 62.56 123 | | | | |
|
ITE_SJBPF | | | | | 80.35 39 | 76.94 158 | 73.60 38 | | 80.48 115 | 66.87 52 | 83.64 73 | 86.18 149 | 70.25 66 | 79.90 144 | 61.12 140 | 88.95 125 | 87.56 68 |
|
DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | 11.83 355 | 15.51 370 | 13.86 367 | | 11.25 373 | 5.76 365 | 20.85 368 | 26.46 364 | 17.06 367 | 9.22 367 | 9.69 365 | 13.82 365 | 12.42 364 |
|