HPM-MVS++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 79.88 5 | 80.14 5 | 79.10 13 | 88.17 1 | 64.80 1 | 86.59 4 | 83.70 44 | 65.37 16 | 78.78 11 | 90.64 10 | 58.63 13 | 87.24 34 | 79.00 5 | 90.37 3 | 85.26 104 |
|
CNVR-MVS | | | 79.84 6 | 79.97 6 | 79.45 5 | 87.90 2 | 62.17 20 | 84.37 24 | 85.03 17 | 66.96 6 | 77.58 14 | 90.06 23 | 59.47 9 | 89.13 10 | 78.67 7 | 89.73 4 | 87.03 38 |
|
test_part2 | | | | | | 87.58 3 | 60.47 40 | | | | 83.42 2 | | | | | | |
|
v1.0 | | | 34.38 334 | 45.84 314 | 0.00 359 | 87.58 3 | 0.00 373 | 0.00 365 | 86.64 3 | 63.49 36 | 83.42 2 | 91.40 5 | 0.00 375 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
NCCC | | | 78.58 10 | 78.31 11 | 79.39 6 | 87.51 5 | 62.61 16 | 85.20 21 | 84.42 25 | 66.73 10 | 74.67 33 | 89.38 34 | 55.30 27 | 89.18 9 | 74.19 23 | 87.34 30 | 86.38 51 |
|
ESAPD | | | 80.56 2 | 80.98 2 | 79.29 7 | 87.27 6 | 60.56 39 | 85.71 17 | 86.42 6 | 63.28 37 | 83.27 4 | 91.83 3 | 64.96 1 | 90.47 1 | 76.41 13 | 89.67 6 | 86.84 42 |
|
region2R | | | 77.67 21 | 77.18 23 | 79.15 9 | 86.76 7 | 62.95 8 | 86.29 6 | 84.16 31 | 62.81 49 | 73.30 51 | 90.58 12 | 49.90 76 | 88.21 21 | 73.78 26 | 87.03 33 | 86.29 61 |
|
ACMMPR | | | 77.71 19 | 77.23 22 | 79.16 8 | 86.75 8 | 62.93 9 | 86.29 6 | 84.24 29 | 62.82 47 | 73.55 48 | 90.56 13 | 49.80 78 | 88.24 20 | 74.02 24 | 87.03 33 | 86.32 59 |
|
HFP-MVS | | | 78.01 17 | 77.65 17 | 79.10 13 | 86.71 9 | 62.81 10 | 86.29 6 | 84.32 27 | 62.82 47 | 73.96 38 | 90.50 15 | 53.20 49 | 88.35 17 | 74.02 24 | 87.05 31 | 86.13 63 |
|
#test# | | | 77.83 18 | 77.41 20 | 79.10 13 | 86.71 9 | 62.81 10 | 85.69 18 | 84.32 27 | 61.61 66 | 73.96 38 | 90.50 15 | 53.20 49 | 88.35 17 | 73.68 27 | 87.05 31 | 86.13 63 |
|
MCST-MVS | | | 77.48 23 | 77.45 19 | 77.54 37 | 86.67 11 | 58.36 63 | 83.22 39 | 86.93 1 | 56.91 151 | 74.91 29 | 88.19 48 | 59.15 11 | 87.68 30 | 73.67 28 | 87.45 29 | 86.57 48 |
|
APDe-MVS | | | 80.16 4 | 80.59 3 | 78.86 21 | 86.64 12 | 60.02 43 | 88.12 1 | 86.42 6 | 62.94 43 | 82.40 5 | 92.12 2 | 59.64 7 | 89.76 4 | 78.70 6 | 88.32 18 | 86.79 44 |
|
SMA-MVS | | | 80.28 3 | 80.39 4 | 79.95 2 | 86.60 13 | 61.95 22 | 86.33 5 | 85.75 11 | 62.49 52 | 82.20 6 | 92.28 1 | 56.53 16 | 89.70 5 | 79.85 3 | 91.48 1 | 88.19 8 |
|
DP-MVS Recon | | | 72.15 78 | 70.73 86 | 76.40 54 | 86.57 14 | 57.99 67 | 81.15 77 | 82.96 63 | 57.03 148 | 66.78 147 | 85.56 91 | 44.50 154 | 88.11 23 | 51.77 184 | 80.23 88 | 83.10 173 |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 78.35 13 | 78.26 13 | 78.64 24 | 86.54 15 | 63.47 5 | 86.02 11 | 83.55 48 | 63.89 31 | 73.60 47 | 90.60 11 | 54.85 32 | 86.72 47 | 77.20 11 | 88.06 23 | 85.74 77 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
mPP-MVS | | | 76.54 33 | 75.93 35 | 78.34 29 | 86.47 16 | 63.50 4 | 85.74 16 | 82.28 72 | 62.90 44 | 71.77 68 | 90.26 20 | 46.61 133 | 86.55 55 | 71.71 38 | 85.66 46 | 84.97 113 |
|
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 78.02 16 | 78.04 15 | 77.98 34 | 86.44 17 | 60.81 36 | 85.52 19 | 84.36 26 | 60.61 78 | 79.05 10 | 90.30 19 | 55.54 26 | 88.32 19 | 73.48 31 | 87.03 33 | 84.83 116 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
XVS | | | 77.17 26 | 76.56 29 | 79.00 16 | 86.32 18 | 62.62 14 | 85.83 13 | 83.92 36 | 64.55 22 | 72.17 64 | 90.01 26 | 47.95 115 | 88.01 25 | 71.55 39 | 86.74 38 | 86.37 54 |
|
X-MVStestdata | | | 70.21 111 | 67.28 151 | 79.00 16 | 86.32 18 | 62.62 14 | 85.83 13 | 83.92 36 | 64.55 22 | 72.17 64 | 6.49 365 | 47.95 115 | 88.01 25 | 71.55 39 | 86.74 38 | 86.37 54 |
|
HSP-MVS | | | 80.69 1 | 81.20 1 | 79.14 10 | 86.21 20 | 62.73 12 | 86.09 9 | 85.03 17 | 65.51 15 | 83.81 1 | 90.51 14 | 63.71 2 | 89.23 8 | 81.51 1 | 88.44 13 | 85.45 92 |
|
114514_t | | | 70.83 91 | 69.56 102 | 74.64 81 | 86.21 20 | 54.63 116 | 82.34 57 | 81.81 81 | 48.22 264 | 63.01 192 | 85.83 86 | 40.92 194 | 87.10 40 | 57.91 141 | 79.79 93 | 82.18 187 |
|
DeepC-MVS_fast | | 68.24 3 | 77.25 25 | 76.63 28 | 79.12 12 | 86.15 22 | 60.86 35 | 84.71 22 | 84.85 21 | 61.98 63 | 73.06 55 | 88.88 42 | 53.72 43 | 89.06 11 | 68.27 53 | 88.04 24 | 87.42 28 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PGM-MVS | | | 76.77 31 | 76.06 32 | 78.88 20 | 86.14 23 | 62.73 12 | 82.55 52 | 83.74 43 | 61.71 64 | 72.45 63 | 90.34 18 | 48.48 110 | 88.13 22 | 72.32 34 | 86.85 36 | 85.78 72 |
|
zzz-MVS | | | 77.61 22 | 77.36 21 | 78.35 27 | 86.08 24 | 63.57 2 | 83.37 37 | 80.97 105 | 65.13 18 | 75.77 21 | 90.88 8 | 48.63 106 | 86.66 48 | 77.23 9 | 88.17 20 | 84.81 117 |
|
MTAPA | | | 76.90 29 | 76.42 30 | 78.35 27 | 86.08 24 | 63.57 2 | 74.92 194 | 80.97 105 | 65.13 18 | 75.77 21 | 90.88 8 | 48.63 106 | 86.66 48 | 77.23 9 | 88.17 20 | 84.81 117 |
|
GST-MVS | | | 78.14 15 | 77.85 16 | 78.99 18 | 86.05 26 | 61.82 25 | 85.84 12 | 85.21 14 | 63.56 35 | 74.29 36 | 90.03 24 | 52.56 53 | 88.53 15 | 74.79 20 | 88.34 15 | 86.63 47 |
|
CP-MVS | | | 77.12 27 | 76.68 27 | 78.43 26 | 86.05 26 | 63.18 7 | 87.55 3 | 83.45 51 | 62.44 54 | 72.68 59 | 90.50 15 | 48.18 113 | 87.34 33 | 73.59 29 | 85.71 45 | 84.76 121 |
|
agg_prior3 | | | 76.13 37 | 75.89 37 | 76.85 46 | 85.76 28 | 62.02 21 | 81.65 67 | 81.01 103 | 55.51 182 | 73.73 44 | 88.60 47 | 53.23 48 | 84.90 95 | 75.24 17 | 88.33 16 | 83.65 161 |
|
新几何1 | | | | | 70.76 187 | 85.66 29 | 61.13 32 | | 66.43 277 | 44.68 294 | 70.29 78 | 86.64 66 | 41.29 188 | 75.23 268 | 49.72 198 | 81.75 70 | 75.93 271 |
|
MG-MVS | | | 73.96 56 | 73.89 51 | 74.16 89 | 85.65 30 | 49.69 200 | 81.59 71 | 81.29 94 | 61.45 67 | 71.05 73 | 88.11 49 | 51.77 61 | 87.73 29 | 61.05 128 | 83.09 57 | 85.05 110 |
|
1121 | | | 68.53 149 | 67.16 156 | 72.63 141 | 85.64 31 | 61.14 31 | 73.95 208 | 66.46 276 | 44.61 295 | 70.28 79 | 86.68 65 | 41.42 186 | 80.78 191 | 53.62 170 | 81.79 68 | 75.97 269 |
|
TEST9 | | | | | | 85.58 32 | 61.59 27 | 81.62 69 | 81.26 95 | 55.65 179 | 74.93 27 | 88.81 43 | 53.70 44 | 84.68 103 | | | |
|
train_agg | | | 76.27 36 | 76.15 31 | 76.64 52 | 85.58 32 | 61.59 27 | 81.62 69 | 81.26 95 | 55.86 173 | 74.93 27 | 88.81 43 | 53.70 44 | 84.68 103 | 75.24 17 | 88.33 16 | 83.65 161 |
|
ACMMP_Plus | | | 78.77 9 | 78.78 9 | 78.74 23 | 85.44 34 | 61.04 33 | 83.84 32 | 85.16 15 | 62.88 45 | 78.10 12 | 91.26 6 | 52.51 54 | 88.39 16 | 79.34 4 | 90.52 2 | 86.78 45 |
|
test_8 | | | | | | 85.40 35 | 60.96 34 | 81.54 72 | 81.18 98 | 55.86 173 | 74.81 30 | 88.80 45 | 53.70 44 | 84.45 108 | | | |
|
原ACMM1 | | | | | 74.69 78 | 85.39 36 | 59.40 49 | | 83.42 52 | 51.47 235 | 70.27 80 | 86.61 68 | 48.61 108 | 86.51 56 | 53.85 169 | 87.96 25 | 78.16 246 |
|
CDPH-MVS | | | 76.31 35 | 75.67 38 | 78.22 30 | 85.35 37 | 59.14 53 | 81.31 75 | 84.02 33 | 56.32 166 | 74.05 37 | 88.98 40 | 53.34 47 | 87.92 27 | 69.23 50 | 88.42 14 | 87.59 22 |
|
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 76.02 38 | 75.33 40 | 78.07 31 | 85.20 38 | 61.91 23 | 85.49 20 | 84.44 24 | 63.04 41 | 69.80 94 | 89.74 31 | 45.43 144 | 87.16 38 | 72.01 37 | 82.87 62 | 85.14 106 |
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 |
agg_prior1 | | | 75.94 39 | 76.01 34 | 75.72 62 | 85.04 39 | 59.96 44 | 81.44 73 | 81.04 101 | 56.14 171 | 74.68 31 | 88.90 41 | 53.91 40 | 84.04 115 | 75.01 19 | 87.92 27 | 83.16 172 |
|
agg_prior | | | | | | 85.04 39 | 59.96 44 | | 81.04 101 | | 74.68 31 | | | 84.04 115 | | | |
|
HPM-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 77.28 24 | 76.85 25 | 78.54 25 | 85.00 41 | 60.81 36 | 82.91 44 | 85.08 16 | 62.57 50 | 73.09 54 | 89.97 27 | 50.90 72 | 87.48 32 | 75.30 15 | 86.85 36 | 87.33 33 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
MP-MVS-pluss | | | 78.35 13 | 78.46 10 | 78.03 33 | 84.96 42 | 59.52 48 | 82.93 43 | 85.39 12 | 62.15 57 | 76.41 18 | 91.51 4 | 52.47 56 | 86.78 46 | 80.66 2 | 89.64 7 | 87.80 16 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
TSAR-MVS + MP. | | | 78.44 12 | 78.28 12 | 78.90 19 | 84.96 42 | 61.41 29 | 84.03 30 | 83.82 42 | 59.34 119 | 79.37 9 | 89.76 30 | 59.84 5 | 87.62 31 | 76.69 12 | 86.74 38 | 87.68 20 |
|
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 69.99 115 | 68.66 122 | 73.97 92 | 84.94 44 | 57.83 68 | 82.63 50 | 78.71 160 | 56.28 168 | 64.34 180 | 84.14 113 | 41.57 181 | 87.06 41 | 46.45 219 | 78.88 109 | 77.02 261 |
|
DP-MVS | | | 65.68 199 | 63.66 205 | 71.75 164 | 84.93 45 | 56.87 85 | 80.74 81 | 73.16 230 | 53.06 210 | 59.09 253 | 82.35 145 | 36.79 238 | 85.94 69 | 32.82 300 | 69.96 223 | 72.45 309 |
|
DeepPCF-MVS | | 69.58 1 | 79.03 8 | 79.00 8 | 79.13 11 | 84.92 46 | 60.32 41 | 83.03 41 | 85.33 13 | 62.86 46 | 80.17 7 | 90.03 24 | 61.76 3 | 88.95 12 | 74.21 22 | 88.67 12 | 88.12 9 |
|
CPTT-MVS | | | 72.78 66 | 72.08 68 | 74.87 76 | 84.88 47 | 61.41 29 | 84.15 29 | 77.86 176 | 55.27 185 | 67.51 139 | 88.08 51 | 41.93 175 | 81.85 171 | 69.04 52 | 80.01 89 | 81.35 205 |
|
test12 | | | | | 77.76 36 | 84.52 48 | 58.41 62 | | 83.36 55 | | 72.93 57 | | 54.61 34 | 88.05 24 | | 88.12 22 | 86.81 43 |
|
SD-MVS | | | 77.70 20 | 77.62 18 | 77.93 35 | 84.47 49 | 61.88 24 | 84.55 23 | 83.87 40 | 60.37 83 | 79.89 8 | 89.38 34 | 54.97 29 | 85.58 75 | 76.12 14 | 84.94 48 | 86.33 57 |
|
HPM-MVS_fast | | | 74.30 53 | 73.46 57 | 76.80 47 | 84.45 50 | 59.04 54 | 83.65 34 | 81.05 100 | 60.15 90 | 70.43 76 | 89.84 29 | 41.09 191 | 85.59 74 | 67.61 61 | 82.90 61 | 85.77 74 |
|
test_prior3 | | | 76.89 30 | 76.96 24 | 76.69 48 | 84.20 51 | 57.27 75 | 81.75 65 | 84.88 19 | 60.37 83 | 75.01 25 | 89.06 37 | 56.22 21 | 86.43 58 | 72.19 35 | 88.96 10 | 86.38 51 |
|
test_prior | | | | | 76.69 48 | 84.20 51 | 57.27 75 | | 84.88 19 | | | | | 86.43 58 | | | 86.38 51 |
|
CSCG | | | 76.92 28 | 76.75 26 | 77.41 39 | 83.96 53 | 59.60 47 | 82.95 42 | 86.50 5 | 60.78 76 | 75.27 24 | 84.83 100 | 60.76 4 | 86.56 54 | 67.86 58 | 87.87 28 | 86.06 66 |
|
DeepC-MVS | | 69.38 2 | 78.56 11 | 78.14 14 | 79.83 3 | 83.60 54 | 61.62 26 | 84.17 28 | 86.85 2 | 63.23 38 | 73.84 43 | 90.25 21 | 57.68 14 | 89.96 3 | 74.62 21 | 89.03 8 | 87.89 11 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
UA-Net | | | 73.13 63 | 72.93 61 | 73.76 98 | 83.58 55 | 51.66 151 | 78.75 103 | 77.66 180 | 67.75 4 | 72.61 60 | 89.42 32 | 49.82 77 | 83.29 132 | 53.61 172 | 83.14 56 | 86.32 59 |
|
LFMVS | | | 71.78 81 | 71.59 71 | 72.32 157 | 83.40 56 | 46.38 234 | 79.75 94 | 71.08 239 | 64.18 28 | 72.80 58 | 88.64 46 | 42.58 168 | 83.72 123 | 57.41 144 | 84.49 51 | 86.86 41 |
|
test222 | | | | | | 83.14 57 | 58.68 60 | 72.57 228 | 63.45 300 | 41.78 315 | 67.56 138 | 86.12 79 | 37.13 227 | | | 78.73 114 | 74.98 282 |
|
旧先验1 | | | | | | 83.04 58 | 53.15 131 | | 67.52 269 | | | 87.85 52 | 44.08 158 | | | 80.76 76 | 78.03 250 |
|
MSLP-MVS++ | | | 73.77 59 | 73.47 56 | 74.66 79 | 83.02 59 | 59.29 52 | 82.30 61 | 81.88 78 | 59.34 119 | 71.59 70 | 86.83 60 | 45.94 137 | 83.65 125 | 65.09 78 | 85.22 47 | 81.06 211 |
|
SteuartSystems-ACMMP | | | 79.48 7 | 79.31 7 | 79.98 1 | 83.01 60 | 62.18 19 | 87.60 2 | 85.83 9 | 66.69 11 | 78.03 13 | 90.98 7 | 54.26 36 | 90.06 2 | 78.42 8 | 89.02 9 | 87.69 19 |
Skip Steuart: Steuart Systems R&D Blog. |
MVS_111021_HR | | | 74.02 55 | 73.46 57 | 75.69 64 | 83.01 60 | 60.63 38 | 77.29 147 | 78.40 172 | 61.18 71 | 70.58 75 | 85.97 83 | 54.18 38 | 84.00 119 | 67.52 62 | 82.98 60 | 82.45 184 |
|
VDDNet | | | 71.81 80 | 71.33 78 | 73.26 126 | 82.80 62 | 47.60 225 | 78.74 104 | 75.27 208 | 59.59 112 | 72.94 56 | 89.40 33 | 41.51 185 | 83.91 120 | 58.75 139 | 82.99 59 | 88.26 6 |
|
abl_6 | | | 74.34 51 | 73.50 54 | 76.86 45 | 82.43 63 | 60.16 42 | 83.48 36 | 81.86 79 | 58.81 125 | 73.95 40 | 89.86 28 | 41.87 176 | 86.62 50 | 67.98 56 | 81.23 74 | 83.80 154 |
|
3Dnovator+ | | 66.72 4 | 75.84 41 | 74.57 45 | 79.66 4 | 82.40 64 | 59.92 46 | 85.83 13 | 86.32 8 | 66.92 9 | 67.80 134 | 89.24 36 | 42.03 173 | 89.38 7 | 64.07 93 | 86.50 41 | 89.69 1 |
|
APD-MVS_3200maxsize | | | 74.96 45 | 74.39 47 | 76.67 51 | 82.20 65 | 58.24 65 | 83.67 33 | 83.29 58 | 58.41 132 | 73.71 45 | 90.14 22 | 45.62 139 | 85.99 66 | 69.64 48 | 82.85 63 | 85.78 72 |
|
PVSNet_Blended_VisFu | | | 71.45 87 | 70.39 89 | 74.65 80 | 82.01 66 | 58.82 58 | 79.93 90 | 80.35 124 | 55.09 188 | 65.82 161 | 82.16 152 | 49.17 98 | 82.64 159 | 60.34 132 | 78.62 116 | 82.50 183 |
|
TSAR-MVS + GP. | | | 74.90 46 | 74.15 49 | 77.17 42 | 82.00 67 | 58.77 59 | 81.80 64 | 78.57 163 | 58.58 128 | 74.32 35 | 84.51 109 | 55.94 23 | 87.22 35 | 67.11 64 | 84.48 52 | 85.52 85 |
|
API-MVS | | | 72.17 76 | 71.41 74 | 74.45 85 | 81.95 68 | 57.22 77 | 84.03 30 | 80.38 122 | 59.89 98 | 68.40 118 | 82.33 146 | 49.64 79 | 87.83 28 | 51.87 182 | 84.16 54 | 78.30 244 |
|
MAR-MVS | | | 71.51 85 | 70.15 93 | 75.60 67 | 81.84 69 | 59.39 50 | 81.38 74 | 82.90 66 | 54.90 192 | 68.08 126 | 78.70 235 | 47.73 117 | 85.51 78 | 51.68 186 | 84.17 53 | 81.88 192 |
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 |
PAPM_NR | | | 72.63 68 | 71.80 69 | 75.13 73 | 81.72 70 | 53.42 128 | 79.91 91 | 83.28 59 | 59.14 121 | 66.31 154 | 85.90 84 | 51.86 60 | 86.06 63 | 57.45 143 | 80.62 77 | 85.91 69 |
|
VDD-MVS | | | 72.50 69 | 72.09 67 | 73.75 100 | 81.58 71 | 49.69 200 | 77.76 133 | 77.63 181 | 63.21 39 | 73.21 52 | 89.02 39 | 42.14 172 | 83.32 131 | 61.72 125 | 82.50 64 | 88.25 7 |
|
PS-MVSNAJ | | | 70.51 100 | 69.70 98 | 72.93 131 | 81.52 72 | 55.79 101 | 74.92 194 | 79.00 155 | 55.04 190 | 69.88 89 | 78.66 236 | 47.05 126 | 82.19 165 | 61.61 126 | 79.58 97 | 80.83 218 |
|
testdata | | | | | 64.66 262 | 81.52 72 | 52.93 134 | | 65.29 283 | 46.09 282 | 73.88 42 | 87.46 54 | 38.08 218 | 66.26 303 | 53.31 175 | 78.48 117 | 74.78 286 |
|
CHOSEN 1792x2688 | | | 65.08 209 | 62.84 214 | 71.82 163 | 81.49 74 | 56.26 91 | 66.32 287 | 74.20 222 | 40.53 325 | 63.16 191 | 78.65 237 | 41.30 187 | 77.80 240 | 45.80 226 | 74.09 152 | 81.40 198 |
|
HQP_MVS | | | 74.31 52 | 73.73 53 | 76.06 56 | 81.41 75 | 56.31 88 | 84.22 26 | 84.01 34 | 64.52 24 | 69.27 106 | 86.10 80 | 45.26 148 | 87.21 36 | 68.16 54 | 80.58 79 | 84.65 122 |
|
plane_prior7 | | | | | | 81.41 75 | 55.96 98 | | | | | | | | | | |
|
MVS_0304 | | | 76.73 32 | 76.04 33 | 78.78 22 | 81.32 77 | 58.89 57 | 82.50 54 | 84.07 32 | 67.73 5 | 72.08 66 | 87.28 58 | 49.49 80 | 89.57 6 | 73.52 30 | 86.40 42 | 87.87 13 |
|
CANet | | | 76.46 34 | 75.93 35 | 78.06 32 | 81.29 78 | 57.53 72 | 82.35 56 | 83.31 57 | 67.78 3 | 70.09 81 | 86.34 76 | 54.92 30 | 88.90 13 | 72.68 33 | 84.55 50 | 87.76 18 |
|
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 72.18 75 | 71.37 77 | 74.61 82 | 81.29 78 | 55.41 110 | 80.90 78 | 78.28 174 | 60.73 77 | 69.23 109 | 88.09 50 | 44.36 157 | 82.65 158 | 57.68 142 | 81.75 70 | 85.77 74 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
plane_prior1 | | | | | | 81.27 80 | | | | | | | | | | | |
|
xiu_mvs_v2_base | | | 70.52 99 | 69.75 96 | 72.84 135 | 81.21 81 | 55.63 105 | 75.11 189 | 78.92 156 | 54.92 191 | 69.96 88 | 79.68 218 | 47.00 130 | 82.09 168 | 61.60 127 | 79.37 100 | 80.81 219 |
|
plane_prior6 | | | | | | 81.20 82 | 56.24 92 | | | | | | 45.26 148 | | | | |
|
PAPR | | | 71.72 82 | 70.82 85 | 74.41 86 | 81.20 82 | 51.17 155 | 79.55 98 | 83.33 56 | 55.81 176 | 66.93 146 | 84.61 105 | 50.95 70 | 86.06 63 | 55.79 153 | 79.20 106 | 86.00 67 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 56.13 14 | 65.09 208 | 63.21 210 | 70.72 189 | 81.04 84 | 54.87 115 | 78.57 108 | 77.47 183 | 48.51 260 | 55.71 283 | 81.89 160 | 33.71 269 | 79.71 204 | 41.66 260 | 70.37 211 | 77.58 252 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
NP-MVS | | | | | | 80.98 85 | 56.05 97 | | | | | 85.54 93 | | | | | |
|
OPM-MVS | | | 74.73 48 | 74.25 48 | 76.19 55 | 80.81 86 | 59.01 55 | 82.60 51 | 83.64 45 | 63.74 33 | 72.52 61 | 87.49 53 | 47.18 125 | 85.88 70 | 69.47 49 | 80.78 75 | 83.66 160 |
|
HQP-NCC | | | | | | 80.66 87 | | 82.31 58 | | 62.10 58 | 67.85 129 | | | | | | |
|
ACMP_Plane | | | | | | 80.66 87 | | 82.31 58 | | 62.10 58 | 67.85 129 | | | | | | |
|
HQP-MVS | | | 73.45 60 | 72.80 62 | 75.40 69 | 80.66 87 | 54.94 112 | 82.31 58 | 83.90 38 | 62.10 58 | 67.85 129 | 85.54 93 | 45.46 142 | 86.93 42 | 67.04 65 | 80.35 85 | 84.32 129 |
|
PHI-MVS | | | 75.87 40 | 75.36 39 | 77.41 39 | 80.62 90 | 55.91 100 | 84.28 25 | 85.78 10 | 56.08 172 | 73.41 50 | 86.58 70 | 50.94 71 | 88.54 14 | 70.79 43 | 89.71 5 | 87.79 17 |
|
ACMM | | 61.98 7 | 70.80 93 | 69.73 97 | 74.02 90 | 80.59 91 | 58.59 61 | 82.68 49 | 82.02 77 | 55.46 183 | 67.18 143 | 84.39 111 | 38.51 211 | 83.17 135 | 60.65 129 | 76.10 139 | 80.30 224 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Regformer-1 | | | 75.47 43 | 74.93 43 | 77.09 43 | 80.43 92 | 57.70 70 | 79.50 99 | 82.13 73 | 67.84 1 | 75.73 23 | 80.75 195 | 56.50 17 | 86.07 62 | 71.07 42 | 80.38 83 | 87.50 24 |
|
Regformer-2 | | | 75.63 42 | 74.99 41 | 77.54 37 | 80.43 92 | 58.32 64 | 79.50 99 | 82.92 64 | 67.84 1 | 75.94 20 | 80.75 195 | 55.73 24 | 86.80 44 | 71.44 41 | 80.38 83 | 87.50 24 |
|
casdiffmvs1 | | | 75.24 44 | 74.76 44 | 76.69 48 | 80.32 94 | 55.61 106 | 82.80 45 | 83.60 46 | 52.54 216 | 76.15 19 | 86.48 72 | 59.44 10 | 85.78 72 | 69.78 46 | 81.70 72 | 88.69 2 |
|
Anonymous20231211 | | | 69.28 131 | 68.47 125 | 71.73 165 | 80.28 95 | 47.18 229 | 79.98 89 | 82.37 71 | 54.61 194 | 67.24 142 | 84.01 117 | 39.43 202 | 82.41 163 | 55.45 157 | 72.83 174 | 85.62 82 |
|
ACMP | | 63.53 6 | 72.30 73 | 71.20 81 | 75.59 68 | 80.28 95 | 57.54 71 | 82.74 48 | 82.84 68 | 60.58 79 | 65.24 168 | 86.18 78 | 39.25 204 | 86.03 65 | 66.95 67 | 76.79 136 | 83.22 167 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LPG-MVS_test | | | 72.74 67 | 71.74 70 | 75.76 60 | 80.22 97 | 57.51 73 | 82.55 52 | 83.40 53 | 61.32 68 | 66.67 148 | 87.33 56 | 39.15 206 | 86.59 51 | 67.70 59 | 77.30 129 | 83.19 169 |
|
LGP-MVS_train | | | | | 75.76 60 | 80.22 97 | 57.51 73 | | 83.40 53 | 61.32 68 | 66.67 148 | 87.33 56 | 39.15 206 | 86.59 51 | 67.70 59 | 77.30 129 | 83.19 169 |
|
WR-MVS | | | 68.47 150 | 68.47 125 | 68.44 217 | 80.20 99 | 39.84 280 | 73.75 213 | 76.07 200 | 64.68 21 | 68.11 125 | 83.63 124 | 50.39 75 | 79.14 220 | 49.78 195 | 69.66 231 | 86.34 56 |
|
Anonymous20240529 | | | 69.91 117 | 69.02 116 | 72.56 144 | 80.19 100 | 47.65 224 | 77.56 140 | 80.99 104 | 55.45 184 | 69.88 89 | 86.76 61 | 39.24 205 | 82.18 166 | 54.04 167 | 77.10 131 | 87.85 14 |
|
Anonymous202405211 | | | 66.84 184 | 65.99 178 | 69.40 206 | 80.19 100 | 42.21 267 | 71.11 250 | 71.31 238 | 58.80 126 | 67.90 127 | 86.39 75 | 29.83 301 | 79.65 205 | 49.60 201 | 78.78 112 | 86.33 57 |
|
BH-RMVSNet | | | 68.81 139 | 67.42 145 | 72.97 130 | 80.11 102 | 52.53 140 | 74.26 205 | 76.29 197 | 58.48 131 | 68.38 119 | 84.20 112 | 42.59 167 | 83.83 122 | 46.53 218 | 75.91 140 | 82.56 180 |
|
test_0402 | | | 63.25 225 | 61.01 239 | 69.96 197 | 80.00 103 | 54.37 118 | 76.86 156 | 72.02 236 | 54.58 196 | 58.71 256 | 80.79 193 | 35.00 254 | 84.36 109 | 26.41 340 | 64.71 270 | 71.15 322 |
|
HyFIR lowres test | | | 65.67 200 | 63.01 212 | 73.67 103 | 79.97 104 | 55.65 104 | 69.07 270 | 75.52 205 | 42.68 313 | 63.53 187 | 77.95 244 | 40.43 195 | 81.64 174 | 46.01 224 | 71.91 191 | 83.73 155 |
|
Regformer-3 | | | 73.89 57 | 73.28 59 | 75.71 63 | 79.75 105 | 55.48 109 | 78.54 110 | 79.93 128 | 66.58 12 | 73.62 46 | 80.30 205 | 54.87 31 | 84.54 106 | 69.09 51 | 76.84 134 | 87.10 37 |
|
Regformer-4 | | | 74.25 54 | 73.48 55 | 76.57 53 | 79.75 105 | 56.54 87 | 78.54 110 | 81.49 87 | 66.93 8 | 73.90 41 | 80.30 205 | 53.84 42 | 85.98 67 | 69.76 47 | 76.84 134 | 87.17 35 |
|
BH-untuned | | | 68.27 156 | 67.29 150 | 71.21 179 | 79.74 107 | 53.22 130 | 76.06 171 | 77.46 185 | 57.19 144 | 66.10 155 | 81.61 168 | 45.37 146 | 83.50 127 | 45.42 234 | 76.68 138 | 76.91 265 |
|
VNet | | | 69.68 122 | 70.19 92 | 68.16 218 | 79.73 108 | 41.63 273 | 70.53 256 | 77.38 186 | 60.37 83 | 70.69 74 | 86.63 67 | 51.08 68 | 77.09 248 | 53.61 172 | 81.69 73 | 85.75 76 |
|
LS3D | | | 64.71 211 | 62.50 218 | 71.34 177 | 79.72 109 | 55.71 102 | 79.82 92 | 74.72 216 | 48.50 261 | 56.62 278 | 84.62 104 | 33.59 271 | 82.34 164 | 29.65 325 | 75.23 145 | 75.97 269 |
|
BH-w/o | | | 66.85 183 | 65.83 181 | 69.90 199 | 79.29 110 | 52.46 142 | 74.66 201 | 76.65 194 | 54.51 199 | 64.85 175 | 78.12 242 | 45.59 141 | 82.95 141 | 43.26 248 | 75.54 143 | 74.27 291 |
|
1112_ss | | | 64.00 217 | 63.36 209 | 65.93 246 | 79.28 111 | 42.58 265 | 71.35 244 | 72.36 235 | 46.41 279 | 60.55 236 | 77.89 247 | 46.27 136 | 73.28 274 | 46.18 221 | 69.97 222 | 81.92 191 |
|
UniMVSNet_NR-MVSNet | | | 71.11 89 | 71.00 83 | 71.44 171 | 79.20 112 | 44.13 252 | 76.02 174 | 82.60 69 | 66.48 14 | 68.20 121 | 84.60 106 | 56.82 15 | 82.82 149 | 54.62 163 | 70.43 206 | 87.36 32 |
|
VPNet | | | 67.52 170 | 68.11 132 | 65.74 249 | 79.18 113 | 36.80 309 | 72.17 234 | 72.83 232 | 62.04 61 | 67.79 135 | 85.83 86 | 48.88 105 | 76.60 253 | 51.30 187 | 72.97 173 | 83.81 151 |
|
TR-MVS | | | 66.59 191 | 65.07 194 | 71.17 181 | 79.18 113 | 49.63 202 | 73.48 216 | 75.20 210 | 52.95 211 | 67.90 127 | 80.33 204 | 39.81 198 | 83.68 124 | 43.20 249 | 73.56 161 | 80.20 225 |
|
TAMVS | | | 66.78 186 | 65.27 190 | 71.33 178 | 79.16 115 | 53.67 122 | 73.84 212 | 69.59 250 | 52.32 219 | 65.28 165 | 81.72 163 | 44.49 155 | 77.40 245 | 42.32 255 | 78.66 115 | 82.92 175 |
|
Test_1112_low_res | | | 62.32 237 | 61.77 231 | 64.00 266 | 79.08 116 | 39.53 284 | 68.17 279 | 70.17 244 | 43.25 308 | 59.03 254 | 79.90 211 | 44.08 158 | 71.24 282 | 43.79 244 | 68.42 240 | 81.25 206 |
|
casdiffmvs | | | 74.55 50 | 73.78 52 | 76.87 44 | 79.00 117 | 56.18 93 | 82.36 55 | 84.45 23 | 53.88 205 | 73.46 49 | 85.76 89 | 56.38 20 | 86.59 51 | 70.70 44 | 78.04 120 | 87.83 15 |
|
CDS-MVSNet | | | 66.80 185 | 65.37 187 | 71.10 183 | 78.98 118 | 53.13 133 | 73.27 217 | 71.07 240 | 52.15 220 | 64.72 176 | 80.23 208 | 43.56 163 | 77.10 247 | 45.48 232 | 78.88 109 | 83.05 174 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
canonicalmvs | | | 74.67 49 | 74.98 42 | 73.71 102 | 78.94 119 | 50.56 172 | 80.23 85 | 83.87 40 | 60.30 88 | 77.15 15 | 86.56 71 | 59.65 6 | 82.00 169 | 66.01 71 | 82.12 66 | 88.58 5 |
|
IS-MVSNet | | | 71.57 83 | 71.00 83 | 73.27 125 | 78.86 120 | 45.63 240 | 80.22 86 | 78.69 161 | 64.14 29 | 66.46 150 | 87.36 55 | 49.30 84 | 85.60 73 | 50.26 193 | 83.71 55 | 88.59 4 |
|
CLD-MVS | | | 73.33 61 | 72.68 63 | 75.29 72 | 78.82 121 | 53.33 129 | 78.23 116 | 84.79 22 | 61.30 70 | 70.41 77 | 81.04 182 | 52.41 57 | 87.12 39 | 64.61 83 | 82.49 65 | 85.41 99 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
pcd1.5k->3k | | | 30.06 336 | 30.56 337 | 28.55 350 | 78.81 122 | 0.00 373 | 0.00 365 | 82.07 76 | 0.00 369 | 0.00 371 | 0.00 371 | 39.61 200 | 0.00 369 | 0.00 368 | 74.56 147 | 85.66 80 |
|
MVSFormer | | | 71.50 86 | 70.38 90 | 74.88 75 | 78.76 123 | 57.15 82 | 82.79 46 | 78.48 167 | 51.26 238 | 69.49 101 | 83.22 130 | 43.99 160 | 83.24 133 | 66.06 69 | 79.37 100 | 84.23 136 |
|
lupinMVS | | | 69.57 125 | 68.28 129 | 73.44 115 | 78.76 123 | 57.15 82 | 76.57 159 | 73.29 229 | 46.19 281 | 69.49 101 | 82.18 149 | 43.99 160 | 79.23 212 | 64.66 81 | 79.37 100 | 83.93 146 |
|
CNLPA | | | 65.43 203 | 64.02 199 | 69.68 200 | 78.73 125 | 58.07 66 | 77.82 132 | 70.71 242 | 51.49 234 | 61.57 222 | 83.58 126 | 38.23 216 | 70.82 283 | 43.90 242 | 70.10 220 | 80.16 226 |
|
EPP-MVSNet | | | 72.16 77 | 71.31 79 | 74.71 77 | 78.68 126 | 49.70 198 | 82.10 62 | 81.65 83 | 60.40 82 | 65.94 157 | 85.84 85 | 51.74 62 | 86.37 60 | 55.93 150 | 79.55 99 | 88.07 10 |
|
TranMVSNet+NR-MVSNet | | | 70.36 107 | 70.10 94 | 71.17 181 | 78.64 127 | 42.97 263 | 76.53 160 | 81.16 99 | 66.95 7 | 68.53 117 | 85.42 95 | 51.61 63 | 83.07 138 | 52.32 180 | 69.70 230 | 87.46 26 |
|
UniMVSNet (Re) | | | 70.63 97 | 70.20 91 | 71.89 161 | 78.55 128 | 45.29 242 | 75.94 175 | 82.92 64 | 63.68 34 | 68.16 123 | 83.59 125 | 53.89 41 | 83.49 128 | 53.97 168 | 71.12 198 | 86.89 40 |
|
Fast-Effi-MVS+ | | | 70.28 110 | 69.12 115 | 73.73 101 | 78.50 129 | 51.50 154 | 75.01 191 | 79.46 146 | 56.16 170 | 68.59 114 | 79.55 225 | 53.97 39 | 84.05 114 | 53.34 174 | 77.53 124 | 85.65 81 |
|
PS-MVSNAJss | | | 72.24 74 | 71.21 80 | 75.31 71 | 78.50 129 | 55.93 99 | 81.63 68 | 82.12 74 | 56.24 169 | 70.02 85 | 85.68 90 | 47.05 126 | 84.34 110 | 65.27 77 | 74.41 150 | 85.67 79 |
|
EI-MVSNet-Vis-set | | | 72.42 72 | 71.59 71 | 74.91 74 | 78.47 131 | 54.02 119 | 77.05 151 | 79.33 150 | 65.03 20 | 71.68 69 | 79.35 229 | 52.75 51 | 84.89 96 | 66.46 68 | 74.23 151 | 85.83 71 |
|
MVS_111021_LR | | | 69.50 127 | 68.78 120 | 71.65 167 | 78.38 132 | 59.33 51 | 74.82 196 | 70.11 245 | 58.08 136 | 67.83 133 | 84.68 102 | 41.96 174 | 76.34 256 | 65.62 75 | 77.54 123 | 79.30 238 |
|
0601test | | | 69.69 120 | 69.13 113 | 71.36 175 | 78.37 133 | 45.74 238 | 74.71 199 | 80.20 125 | 57.91 137 | 70.01 86 | 83.83 119 | 42.44 169 | 82.87 145 | 54.97 159 | 79.72 94 | 85.48 87 |
|
Anonymous20240521 | | | 69.69 120 | 69.13 113 | 71.36 175 | 78.37 133 | 45.74 238 | 74.71 199 | 80.20 125 | 57.91 137 | 70.01 86 | 83.83 119 | 42.44 169 | 82.87 145 | 54.97 159 | 79.72 94 | 85.48 87 |
|
FIs | | | 70.82 92 | 71.43 73 | 68.98 210 | 78.33 135 | 38.14 297 | 76.96 153 | 83.59 47 | 61.02 72 | 67.33 141 | 86.73 62 | 55.07 28 | 81.64 174 | 54.61 165 | 79.22 105 | 87.14 36 |
|
UGNet | | | 68.81 139 | 67.39 146 | 73.06 129 | 78.33 135 | 54.47 117 | 79.77 93 | 75.40 207 | 60.45 81 | 63.22 189 | 84.40 110 | 32.71 283 | 80.91 188 | 51.71 185 | 80.56 81 | 83.81 151 |
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 |
jason | | | 69.65 123 | 68.39 128 | 73.43 116 | 78.27 137 | 56.88 84 | 77.12 149 | 73.71 227 | 46.53 278 | 69.34 105 | 83.22 130 | 43.37 164 | 79.18 214 | 64.77 80 | 79.20 106 | 84.23 136 |
jason: jason. |
alignmvs | | | 73.86 58 | 73.99 50 | 73.45 114 | 78.20 138 | 50.50 174 | 78.57 108 | 82.43 70 | 59.40 117 | 76.57 16 | 86.71 64 | 56.42 19 | 81.23 182 | 65.84 73 | 81.79 68 | 88.62 3 |
|
xiu_mvs_v1_base_debu | | | 68.58 145 | 67.28 151 | 72.48 148 | 78.19 139 | 57.19 79 | 75.28 184 | 75.09 212 | 51.61 230 | 70.04 82 | 81.41 176 | 32.79 279 | 79.02 222 | 63.81 98 | 77.31 126 | 81.22 207 |
|
xiu_mvs_v1_base | | | 68.58 145 | 67.28 151 | 72.48 148 | 78.19 139 | 57.19 79 | 75.28 184 | 75.09 212 | 51.61 230 | 70.04 82 | 81.41 176 | 32.79 279 | 79.02 222 | 63.81 98 | 77.31 126 | 81.22 207 |
|
xiu_mvs_v1_base_debi | | | 68.58 145 | 67.28 151 | 72.48 148 | 78.19 139 | 57.19 79 | 75.28 184 | 75.09 212 | 51.61 230 | 70.04 82 | 81.41 176 | 32.79 279 | 79.02 222 | 63.81 98 | 77.31 126 | 81.22 207 |
|
PAPM | | | 67.92 166 | 66.69 167 | 71.63 168 | 78.09 142 | 49.02 208 | 77.09 150 | 81.24 97 | 51.04 241 | 60.91 232 | 83.98 118 | 47.71 118 | 84.99 87 | 40.81 264 | 79.32 104 | 80.90 217 |
|
ACMH | | 55.70 15 | 65.20 207 | 63.57 206 | 70.07 196 | 78.07 143 | 52.01 150 | 79.48 101 | 79.69 130 | 55.75 177 | 56.59 279 | 80.98 186 | 27.12 317 | 80.94 186 | 42.90 253 | 71.58 194 | 77.25 259 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
DU-MVS | | | 70.01 114 | 69.53 103 | 71.44 171 | 78.05 144 | 44.13 252 | 75.01 191 | 81.51 86 | 64.37 27 | 68.20 121 | 84.52 107 | 49.12 101 | 82.82 149 | 54.62 163 | 70.43 206 | 87.37 30 |
|
NR-MVSNet | | | 69.54 126 | 68.85 118 | 71.59 169 | 78.05 144 | 43.81 256 | 74.20 206 | 80.86 108 | 65.18 17 | 62.76 194 | 84.52 107 | 52.35 58 | 83.59 126 | 50.96 189 | 70.78 200 | 87.37 30 |
|
EI-MVSNet-UG-set | | | 71.92 79 | 71.06 82 | 74.52 84 | 77.98 146 | 53.56 125 | 76.62 158 | 79.16 152 | 64.40 26 | 71.18 71 | 78.95 234 | 52.19 59 | 84.66 105 | 65.47 76 | 73.57 160 | 85.32 101 |
|
WR-MVS_H | | | 67.02 180 | 66.92 160 | 67.33 226 | 77.95 147 | 37.75 300 | 77.57 139 | 82.11 75 | 62.03 62 | 62.65 197 | 82.48 143 | 50.57 73 | 79.46 208 | 42.91 252 | 64.01 276 | 84.79 119 |
|
Effi-MVS+ | | | 73.31 62 | 72.54 64 | 75.62 66 | 77.87 148 | 53.64 123 | 79.62 97 | 79.61 133 | 61.63 65 | 72.02 67 | 82.61 139 | 56.44 18 | 85.97 68 | 63.99 96 | 79.07 108 | 87.25 34 |
|
DELS-MVS | | | 74.76 47 | 74.46 46 | 75.65 65 | 77.84 149 | 52.25 145 | 75.59 178 | 84.17 30 | 63.76 32 | 73.15 53 | 82.79 134 | 59.58 8 | 86.80 44 | 67.24 63 | 86.04 44 | 87.89 11 |
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 |
ACMH+ | | 57.40 11 | 66.12 195 | 64.06 198 | 72.30 158 | 77.79 150 | 52.83 135 | 80.39 84 | 78.03 175 | 57.30 143 | 57.47 273 | 82.55 141 | 27.68 313 | 84.17 112 | 45.54 230 | 69.78 227 | 79.90 229 |
|
3Dnovator | | 64.47 5 | 72.49 70 | 71.39 76 | 75.79 59 | 77.70 151 | 58.99 56 | 80.66 82 | 83.15 61 | 62.24 56 | 65.46 163 | 86.59 69 | 42.38 171 | 85.52 77 | 59.59 138 | 84.72 49 | 82.85 178 |
|
EG-PatchMatch MVS | | | 64.71 211 | 62.87 213 | 70.22 193 | 77.68 152 | 53.48 126 | 77.99 126 | 78.82 157 | 53.37 209 | 56.03 282 | 77.41 263 | 24.75 331 | 84.04 115 | 46.37 220 | 73.42 163 | 73.14 302 |
|
CP-MVSNet | | | 66.49 192 | 66.41 173 | 66.72 229 | 77.67 153 | 36.33 313 | 76.83 157 | 79.52 144 | 62.45 53 | 62.54 200 | 83.47 129 | 46.32 134 | 78.37 232 | 45.47 233 | 63.43 281 | 85.45 92 |
|
GBi-Net | | | 67.21 174 | 66.55 168 | 69.19 207 | 77.63 154 | 43.33 259 | 77.31 144 | 77.83 177 | 56.62 160 | 65.04 171 | 82.70 135 | 41.85 177 | 80.33 197 | 47.18 213 | 72.76 176 | 83.92 147 |
|
test1 | | | 67.21 174 | 66.55 168 | 69.19 207 | 77.63 154 | 43.33 259 | 77.31 144 | 77.83 177 | 56.62 160 | 65.04 171 | 82.70 135 | 41.85 177 | 80.33 197 | 47.18 213 | 72.76 176 | 83.92 147 |
|
FMVSNet2 | | | 66.93 182 | 66.31 177 | 68.79 213 | 77.63 154 | 42.98 262 | 76.11 169 | 77.47 183 | 56.62 160 | 65.22 170 | 82.17 151 | 41.85 177 | 80.18 200 | 47.05 216 | 72.72 179 | 83.20 168 |
|
PCF-MVS | | 61.88 8 | 70.95 90 | 69.49 107 | 75.35 70 | 77.63 154 | 55.71 102 | 76.04 173 | 81.81 81 | 50.30 246 | 69.66 95 | 85.40 96 | 52.51 54 | 84.89 96 | 51.82 183 | 80.24 87 | 85.45 92 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVP-Stereo | | | 65.41 204 | 63.80 202 | 70.22 193 | 77.62 158 | 55.53 107 | 76.30 164 | 78.53 165 | 50.59 245 | 56.47 280 | 78.65 237 | 39.84 197 | 82.68 157 | 44.10 241 | 72.12 190 | 72.44 310 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
FC-MVSNet-test | | | 69.80 118 | 70.58 88 | 67.46 223 | 77.61 159 | 34.73 321 | 76.05 172 | 83.19 60 | 60.84 73 | 65.88 159 | 86.46 73 | 54.52 35 | 80.76 193 | 52.52 179 | 78.12 119 | 86.91 39 |
|
PS-CasMVS | | | 66.42 193 | 66.32 176 | 66.70 231 | 77.60 160 | 36.30 315 | 76.94 154 | 79.61 133 | 62.36 55 | 62.43 209 | 83.66 123 | 45.69 138 | 78.37 232 | 45.35 235 | 63.26 282 | 85.42 95 |
|
FMVSNet1 | | | 66.70 187 | 65.87 180 | 69.19 207 | 77.49 161 | 43.33 259 | 77.31 144 | 77.83 177 | 56.45 164 | 64.60 179 | 82.70 135 | 38.08 218 | 80.33 197 | 46.08 223 | 72.31 188 | 83.92 147 |
|
VPA-MVSNet | | | 69.02 136 | 69.47 109 | 67.69 222 | 77.42 162 | 41.00 277 | 74.04 207 | 79.68 131 | 60.06 91 | 69.26 108 | 84.81 101 | 51.06 69 | 77.58 242 | 54.44 166 | 74.43 149 | 84.48 127 |
|
tfpn111 | | | 63.33 221 | 62.34 221 | 66.30 235 | 77.31 163 | 38.66 291 | 72.65 223 | 69.11 257 | 57.07 145 | 62.45 204 | 81.03 183 | 37.01 229 | 79.23 212 | 31.38 313 | 73.09 171 | 81.03 212 |
|
conf200view11 | | | 63.38 220 | 62.41 219 | 66.29 237 | 77.31 163 | 38.66 291 | 72.65 223 | 69.11 257 | 57.07 145 | 62.45 204 | 81.03 183 | 37.01 229 | 79.17 215 | 31.84 304 | 73.25 166 | 81.03 212 |
|
thres100view900 | | | 63.28 224 | 62.41 219 | 65.89 247 | 77.31 163 | 38.66 291 | 72.65 223 | 69.11 257 | 57.07 145 | 62.45 204 | 81.03 183 | 37.01 229 | 79.17 215 | 31.84 304 | 73.25 166 | 79.83 230 |
|
view600 | | | 62.77 230 | 61.84 227 | 65.55 251 | 77.28 166 | 36.87 305 | 72.15 235 | 67.78 265 | 56.79 152 | 61.46 223 | 81.92 156 | 36.88 233 | 78.42 228 | 29.86 320 | 72.46 181 | 81.36 199 |
|
view800 | | | 62.77 230 | 61.84 227 | 65.55 251 | 77.28 166 | 36.87 305 | 72.15 235 | 67.78 265 | 56.79 152 | 61.46 223 | 81.92 156 | 36.88 233 | 78.42 228 | 29.86 320 | 72.46 181 | 81.36 199 |
|
conf0.05thres1000 | | | 62.77 230 | 61.84 227 | 65.55 251 | 77.28 166 | 36.87 305 | 72.15 235 | 67.78 265 | 56.79 152 | 61.46 223 | 81.92 156 | 36.88 233 | 78.42 228 | 29.86 320 | 72.46 181 | 81.36 199 |
|
tfpn | | | 62.77 230 | 61.84 227 | 65.55 251 | 77.28 166 | 36.87 305 | 72.15 235 | 67.78 265 | 56.79 152 | 61.46 223 | 81.92 156 | 36.88 233 | 78.42 228 | 29.86 320 | 72.46 181 | 81.36 199 |
|
cascas | | | 65.98 197 | 63.42 208 | 73.64 106 | 77.26 170 | 52.58 139 | 72.26 233 | 77.21 188 | 48.56 259 | 61.21 228 | 74.60 291 | 32.57 287 | 85.82 71 | 50.38 192 | 76.75 137 | 82.52 182 |
|
thres600view7 | | | 63.30 223 | 62.27 222 | 66.41 233 | 77.18 171 | 38.87 288 | 72.35 231 | 69.11 257 | 56.98 149 | 62.37 210 | 80.96 187 | 37.01 229 | 79.00 225 | 31.43 312 | 73.05 172 | 81.36 199 |
|
PEN-MVS | | | 66.60 189 | 66.45 170 | 67.04 227 | 77.11 172 | 36.56 311 | 77.03 152 | 80.42 121 | 62.95 42 | 62.51 202 | 84.03 116 | 46.69 132 | 79.07 221 | 44.22 238 | 63.08 284 | 85.51 86 |
|
PatchMatch-RL | | | 56.25 284 | 54.55 287 | 61.32 284 | 77.06 173 | 56.07 96 | 65.57 291 | 54.10 344 | 44.13 302 | 53.49 308 | 71.27 308 | 25.20 328 | 66.78 300 | 36.52 288 | 63.66 278 | 61.12 342 |
|
PVSNet_BlendedMVS | | | 68.56 148 | 67.72 137 | 71.07 184 | 77.03 174 | 50.57 170 | 74.50 204 | 81.52 84 | 53.66 208 | 64.22 184 | 79.72 217 | 49.13 99 | 82.87 145 | 55.82 151 | 73.92 155 | 79.77 233 |
|
PVSNet_Blended | | | 68.59 144 | 67.72 137 | 71.19 180 | 77.03 174 | 50.57 170 | 72.51 229 | 81.52 84 | 51.91 222 | 64.22 184 | 77.77 250 | 49.13 99 | 82.87 145 | 55.82 151 | 79.58 97 | 80.14 227 |
|
F-COLMAP | | | 63.05 228 | 60.87 240 | 69.58 204 | 76.99 176 | 53.63 124 | 78.12 118 | 76.16 198 | 47.97 268 | 52.41 310 | 81.61 168 | 27.87 311 | 78.11 236 | 40.07 267 | 66.66 257 | 77.00 262 |
|
tfpn200view9 | | | 63.18 226 | 62.18 224 | 66.21 239 | 76.85 177 | 39.62 282 | 71.96 241 | 69.44 253 | 56.63 158 | 62.61 198 | 79.83 213 | 37.18 224 | 79.17 215 | 31.84 304 | 73.25 166 | 79.83 230 |
|
thres400 | | | 63.31 222 | 62.18 224 | 66.72 229 | 76.85 177 | 39.62 282 | 71.96 241 | 69.44 253 | 56.63 158 | 62.61 198 | 79.83 213 | 37.18 224 | 79.17 215 | 31.84 304 | 73.25 166 | 81.36 199 |
|
tttt0517 | | | 67.83 168 | 65.66 184 | 74.33 87 | 76.69 179 | 50.82 163 | 77.86 130 | 73.99 224 | 54.54 198 | 64.64 178 | 82.53 142 | 35.06 253 | 85.50 79 | 55.71 154 | 69.91 224 | 86.67 46 |
|
TAPA-MVS | | 59.36 10 | 66.60 189 | 65.20 191 | 70.81 186 | 76.63 180 | 48.75 211 | 76.52 161 | 80.04 127 | 50.64 244 | 65.24 168 | 84.93 99 | 39.15 206 | 78.54 227 | 36.77 282 | 76.88 133 | 85.14 106 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
OMC-MVS | | | 71.40 88 | 70.60 87 | 73.78 96 | 76.60 181 | 53.15 131 | 79.74 95 | 79.78 129 | 58.37 133 | 68.75 113 | 86.45 74 | 45.43 144 | 80.60 194 | 62.58 108 | 77.73 122 | 87.58 23 |
|
LTVRE_ROB | | 55.42 16 | 63.15 227 | 61.23 237 | 68.92 211 | 76.57 182 | 47.80 221 | 59.92 317 | 76.39 195 | 54.35 201 | 58.67 257 | 82.46 144 | 29.44 304 | 81.49 178 | 42.12 256 | 71.14 197 | 77.46 253 |
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 |
QAPM | | | 70.05 113 | 68.81 119 | 73.78 96 | 76.54 183 | 53.43 127 | 83.23 38 | 83.48 49 | 52.89 212 | 65.90 158 | 86.29 77 | 41.55 184 | 86.49 57 | 51.01 188 | 78.40 118 | 81.42 197 |
|
FMVSNet3 | | | 66.32 194 | 65.61 185 | 68.46 216 | 76.48 184 | 42.34 266 | 74.98 193 | 77.15 189 | 55.83 175 | 65.04 171 | 81.16 179 | 39.91 196 | 80.14 201 | 47.18 213 | 72.76 176 | 82.90 177 |
|
thisisatest0530 | | | 67.92 166 | 65.78 182 | 74.33 87 | 76.29 185 | 51.03 156 | 76.89 155 | 74.25 221 | 53.67 207 | 65.59 162 | 81.76 162 | 35.15 252 | 85.50 79 | 55.94 149 | 72.47 180 | 86.47 49 |
|
ab-mvs | | | 66.65 188 | 66.42 172 | 67.37 224 | 76.17 186 | 41.73 271 | 70.41 259 | 76.14 199 | 53.99 204 | 65.98 156 | 83.51 127 | 49.48 81 | 76.24 257 | 48.60 207 | 73.46 162 | 84.14 143 |
|
Effi-MVS+-dtu | | | 69.64 124 | 67.53 142 | 75.95 57 | 76.10 187 | 62.29 18 | 80.20 87 | 76.06 201 | 59.83 100 | 65.26 167 | 77.09 264 | 41.56 182 | 84.02 118 | 60.60 130 | 71.09 199 | 81.53 195 |
|
mvs-test1 | | | 70.44 105 | 68.19 130 | 77.18 41 | 76.10 187 | 63.22 6 | 80.59 83 | 76.06 201 | 59.83 100 | 66.32 153 | 79.87 212 | 41.56 182 | 85.53 76 | 60.60 130 | 72.77 175 | 82.80 179 |
|
DTE-MVSNet | | | 65.58 201 | 65.34 188 | 66.31 234 | 76.06 189 | 34.79 319 | 76.43 162 | 79.38 149 | 62.55 51 | 61.66 220 | 83.83 119 | 45.60 140 | 79.15 219 | 41.64 262 | 60.88 299 | 85.00 111 |
|
EPNet | | | 73.09 64 | 72.16 66 | 75.90 58 | 75.95 190 | 56.28 90 | 83.05 40 | 72.39 234 | 66.53 13 | 65.27 166 | 87.00 59 | 50.40 74 | 85.47 81 | 62.48 110 | 86.32 43 | 85.94 68 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
SixPastTwentyTwo | | | 61.65 245 | 58.80 257 | 70.20 195 | 75.80 191 | 47.22 228 | 75.59 178 | 69.68 248 | 54.61 194 | 54.11 300 | 79.26 230 | 27.07 318 | 82.96 140 | 43.27 247 | 49.79 337 | 80.41 223 |
|
Baseline_NR-MVSNet | | | 67.05 179 | 67.56 140 | 65.50 255 | 75.65 192 | 37.70 301 | 75.42 181 | 74.65 217 | 59.90 96 | 68.14 124 | 83.15 133 | 49.12 101 | 77.20 246 | 52.23 181 | 69.78 227 | 81.60 194 |
|
jajsoiax | | | 68.25 158 | 66.45 170 | 73.66 104 | 75.62 193 | 55.49 108 | 80.82 79 | 78.51 166 | 52.33 218 | 64.33 181 | 84.11 114 | 28.28 309 | 81.81 173 | 63.48 103 | 70.62 202 | 83.67 159 |
|
mvs_tets | | | 68.18 162 | 66.36 174 | 73.63 107 | 75.61 194 | 55.35 111 | 80.77 80 | 78.56 164 | 52.48 217 | 64.27 183 | 84.10 115 | 27.45 315 | 81.84 172 | 63.45 104 | 70.56 205 | 83.69 156 |
|
PVSNet | | 50.76 19 | 58.40 269 | 57.39 267 | 61.42 282 | 75.53 195 | 44.04 254 | 61.43 310 | 63.45 300 | 47.04 276 | 56.91 276 | 73.61 297 | 27.00 319 | 64.76 308 | 39.12 271 | 72.40 185 | 75.47 276 |
|
MVS | | | 67.37 171 | 66.33 175 | 70.51 191 | 75.46 196 | 50.94 157 | 73.95 208 | 81.85 80 | 41.57 319 | 62.54 200 | 78.57 240 | 47.98 114 | 85.47 81 | 52.97 177 | 82.05 67 | 75.14 278 |
|
nrg030 | | | 72.96 65 | 73.01 60 | 72.84 135 | 75.41 197 | 50.24 181 | 80.02 88 | 82.89 67 | 58.36 134 | 74.44 34 | 86.73 62 | 58.90 12 | 80.83 189 | 65.84 73 | 74.46 148 | 87.44 27 |
|
thres200 | | | 62.20 238 | 61.16 238 | 65.34 257 | 75.38 198 | 39.99 279 | 69.60 265 | 69.29 255 | 55.64 180 | 61.87 215 | 76.99 265 | 37.07 228 | 78.96 226 | 31.28 314 | 73.28 165 | 77.06 260 |
|
TransMVSNet (Re) | | | 64.72 210 | 64.33 197 | 65.87 248 | 75.22 199 | 38.56 294 | 74.66 201 | 75.08 215 | 58.90 124 | 61.79 217 | 82.63 138 | 51.18 67 | 78.07 237 | 43.63 245 | 55.87 320 | 80.99 216 |
|
MS-PatchMatch | | | 62.42 236 | 61.46 234 | 65.31 258 | 75.21 200 | 52.10 146 | 72.05 239 | 74.05 223 | 46.41 279 | 57.42 274 | 74.36 292 | 34.35 263 | 77.57 243 | 45.62 229 | 73.67 157 | 66.26 333 |
|
IB-MVS | | 56.42 12 | 65.40 205 | 62.73 216 | 73.40 120 | 74.89 201 | 52.78 136 | 73.09 219 | 75.13 211 | 55.69 178 | 58.48 261 | 73.73 296 | 32.86 278 | 86.32 61 | 50.63 190 | 70.11 219 | 81.10 210 |
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 |
MVS_Test | | | 72.45 71 | 72.46 65 | 72.42 154 | 74.88 202 | 48.50 214 | 76.28 165 | 83.14 62 | 59.40 117 | 72.46 62 | 84.68 102 | 55.66 25 | 81.12 183 | 65.98 72 | 79.66 96 | 87.63 21 |
|
CANet_DTU | | | 68.18 162 | 67.71 139 | 69.59 202 | 74.83 203 | 46.24 235 | 78.66 106 | 76.85 193 | 59.60 109 | 63.45 188 | 82.09 155 | 35.25 251 | 77.41 244 | 59.88 135 | 78.76 113 | 85.14 106 |
|
tfpnnormal | | | 62.47 235 | 61.63 233 | 64.99 260 | 74.81 204 | 39.01 287 | 71.22 247 | 73.72 226 | 55.22 187 | 60.21 238 | 80.09 210 | 41.26 190 | 76.98 250 | 30.02 319 | 68.09 248 | 78.97 242 |
|
Vis-MVSNet (Re-imp) | | | 63.69 218 | 63.88 201 | 63.14 272 | 74.75 205 | 31.04 342 | 71.16 249 | 63.64 299 | 56.32 166 | 59.80 244 | 84.99 98 | 44.51 153 | 75.46 260 | 39.12 271 | 80.62 77 | 82.92 175 |
|
HY-MVS | | 56.14 13 | 64.55 214 | 63.89 200 | 66.55 232 | 74.73 206 | 41.02 275 | 69.96 263 | 74.43 218 | 49.29 253 | 61.66 220 | 80.92 188 | 47.43 124 | 76.68 252 | 44.91 237 | 71.69 193 | 81.94 190 |
|
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 52.97 17 | 61.27 248 | 58.81 250 | 68.64 214 | 74.63 207 | 52.51 141 | 78.42 113 | 73.30 228 | 49.92 250 | 50.96 315 | 81.51 171 | 23.06 334 | 79.40 209 | 31.63 309 | 65.85 261 | 74.01 298 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
LCM-MVSNet-Re | | | 61.88 243 | 61.35 235 | 63.46 267 | 74.58 208 | 31.48 341 | 61.42 311 | 58.14 323 | 58.71 127 | 53.02 309 | 79.55 225 | 43.07 166 | 76.80 251 | 45.69 227 | 77.96 121 | 82.11 189 |
|
test_djsdf | | | 69.45 129 | 67.74 136 | 74.58 83 | 74.57 209 | 54.92 114 | 82.79 46 | 78.48 167 | 51.26 238 | 65.41 164 | 83.49 128 | 38.37 213 | 83.24 133 | 66.06 69 | 69.25 234 | 85.56 83 |
|
EI-MVSNet | | | 69.27 132 | 68.44 127 | 71.73 165 | 74.47 210 | 49.39 205 | 75.20 187 | 78.45 169 | 59.60 109 | 69.16 110 | 76.51 274 | 51.29 64 | 82.50 160 | 59.86 137 | 71.45 196 | 83.30 165 |
|
CVMVSNet | | | 59.63 258 | 59.14 248 | 61.08 290 | 74.47 210 | 38.84 289 | 75.20 187 | 68.74 261 | 31.15 347 | 58.24 262 | 76.51 274 | 32.39 288 | 68.58 293 | 49.77 196 | 65.84 262 | 75.81 272 |
|
IterMVS-LS | | | 69.22 135 | 68.48 124 | 71.43 173 | 74.44 212 | 49.40 204 | 76.23 167 | 77.55 182 | 59.60 109 | 65.85 160 | 81.59 170 | 51.28 65 | 81.58 177 | 59.87 136 | 69.90 225 | 83.30 165 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
XVG-OURS-SEG-HR | | | 68.81 139 | 67.47 144 | 72.82 137 | 74.40 213 | 56.87 85 | 70.59 255 | 79.04 154 | 54.77 193 | 66.99 145 | 86.01 82 | 39.57 201 | 78.21 235 | 62.54 109 | 73.33 164 | 83.37 164 |
|
XVG-OURS | | | 68.76 142 | 67.37 147 | 72.90 132 | 74.32 214 | 57.22 77 | 70.09 262 | 78.81 158 | 55.24 186 | 67.79 135 | 85.81 88 | 36.54 240 | 78.28 234 | 62.04 120 | 75.74 141 | 83.19 169 |
|
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 61.03 9 | 68.85 138 | 67.56 140 | 72.70 140 | 74.26 215 | 53.99 120 | 81.21 76 | 81.34 92 | 52.70 213 | 62.75 195 | 85.55 92 | 38.86 209 | 84.14 113 | 48.41 209 | 83.01 58 | 79.97 228 |
|
MIMVSNet | | | 57.35 276 | 57.07 269 | 58.22 299 | 74.21 216 | 37.18 302 | 62.46 306 | 60.88 315 | 48.88 257 | 55.29 289 | 75.99 281 | 31.68 291 | 62.04 317 | 31.87 303 | 72.35 186 | 75.43 277 |
|
conf0.01 | | | 59.97 252 | 58.81 250 | 63.42 268 | 74.15 217 | 33.83 327 | 68.32 273 | 64.22 290 | 51.79 223 | 58.04 264 | 79.57 219 | 35.41 244 | 75.41 261 | 29.57 326 | 68.26 241 | 81.03 212 |
|
conf0.002 | | | 59.97 252 | 58.81 250 | 63.42 268 | 74.15 217 | 33.83 327 | 68.32 273 | 64.22 290 | 51.79 223 | 58.04 264 | 79.57 219 | 35.41 244 | 75.41 261 | 29.57 326 | 68.26 241 | 81.03 212 |
|
thresconf0.02 | | | 59.40 260 | 58.81 250 | 61.17 286 | 74.15 217 | 33.83 327 | 68.32 273 | 64.22 290 | 51.79 223 | 58.04 264 | 79.57 219 | 35.41 244 | 75.41 261 | 29.57 326 | 68.26 241 | 74.25 292 |
|
tfpn_n400 | | | 59.40 260 | 58.81 250 | 61.17 286 | 74.15 217 | 33.83 327 | 68.32 273 | 64.22 290 | 51.79 223 | 58.04 264 | 79.57 219 | 35.41 244 | 75.41 261 | 29.57 326 | 68.26 241 | 74.25 292 |
|
tfpnconf | | | 59.40 260 | 58.81 250 | 61.17 286 | 74.15 217 | 33.83 327 | 68.32 273 | 64.22 290 | 51.79 223 | 58.04 264 | 79.57 219 | 35.41 244 | 75.41 261 | 29.57 326 | 68.26 241 | 74.25 292 |
|
tfpnview11 | | | 59.40 260 | 58.81 250 | 61.17 286 | 74.15 217 | 33.83 327 | 68.32 273 | 64.22 290 | 51.79 223 | 58.04 264 | 79.57 219 | 35.41 244 | 75.41 261 | 29.57 326 | 68.26 241 | 74.25 292 |
|
Patchmatch-test1 | | | 59.75 256 | 58.00 265 | 64.98 261 | 74.14 223 | 48.06 219 | 63.35 303 | 63.23 302 | 49.13 255 | 59.33 250 | 71.46 305 | 37.45 222 | 69.59 288 | 41.39 263 | 62.57 287 | 77.30 255 |
|
tfpn_ndepth | | | 59.57 259 | 59.02 249 | 61.23 285 | 73.81 224 | 35.60 317 | 69.40 268 | 65.59 281 | 50.96 242 | 57.96 270 | 77.72 251 | 34.81 256 | 75.91 259 | 30.36 317 | 70.57 204 | 72.18 315 |
|
thisisatest0515 | | | 65.83 198 | 63.50 207 | 72.82 137 | 73.75 225 | 49.50 203 | 71.32 245 | 73.12 231 | 49.39 252 | 63.82 186 | 76.50 276 | 34.95 255 | 84.84 99 | 53.20 176 | 75.49 144 | 84.13 144 |
|
tfpn1000 | | | 59.24 265 | 58.70 258 | 60.86 291 | 73.75 225 | 33.99 325 | 68.86 271 | 63.98 297 | 51.25 240 | 57.29 275 | 79.51 227 | 34.58 258 | 75.26 267 | 29.08 333 | 69.99 221 | 73.32 301 |
|
K. test v3 | | | 60.47 250 | 57.11 268 | 70.56 190 | 73.74 227 | 48.22 216 | 75.10 190 | 62.55 307 | 58.27 135 | 53.62 305 | 76.31 277 | 27.81 312 | 81.59 176 | 47.42 211 | 39.18 349 | 81.88 192 |
|
v10 | | | 70.21 111 | 69.02 116 | 73.81 95 | 73.51 228 | 50.92 159 | 78.74 104 | 81.39 90 | 60.05 92 | 66.39 152 | 81.83 161 | 47.58 119 | 85.41 84 | 62.80 107 | 68.86 237 | 85.09 109 |
|
v13 | | | 68.29 154 | 66.84 161 | 72.63 141 | 73.50 229 | 50.83 162 | 78.25 115 | 79.58 140 | 60.05 92 | 60.76 234 | 77.68 253 | 49.11 104 | 82.77 151 | 62.17 117 | 60.45 307 | 84.30 131 |
|
v7 | | | 70.57 98 | 69.48 108 | 73.85 93 | 73.50 229 | 50.92 159 | 78.27 114 | 81.43 88 | 58.93 122 | 69.61 96 | 81.49 172 | 47.56 120 | 85.43 83 | 63.94 97 | 70.62 202 | 85.21 105 |
|
v12 | | | 68.28 155 | 66.83 163 | 72.60 143 | 73.43 231 | 50.74 165 | 78.18 117 | 79.59 138 | 60.01 94 | 60.89 233 | 77.66 254 | 49.12 101 | 82.77 151 | 62.18 115 | 60.46 306 | 84.29 132 |
|
v11 | | | 68.15 164 | 66.73 166 | 72.42 154 | 73.43 231 | 50.28 180 | 77.94 128 | 79.65 132 | 59.88 99 | 61.11 230 | 77.55 259 | 48.25 112 | 82.75 156 | 61.88 124 | 60.85 300 | 84.23 136 |
|
tpmp4_e23 | | | 62.71 234 | 60.13 243 | 70.45 192 | 73.40 233 | 48.39 215 | 72.82 222 | 69.49 252 | 44.88 291 | 59.91 241 | 74.99 287 | 37.79 220 | 81.47 179 | 40.22 266 | 67.71 253 | 81.48 196 |
|
V9 | | | 68.27 156 | 66.84 161 | 72.56 144 | 73.39 234 | 50.63 168 | 78.10 122 | 79.60 135 | 59.94 95 | 61.05 231 | 77.62 255 | 49.18 97 | 82.77 151 | 62.17 117 | 60.48 305 | 84.27 133 |
|
V14 | | | 68.25 158 | 66.82 164 | 72.52 147 | 73.33 235 | 50.53 173 | 78.02 125 | 79.60 135 | 59.83 100 | 61.16 229 | 77.57 258 | 49.19 96 | 82.77 151 | 62.18 115 | 60.50 304 | 84.26 134 |
|
v15 | | | 68.22 161 | 66.81 165 | 72.47 152 | 73.25 236 | 50.40 176 | 77.92 129 | 79.60 135 | 59.77 103 | 61.28 227 | 77.52 260 | 49.25 93 | 82.77 151 | 62.16 119 | 60.51 303 | 84.24 135 |
|
v1144 | | | 70.42 106 | 69.31 111 | 73.76 98 | 73.22 237 | 50.64 167 | 77.83 131 | 81.43 88 | 58.58 128 | 69.40 104 | 81.16 179 | 47.53 121 | 85.29 86 | 64.01 95 | 70.64 201 | 85.34 100 |
|
v17 | | | 68.37 152 | 67.00 158 | 72.48 148 | 73.22 237 | 50.31 178 | 78.10 122 | 79.58 140 | 59.71 104 | 61.67 219 | 77.60 256 | 49.31 83 | 82.89 143 | 62.37 112 | 61.48 296 | 84.23 136 |
|
v16 | | | 68.38 151 | 67.01 157 | 72.47 152 | 73.22 237 | 50.29 179 | 78.10 122 | 79.59 138 | 59.71 104 | 61.72 218 | 77.60 256 | 49.28 89 | 82.89 143 | 62.36 113 | 61.54 293 | 84.23 136 |
|
v1192 | | | 69.97 116 | 68.68 121 | 73.85 93 | 73.19 240 | 50.94 157 | 77.68 137 | 81.36 91 | 57.51 142 | 68.95 112 | 80.85 191 | 45.28 147 | 85.33 85 | 62.97 106 | 70.37 211 | 85.27 103 |
|
v1neww | | | 70.66 94 | 69.70 98 | 73.53 109 | 73.15 241 | 50.22 182 | 78.11 119 | 80.68 110 | 59.65 106 | 69.83 91 | 81.67 164 | 49.29 86 | 84.96 91 | 64.55 84 | 70.38 209 | 85.42 95 |
|
v7new | | | 70.66 94 | 69.70 98 | 73.53 109 | 73.15 241 | 50.22 182 | 78.11 119 | 80.68 110 | 59.65 106 | 69.83 91 | 81.67 164 | 49.29 86 | 84.96 91 | 64.55 84 | 70.38 209 | 85.42 95 |
|
v8 | | | 70.33 109 | 69.28 112 | 73.49 112 | 73.15 241 | 50.22 182 | 78.62 107 | 80.78 109 | 60.79 75 | 66.45 151 | 82.11 154 | 49.35 82 | 84.98 89 | 63.58 102 | 68.71 238 | 85.28 102 |
|
v6 | | | 70.66 94 | 69.70 98 | 73.53 109 | 73.14 244 | 50.21 185 | 78.11 119 | 80.67 112 | 59.65 106 | 69.82 93 | 81.65 166 | 49.29 86 | 84.96 91 | 64.55 84 | 70.39 208 | 85.42 95 |
|
v18 | | | 68.33 153 | 66.96 159 | 72.42 154 | 73.13 245 | 50.16 187 | 77.97 127 | 79.57 142 | 59.57 113 | 61.80 216 | 77.50 261 | 49.30 84 | 82.90 142 | 62.31 114 | 61.50 294 | 84.20 142 |
|
v144192 | | | 69.71 119 | 68.51 123 | 73.33 122 | 73.10 246 | 50.13 189 | 77.54 141 | 80.64 113 | 56.65 157 | 68.57 116 | 80.55 198 | 46.87 131 | 84.96 91 | 62.98 105 | 69.66 231 | 84.89 115 |
|
v1921920 | | | 69.47 128 | 68.17 131 | 73.36 121 | 73.06 247 | 50.10 190 | 77.39 143 | 80.56 117 | 56.58 163 | 68.59 114 | 80.37 201 | 44.72 150 | 84.98 89 | 62.47 111 | 69.82 226 | 85.00 111 |
|
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 59.84 255 | 58.24 261 | 64.65 263 | 73.05 248 | 46.70 233 | 69.42 267 | 62.18 309 | 47.55 271 | 58.88 255 | 71.96 303 | 34.49 261 | 69.16 290 | 42.99 251 | 63.60 279 | 78.07 247 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
v1240 | | | 69.24 134 | 67.91 134 | 73.25 127 | 73.02 249 | 49.82 195 | 77.21 148 | 80.54 118 | 56.43 165 | 68.34 120 | 80.51 199 | 43.33 165 | 84.99 87 | 62.03 121 | 69.77 229 | 84.95 114 |
|
Fast-Effi-MVS+-dtu | | | 67.37 171 | 65.33 189 | 73.48 113 | 72.94 250 | 57.78 69 | 77.47 142 | 76.88 192 | 57.60 141 | 61.97 213 | 76.85 268 | 39.31 203 | 80.49 195 | 54.72 162 | 70.28 217 | 82.17 188 |
|
v1141 | | | 70.50 101 | 69.53 103 | 73.41 118 | 72.92 251 | 50.00 192 | 77.69 134 | 80.60 114 | 59.50 114 | 69.60 97 | 81.43 173 | 49.24 95 | 84.77 100 | 64.48 88 | 70.30 215 | 85.46 91 |
|
divwei89l23v2f112 | | | 70.50 101 | 69.53 103 | 73.41 118 | 72.91 252 | 50.00 192 | 77.69 134 | 80.59 115 | 59.50 114 | 69.60 97 | 81.43 173 | 49.26 91 | 84.77 100 | 64.48 88 | 70.31 214 | 85.47 89 |
|
v1 | | | 70.50 101 | 69.53 103 | 73.42 117 | 72.91 252 | 50.00 192 | 77.69 134 | 80.59 115 | 59.50 114 | 69.59 99 | 81.42 175 | 49.26 91 | 84.77 100 | 64.49 87 | 70.30 215 | 85.47 89 |
|
EPNet_dtu | | | 61.90 241 | 61.97 226 | 61.68 280 | 72.89 254 | 39.78 281 | 75.85 176 | 65.62 280 | 55.09 188 | 54.56 295 | 79.36 228 | 37.59 221 | 67.02 299 | 39.80 270 | 76.95 132 | 78.25 245 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tpm2 | | | 62.07 240 | 60.10 244 | 67.99 219 | 72.79 255 | 43.86 255 | 71.05 251 | 66.85 274 | 43.14 310 | 62.77 193 | 75.39 285 | 38.32 214 | 80.80 190 | 41.69 259 | 68.88 236 | 79.32 237 |
|
MDTV_nov1_ep13 | | | | 57.00 270 | | 72.73 256 | 38.26 296 | 65.02 298 | 64.73 287 | 44.74 293 | 55.46 285 | 72.48 300 | 32.61 286 | 70.47 286 | 37.47 278 | 67.75 252 | |
|
MSDG | | | 61.81 244 | 59.23 247 | 69.55 205 | 72.64 257 | 52.63 138 | 70.45 258 | 75.81 203 | 51.38 236 | 53.70 303 | 76.11 278 | 29.52 302 | 81.08 185 | 37.70 277 | 65.79 263 | 74.93 283 |
|
gg-mvs-nofinetune | | | 57.86 274 | 56.43 275 | 62.18 278 | 72.62 258 | 35.35 318 | 66.57 284 | 56.33 332 | 50.65 243 | 57.64 272 | 57.10 347 | 30.65 294 | 76.36 255 | 37.38 279 | 78.88 109 | 74.82 285 |
|
v2v482 | | | 70.50 101 | 69.45 110 | 73.66 104 | 72.62 258 | 50.03 191 | 77.58 138 | 80.51 119 | 59.90 96 | 69.52 100 | 82.14 153 | 47.53 121 | 84.88 98 | 65.07 79 | 70.17 218 | 86.09 65 |
|
v7n | | | 69.01 137 | 67.36 148 | 73.98 91 | 72.51 260 | 52.65 137 | 78.54 110 | 81.30 93 | 60.26 89 | 62.67 196 | 81.62 167 | 43.61 162 | 84.49 107 | 57.01 145 | 68.70 239 | 84.79 119 |
|
pm-mvs1 | | | 65.24 206 | 64.97 195 | 66.04 243 | 72.38 261 | 39.40 285 | 72.62 227 | 75.63 204 | 55.53 181 | 62.35 211 | 83.18 132 | 47.45 123 | 76.47 254 | 49.06 204 | 66.54 258 | 82.24 186 |
|
XVG-ACMP-BASELINE | | | 64.36 215 | 62.23 223 | 70.74 188 | 72.35 262 | 52.45 143 | 70.80 254 | 78.45 169 | 53.84 206 | 59.87 242 | 81.10 181 | 16.24 343 | 79.32 211 | 55.64 156 | 71.76 192 | 80.47 221 |
|
WTY-MVS | | | 59.75 256 | 60.39 242 | 57.85 302 | 72.32 263 | 37.83 299 | 61.05 315 | 64.18 296 | 45.95 286 | 61.91 214 | 79.11 232 | 47.01 129 | 60.88 320 | 42.50 254 | 69.49 233 | 74.83 284 |
|
tpm cat1 | | | 59.25 264 | 56.95 271 | 66.15 240 | 72.19 264 | 46.96 230 | 68.09 280 | 65.76 279 | 40.03 328 | 57.81 271 | 70.56 312 | 38.32 214 | 74.51 272 | 38.26 275 | 61.50 294 | 77.00 262 |
|
PatchFormer-LS_test | | | 62.20 238 | 60.59 241 | 67.04 227 | 72.18 265 | 46.82 232 | 70.36 260 | 68.62 262 | 51.92 221 | 59.19 251 | 70.23 314 | 36.86 237 | 75.07 269 | 50.23 194 | 65.68 264 | 79.23 239 |
|
mvs_anonymous | | | 68.03 165 | 67.51 143 | 69.59 202 | 72.08 266 | 44.57 249 | 71.99 240 | 75.23 209 | 51.67 229 | 67.06 144 | 82.57 140 | 54.68 33 | 77.94 238 | 56.56 146 | 75.71 142 | 86.26 62 |
|
OurMVSNet-221017-0 | | | 61.37 247 | 58.63 260 | 69.61 201 | 72.05 267 | 48.06 219 | 73.93 211 | 72.51 233 | 47.23 274 | 54.74 292 | 80.92 188 | 21.49 338 | 81.24 181 | 48.57 208 | 56.22 319 | 79.53 235 |
|
semantic-postprocess | | | | | 65.40 256 | 71.99 268 | 50.80 164 | | 69.63 249 | 45.71 288 | 60.61 235 | 77.93 245 | 36.56 239 | 65.99 305 | 55.67 155 | 63.50 280 | 79.42 236 |
|
DWT-MVSNet_test | | | 61.90 241 | 59.93 245 | 67.83 220 | 71.98 269 | 46.09 236 | 71.03 252 | 69.71 246 | 50.09 247 | 58.51 260 | 70.62 311 | 30.21 298 | 77.63 241 | 49.28 202 | 67.91 249 | 79.78 232 |
|
CostFormer | | | 64.04 216 | 62.51 217 | 68.61 215 | 71.88 270 | 45.77 237 | 71.30 246 | 70.60 243 | 47.55 271 | 64.31 182 | 76.61 272 | 41.63 180 | 79.62 207 | 49.74 197 | 69.00 235 | 80.42 222 |
|
1314 | | | 64.61 213 | 63.21 210 | 68.80 212 | 71.87 271 | 47.46 226 | 73.95 208 | 78.39 173 | 42.88 312 | 59.97 240 | 76.60 273 | 38.11 217 | 79.39 210 | 54.84 161 | 72.32 187 | 79.55 234 |
|
tpm | | | 57.34 277 | 58.16 262 | 54.86 314 | 71.80 272 | 34.77 320 | 67.47 283 | 56.04 335 | 48.20 265 | 60.10 239 | 76.92 266 | 37.17 226 | 53.41 348 | 40.76 265 | 65.01 268 | 76.40 268 |
|
pmmvs4 | | | 61.48 246 | 59.39 246 | 67.76 221 | 71.57 273 | 53.86 121 | 71.42 243 | 65.34 282 | 44.20 300 | 59.46 246 | 77.92 246 | 35.90 241 | 74.71 271 | 43.87 243 | 64.87 269 | 74.71 287 |
|
AllTest | | | 57.08 279 | 54.65 286 | 64.39 264 | 71.44 274 | 49.03 206 | 69.92 264 | 67.30 270 | 45.97 284 | 47.16 326 | 79.77 215 | 17.47 340 | 67.56 296 | 33.65 297 | 59.16 311 | 76.57 266 |
|
TestCases | | | | | 64.39 264 | 71.44 274 | 49.03 206 | | 67.30 270 | 45.97 284 | 47.16 326 | 79.77 215 | 17.47 340 | 67.56 296 | 33.65 297 | 59.16 311 | 76.57 266 |
|
lessismore_v0 | | | | | 69.91 198 | 71.42 276 | 47.80 221 | | 50.90 349 | | 50.39 320 | 75.56 284 | 27.43 316 | 81.33 180 | 45.91 225 | 34.10 352 | 80.59 220 |
|
gm-plane-assit | | | | | | 71.40 277 | 41.72 272 | | | 48.85 258 | | 73.31 298 | | 82.48 162 | 48.90 205 | | |
|
GG-mvs-BLEND | | | | | 62.34 277 | 71.36 278 | 37.04 304 | 69.20 269 | 57.33 327 | | 54.73 293 | 65.48 333 | 30.37 295 | 77.82 239 | 34.82 293 | 74.93 146 | 72.17 316 |
|
diffmvs1 | | | 71.56 84 | 71.40 75 | 72.04 159 | 71.20 279 | 48.71 212 | 74.80 197 | 77.10 190 | 60.84 73 | 71.10 72 | 85.28 97 | 52.65 52 | 80.01 202 | 70.26 45 | 79.35 103 | 87.40 29 |
|
DI_MVS_plusplus_test | | | 69.35 130 | 68.03 133 | 73.30 124 | 71.11 280 | 50.14 188 | 75.49 180 | 79.16 152 | 54.57 197 | 62.45 204 | 80.76 194 | 44.67 152 | 84.20 111 | 64.23 91 | 79.81 92 | 85.54 84 |
|
FMVSNet5 | | | 55.86 286 | 54.93 284 | 58.66 298 | 71.05 281 | 36.35 312 | 64.18 302 | 62.48 308 | 46.76 277 | 50.66 319 | 74.73 290 | 25.80 325 | 64.04 310 | 33.11 299 | 65.57 265 | 75.59 275 |
|
GA-MVS | | | 65.53 202 | 63.70 204 | 71.02 185 | 70.87 282 | 48.10 218 | 70.48 257 | 74.40 219 | 56.69 156 | 64.70 177 | 76.77 269 | 33.66 270 | 81.10 184 | 55.42 158 | 70.32 213 | 83.87 150 |
|
test_normal | | | 69.26 133 | 67.90 135 | 73.32 123 | 70.84 283 | 50.38 177 | 75.30 183 | 79.17 151 | 54.23 202 | 62.00 212 | 80.61 197 | 44.69 151 | 83.89 121 | 64.33 90 | 79.95 91 | 85.69 78 |
|
pmmvs6 | | | 63.69 218 | 62.82 215 | 66.27 238 | 70.63 284 | 39.27 286 | 73.13 218 | 75.47 206 | 52.69 214 | 59.75 245 | 82.30 147 | 39.71 199 | 77.03 249 | 47.40 212 | 64.35 275 | 82.53 181 |
|
diffmvs | | | 70.36 107 | 69.99 95 | 71.46 170 | 70.48 285 | 48.19 217 | 74.59 203 | 76.30 196 | 60.36 87 | 67.75 137 | 83.81 122 | 51.22 66 | 79.77 203 | 67.92 57 | 77.50 125 | 86.42 50 |
|
OpenMVS_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 52.78 18 | 60.03 251 | 58.14 263 | 65.69 250 | 70.47 286 | 44.82 244 | 75.33 182 | 70.86 241 | 45.04 290 | 56.06 281 | 76.00 279 | 26.89 320 | 79.65 205 | 35.36 292 | 67.29 255 | 72.60 306 |
|
v148 | | | 68.24 160 | 67.19 155 | 71.40 174 | 70.43 287 | 47.77 223 | 75.76 177 | 77.03 191 | 58.91 123 | 67.36 140 | 80.10 209 | 48.60 109 | 81.89 170 | 60.01 134 | 66.52 259 | 84.53 125 |
|
XXY-MVS | | | 60.68 249 | 61.67 232 | 57.70 304 | 70.43 287 | 38.45 295 | 64.19 301 | 66.47 275 | 48.05 267 | 63.22 189 | 80.86 190 | 49.28 89 | 60.47 321 | 45.25 236 | 67.28 256 | 74.19 296 |
|
MVSTER | | | 67.16 176 | 65.58 186 | 71.88 162 | 70.37 289 | 49.70 198 | 70.25 261 | 78.45 169 | 51.52 233 | 69.16 110 | 80.37 201 | 38.45 212 | 82.50 160 | 60.19 133 | 71.46 195 | 83.44 163 |
|
tpmvs | | | 58.47 268 | 56.95 271 | 63.03 274 | 70.20 290 | 41.21 274 | 67.90 282 | 67.23 272 | 49.62 251 | 54.73 293 | 70.84 309 | 34.14 264 | 76.24 257 | 36.64 286 | 61.29 297 | 71.64 318 |
|
anonymousdsp | | | 67.00 181 | 64.82 196 | 73.57 108 | 70.09 291 | 56.13 94 | 76.35 163 | 77.35 187 | 48.43 262 | 64.99 174 | 80.84 192 | 33.01 276 | 80.34 196 | 64.66 81 | 67.64 254 | 84.23 136 |
|
MIMVSNet1 | | | 55.17 290 | 54.31 290 | 57.77 303 | 70.03 292 | 32.01 339 | 65.68 290 | 64.81 285 | 49.19 254 | 46.75 329 | 76.00 279 | 25.53 327 | 64.04 310 | 28.65 334 | 62.13 290 | 77.26 258 |
|
CR-MVSNet | | | 59.91 254 | 57.90 266 | 65.96 244 | 69.96 293 | 52.07 147 | 65.31 295 | 63.15 303 | 42.48 314 | 59.36 247 | 74.84 288 | 35.83 242 | 70.75 284 | 45.50 231 | 64.65 273 | 75.06 279 |
|
RPMNet | | | 58.70 267 | 56.29 277 | 65.96 244 | 69.96 293 | 52.07 147 | 65.31 295 | 62.15 310 | 43.20 309 | 59.36 247 | 70.15 316 | 35.37 250 | 70.75 284 | 36.42 289 | 64.65 273 | 75.06 279 |
|
v748 | | | 67.26 173 | 65.67 183 | 72.02 160 | 69.90 295 | 49.77 197 | 76.24 166 | 79.57 142 | 58.58 128 | 60.49 237 | 80.38 200 | 44.47 156 | 82.17 167 | 56.16 148 | 65.26 267 | 84.12 145 |
|
Anonymous20231206 | | | 55.10 291 | 55.30 283 | 54.48 316 | 69.81 296 | 33.94 326 | 62.91 305 | 62.13 311 | 41.08 320 | 55.18 290 | 75.65 283 | 32.75 282 | 56.59 337 | 30.32 318 | 67.86 250 | 72.91 303 |
|
our_test_3 | | | 56.49 280 | 54.42 288 | 62.68 276 | 69.51 297 | 45.48 241 | 66.08 288 | 61.49 313 | 44.11 303 | 50.73 318 | 69.60 319 | 33.05 275 | 68.15 294 | 38.38 274 | 56.86 316 | 74.40 289 |
|
ppachtmachnet_test | | | 58.06 273 | 55.38 282 | 66.10 242 | 69.51 297 | 48.99 209 | 68.01 281 | 66.13 278 | 44.50 297 | 54.05 301 | 70.74 310 | 32.09 290 | 72.34 278 | 36.68 285 | 56.71 318 | 76.99 264 |
|
IterMVS | | | 62.79 229 | 61.27 236 | 67.35 225 | 69.37 299 | 52.04 149 | 71.17 248 | 68.24 264 | 52.63 215 | 59.82 243 | 76.91 267 | 37.32 223 | 72.36 277 | 52.80 178 | 63.19 283 | 77.66 251 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Patchmtry | | | 57.16 278 | 56.47 274 | 59.23 293 | 69.17 300 | 34.58 322 | 62.98 304 | 63.15 303 | 44.53 296 | 56.83 277 | 74.84 288 | 35.83 242 | 68.71 292 | 40.03 268 | 60.91 298 | 74.39 290 |
|
V42 | | | 68.65 143 | 67.35 149 | 72.56 144 | 68.93 301 | 50.18 186 | 72.90 221 | 79.47 145 | 56.92 150 | 69.45 103 | 80.26 207 | 46.29 135 | 82.99 139 | 64.07 93 | 67.82 251 | 84.53 125 |
|
Test4 | | | 67.77 169 | 65.97 179 | 73.19 128 | 68.64 302 | 50.58 169 | 74.80 197 | 80.48 120 | 54.13 203 | 59.11 252 | 79.07 233 | 33.89 268 | 83.12 137 | 63.61 101 | 79.98 90 | 85.87 70 |
|
test-LLR | | | 58.15 272 | 58.13 264 | 58.22 299 | 68.57 303 | 44.80 245 | 65.46 292 | 57.92 324 | 50.08 248 | 55.44 286 | 69.82 317 | 32.62 284 | 57.44 331 | 49.66 199 | 73.62 158 | 72.41 311 |
|
test-mter | | | 56.42 282 | 55.82 279 | 58.22 299 | 68.57 303 | 44.80 245 | 65.46 292 | 57.92 324 | 39.94 329 | 55.44 286 | 69.82 317 | 21.92 337 | 57.44 331 | 49.66 199 | 73.62 158 | 72.41 311 |
|
MVS-HIRNet | | | 45.52 317 | 44.48 320 | 48.65 332 | 68.49 305 | 34.05 324 | 59.41 320 | 44.50 359 | 27.03 351 | 37.96 350 | 50.47 354 | 26.16 324 | 64.10 309 | 26.74 339 | 59.52 309 | 47.82 352 |
|
dp | | | 51.89 303 | 51.60 301 | 52.77 323 | 68.44 306 | 32.45 337 | 62.36 307 | 54.57 340 | 44.16 301 | 49.31 322 | 67.91 322 | 28.87 308 | 56.61 336 | 33.89 296 | 54.89 323 | 69.24 330 |
|
PatchT | | | 53.17 299 | 53.44 296 | 52.33 325 | 68.29 307 | 25.34 356 | 58.21 322 | 54.41 341 | 44.46 298 | 54.56 295 | 69.05 320 | 33.32 273 | 60.94 319 | 36.93 281 | 61.76 292 | 70.73 324 |
|
Patchmatch-RL test | | | 58.16 271 | 55.49 281 | 66.15 240 | 67.92 308 | 48.89 210 | 60.66 316 | 51.07 348 | 47.86 269 | 59.36 247 | 62.71 340 | 34.02 266 | 72.27 279 | 56.41 147 | 59.40 310 | 77.30 255 |
|
pmmvs-eth3d | | | 58.81 266 | 56.31 276 | 66.30 235 | 67.61 309 | 52.42 144 | 72.30 232 | 64.76 286 | 43.55 306 | 54.94 291 | 74.19 294 | 28.95 306 | 72.60 276 | 43.31 246 | 57.21 315 | 73.88 299 |
|
PVSNet_0 | | 43.31 20 | 47.46 314 | 45.64 315 | 52.92 322 | 67.60 310 | 44.65 247 | 54.06 332 | 54.64 339 | 41.59 318 | 46.15 330 | 58.75 346 | 30.99 292 | 58.66 327 | 32.18 301 | 24.81 355 | 55.46 349 |
|
CHOSEN 280x420 | | | 47.83 312 | 46.36 313 | 52.24 326 | 67.37 311 | 49.78 196 | 38.91 357 | 43.11 360 | 35.00 341 | 43.27 341 | 63.30 339 | 28.95 306 | 49.19 353 | 36.53 287 | 60.80 301 | 57.76 347 |
|
tpmrst | | | 58.24 270 | 58.70 258 | 56.84 306 | 66.97 312 | 34.32 323 | 69.57 266 | 61.14 314 | 47.17 275 | 58.58 259 | 71.60 304 | 41.28 189 | 60.41 322 | 49.20 203 | 62.84 285 | 75.78 273 |
|
sss | | | 56.17 285 | 56.57 273 | 54.96 313 | 66.93 313 | 36.32 314 | 57.94 323 | 61.69 312 | 41.67 317 | 58.64 258 | 75.32 286 | 38.72 210 | 56.25 340 | 42.04 257 | 66.19 260 | 72.31 314 |
|
TinyColmap | | | 54.14 292 | 51.72 300 | 61.40 283 | 66.84 314 | 41.97 268 | 66.52 285 | 68.51 263 | 44.81 292 | 42.69 343 | 75.77 282 | 11.66 353 | 72.94 275 | 31.96 302 | 56.77 317 | 69.27 329 |
|
v52 | | | 67.09 177 | 65.16 192 | 72.87 133 | 66.77 315 | 51.60 152 | 73.69 214 | 79.45 147 | 57.88 139 | 62.46 203 | 78.57 240 | 40.95 193 | 83.34 129 | 61.99 122 | 64.70 272 | 83.68 157 |
|
V4 | | | 67.09 177 | 65.16 192 | 72.87 133 | 66.76 316 | 51.60 152 | 73.69 214 | 79.45 147 | 57.88 139 | 62.45 204 | 78.58 239 | 40.96 192 | 83.34 129 | 61.99 122 | 64.71 270 | 83.68 157 |
|
TESTMET0.1,1 | | | 55.28 289 | 54.90 285 | 56.42 307 | 66.56 317 | 43.67 257 | 65.46 292 | 56.27 333 | 39.18 331 | 53.83 302 | 67.44 325 | 24.21 332 | 55.46 345 | 48.04 210 | 73.11 170 | 70.13 325 |
|
MDA-MVSNet-bldmvs | | | 53.87 295 | 50.81 302 | 63.05 273 | 66.25 318 | 48.58 213 | 56.93 326 | 63.82 298 | 48.09 266 | 41.22 344 | 70.48 313 | 30.34 296 | 68.00 295 | 34.24 295 | 45.92 344 | 72.57 307 |
|
ITE_SJBPF | | | | | 62.09 279 | 66.16 319 | 44.55 250 | | 64.32 289 | 47.36 273 | 55.31 288 | 80.34 203 | 19.27 339 | 62.68 315 | 36.29 290 | 62.39 289 | 79.04 240 |
|
EPMVS | | | 53.96 293 | 53.69 294 | 54.79 315 | 66.12 320 | 31.96 340 | 62.34 308 | 49.05 351 | 44.42 299 | 55.54 284 | 71.33 307 | 30.22 297 | 56.70 335 | 41.65 261 | 62.54 288 | 75.71 274 |
|
testing_2 | | | 66.02 196 | 63.77 203 | 72.76 139 | 66.03 321 | 50.48 175 | 72.93 220 | 80.36 123 | 54.41 200 | 54.25 299 | 76.76 270 | 30.89 293 | 83.16 136 | 64.19 92 | 74.08 153 | 84.65 122 |
|
ADS-MVSNet2 | | | 51.33 305 | 48.76 308 | 59.07 295 | 66.02 322 | 44.60 248 | 50.90 341 | 59.76 318 | 36.90 336 | 50.74 316 | 66.18 331 | 26.38 321 | 63.11 312 | 27.17 336 | 54.76 324 | 69.50 327 |
|
ADS-MVSNet | | | 48.48 311 | 47.77 310 | 50.63 328 | 66.02 322 | 29.92 344 | 50.90 341 | 50.87 350 | 36.90 336 | 50.74 316 | 66.18 331 | 26.38 321 | 52.47 350 | 27.17 336 | 54.76 324 | 69.50 327 |
|
EU-MVSNet | | | 55.61 288 | 54.41 289 | 59.19 294 | 65.41 324 | 33.42 334 | 72.44 230 | 71.91 237 | 28.81 349 | 51.27 313 | 73.87 295 | 24.76 330 | 69.08 291 | 43.04 250 | 58.20 314 | 75.06 279 |
|
RPSCF | | | 55.80 287 | 54.22 292 | 60.53 292 | 65.13 325 | 42.91 264 | 64.30 300 | 57.62 326 | 36.84 338 | 58.05 263 | 82.28 148 | 28.01 310 | 56.24 341 | 37.14 280 | 58.61 313 | 82.44 185 |
|
USDC | | | 56.35 283 | 54.24 291 | 62.69 275 | 64.74 326 | 40.31 278 | 65.05 297 | 73.83 225 | 43.93 304 | 47.58 324 | 77.71 252 | 15.36 345 | 75.05 270 | 38.19 276 | 61.81 291 | 72.70 305 |
|
JIA-IIPM | | | 51.56 304 | 47.68 312 | 63.21 271 | 64.61 327 | 50.73 166 | 47.71 346 | 58.77 321 | 42.90 311 | 48.46 323 | 51.72 351 | 24.97 329 | 70.24 287 | 36.06 291 | 53.89 327 | 68.64 331 |
|
Patchmatch-test | | | 49.08 309 | 48.28 309 | 51.50 327 | 64.40 328 | 30.85 343 | 45.68 349 | 48.46 354 | 35.60 340 | 46.10 332 | 72.10 302 | 34.47 262 | 46.37 354 | 27.08 338 | 60.65 302 | 77.27 257 |
|
TDRefinement | | | 53.44 297 | 50.72 303 | 61.60 281 | 64.31 329 | 46.96 230 | 70.89 253 | 65.27 284 | 41.78 315 | 44.61 335 | 77.98 243 | 11.52 354 | 66.36 302 | 28.57 335 | 51.59 332 | 71.49 319 |
|
N_pmnet | | | 39.35 328 | 40.28 326 | 36.54 345 | 63.76 330 | 1.62 371 | 49.37 344 | 0.76 373 | 34.62 342 | 43.61 340 | 66.38 330 | 26.25 323 | 42.57 360 | 26.02 342 | 51.77 331 | 65.44 334 |
|
ambc | | | | | 65.13 259 | 63.72 331 | 37.07 303 | 47.66 347 | 78.78 159 | | 54.37 298 | 71.42 306 | 11.24 355 | 80.94 186 | 45.64 228 | 53.85 328 | 77.38 254 |
|
LP | | | 48.51 310 | 45.51 316 | 57.52 305 | 62.86 332 | 44.53 251 | 52.38 338 | 59.84 317 | 38.11 333 | 42.81 342 | 61.02 341 | 23.23 333 | 63.02 313 | 24.10 343 | 45.24 345 | 65.02 336 |
|
test0.0.03 1 | | | 53.32 298 | 53.59 295 | 52.50 324 | 62.81 333 | 29.45 345 | 59.51 318 | 54.11 343 | 50.08 248 | 54.40 297 | 74.31 293 | 32.62 284 | 55.92 342 | 30.50 316 | 63.95 277 | 72.15 317 |
|
PMMVS | | | 53.96 293 | 53.26 297 | 56.04 308 | 62.60 334 | 50.92 159 | 61.17 314 | 56.09 334 | 32.81 344 | 53.51 307 | 66.84 327 | 34.04 265 | 59.93 324 | 44.14 240 | 68.18 247 | 57.27 348 |
|
PM-MVS | | | 52.33 301 | 50.19 304 | 58.75 297 | 62.10 335 | 45.14 243 | 65.75 289 | 40.38 361 | 43.60 305 | 53.52 306 | 72.65 299 | 9.16 359 | 65.87 306 | 50.41 191 | 54.18 326 | 65.24 335 |
|
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 34.77 333 | 31.91 336 | 43.33 340 | 62.05 336 | 37.87 298 | 20.39 362 | 67.03 273 | 23.23 355 | 18.41 360 | 25.84 360 | 4.24 365 | 62.73 314 | 14.71 357 | 51.32 333 | 29.38 360 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
test20.03 | | | 53.87 295 | 54.02 293 | 53.41 320 | 61.47 337 | 28.11 348 | 61.30 312 | 59.21 319 | 51.34 237 | 52.09 311 | 77.43 262 | 33.29 274 | 58.55 328 | 29.76 324 | 60.27 308 | 73.58 300 |
|
pmmvs5 | | | 56.47 281 | 55.68 280 | 58.86 296 | 61.41 338 | 36.71 310 | 66.37 286 | 62.75 306 | 40.38 326 | 53.70 303 | 76.62 271 | 34.56 259 | 67.05 298 | 40.02 269 | 65.27 266 | 72.83 304 |
|
MDA-MVSNet_test_wron | | | 50.71 307 | 48.95 306 | 56.00 310 | 61.17 339 | 41.84 269 | 51.90 340 | 56.45 330 | 40.96 321 | 44.79 334 | 67.84 323 | 30.04 300 | 55.07 347 | 36.71 284 | 50.69 335 | 71.11 323 |
|
YYNet1 | | | 50.73 306 | 48.96 305 | 56.03 309 | 61.10 340 | 41.78 270 | 51.94 339 | 56.44 331 | 40.94 322 | 44.84 333 | 67.80 324 | 30.08 299 | 55.08 346 | 36.77 282 | 50.71 334 | 71.22 320 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 42.80 21 | 57.81 275 | 55.97 278 | 63.32 270 | 60.98 341 | 47.38 227 | 64.66 299 | 69.50 251 | 32.06 346 | 46.83 328 | 77.80 249 | 29.50 303 | 71.36 281 | 48.68 206 | 73.75 156 | 71.21 321 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
UnsupCasMVSNet_bld | | | 50.07 308 | 48.87 307 | 53.66 318 | 60.97 342 | 33.67 333 | 57.62 324 | 64.56 288 | 39.47 330 | 47.38 325 | 64.02 336 | 27.47 314 | 59.32 325 | 34.69 294 | 43.68 347 | 67.98 332 |
|
testpf | | | 44.11 321 | 45.40 317 | 40.26 343 | 60.52 343 | 27.34 350 | 33.26 359 | 54.33 342 | 45.87 287 | 41.08 345 | 60.26 343 | 16.46 342 | 59.14 326 | 46.09 222 | 50.68 336 | 34.31 358 |
|
testgi | | | 51.90 302 | 52.37 299 | 50.51 329 | 60.39 344 | 23.55 359 | 58.42 321 | 58.15 322 | 49.03 256 | 51.83 312 | 79.21 231 | 22.39 335 | 55.59 343 | 29.24 332 | 62.64 286 | 72.40 313 |
|
test2356 | | | 45.61 316 | 44.66 319 | 48.47 333 | 60.15 345 | 28.08 349 | 52.44 337 | 52.83 347 | 38.01 334 | 46.13 331 | 60.98 342 | 15.08 346 | 55.54 344 | 20.43 352 | 55.85 321 | 61.78 340 |
|
test1235678 | | | 45.66 315 | 44.46 321 | 49.26 330 | 59.88 346 | 28.68 347 | 56.36 328 | 55.54 338 | 39.12 332 | 40.89 346 | 63.40 338 | 14.41 347 | 57.32 333 | 21.05 349 | 49.47 339 | 61.78 340 |
|
testus | | | 44.59 319 | 43.87 322 | 46.76 335 | 59.85 347 | 24.65 357 | 53.86 333 | 55.82 336 | 36.26 339 | 43.97 339 | 63.42 337 | 8.39 360 | 53.14 349 | 20.70 351 | 52.52 330 | 62.51 338 |
|
UnsupCasMVSNet_eth | | | 53.16 300 | 52.47 298 | 55.23 311 | 59.45 348 | 33.39 335 | 59.43 319 | 69.13 256 | 45.98 283 | 50.35 321 | 72.32 301 | 29.30 305 | 58.26 329 | 42.02 258 | 44.30 346 | 74.05 297 |
|
new-patchmatchnet | | | 47.56 313 | 47.73 311 | 47.06 334 | 58.81 349 | 9.37 367 | 48.78 345 | 59.21 319 | 43.28 307 | 44.22 336 | 68.66 321 | 25.67 326 | 57.20 334 | 31.57 311 | 49.35 340 | 74.62 288 |
|
FPMVS | | | 42.18 324 | 41.11 325 | 45.39 336 | 58.03 350 | 41.01 276 | 49.50 343 | 53.81 345 | 30.07 348 | 33.71 351 | 64.03 334 | 11.69 352 | 52.08 351 | 14.01 358 | 55.11 322 | 43.09 355 |
|
1111 | | | 44.40 320 | 45.00 318 | 42.61 341 | 57.55 351 | 17.33 364 | 53.82 335 | 57.05 328 | 40.78 323 | 44.11 337 | 66.57 328 | 13.37 348 | 45.77 355 | 22.15 345 | 49.58 338 | 64.73 337 |
|
.test1245 | | | 34.88 332 | 39.49 328 | 21.04 353 | 57.55 351 | 17.33 364 | 53.82 335 | 57.05 328 | 40.78 323 | 44.11 337 | 66.57 328 | 13.37 348 | 45.77 355 | 22.15 345 | 0.00 366 | 0.03 367 |
|
testmv | | | 42.25 323 | 40.11 327 | 48.66 331 | 53.23 353 | 27.02 351 | 56.62 327 | 55.74 337 | 37.25 335 | 33.10 352 | 59.52 345 | 7.78 361 | 56.58 338 | 19.61 353 | 38.13 351 | 62.40 339 |
|
new_pmnet | | | 34.13 335 | 34.29 334 | 33.64 346 | 52.63 354 | 18.23 363 | 44.43 353 | 33.90 364 | 22.81 356 | 30.89 353 | 53.18 349 | 10.48 357 | 35.72 364 | 20.77 350 | 39.51 348 | 46.98 353 |
|
pmmvs3 | | | 44.92 318 | 41.95 324 | 53.86 317 | 52.58 355 | 43.55 258 | 62.11 309 | 46.90 358 | 26.05 353 | 40.63 347 | 60.19 344 | 11.08 356 | 57.91 330 | 31.83 308 | 46.15 343 | 60.11 344 |
|
DSMNet-mixed | | | 39.30 329 | 38.72 329 | 41.03 342 | 51.22 356 | 19.66 361 | 45.53 350 | 31.35 366 | 15.83 361 | 39.80 349 | 67.42 326 | 22.19 336 | 45.13 357 | 22.43 344 | 52.69 329 | 58.31 346 |
|
test12356 | | | 36.16 331 | 35.94 332 | 36.83 344 | 50.82 357 | 8.52 368 | 44.84 352 | 53.49 346 | 32.72 345 | 30.11 354 | 55.08 348 | 7.11 363 | 49.47 352 | 16.60 355 | 32.68 353 | 52.50 350 |
|
no-one | | | 40.85 326 | 36.09 331 | 55.14 312 | 48.55 358 | 38.72 290 | 42.15 355 | 62.92 305 | 34.60 343 | 23.55 357 | 49.74 355 | 12.21 351 | 66.16 304 | 26.27 341 | 24.84 354 | 60.54 343 |
|
LF4IMVS | | | 42.95 322 | 42.26 323 | 45.04 337 | 48.30 359 | 32.50 336 | 54.80 330 | 48.49 353 | 28.03 350 | 40.51 348 | 70.16 315 | 9.24 358 | 43.89 358 | 31.63 309 | 49.18 341 | 58.72 345 |
|
PNet_i23d | | | 27.88 338 | 25.99 338 | 33.55 347 | 47.54 360 | 25.89 353 | 47.24 348 | 32.91 365 | 21.44 358 | 15.90 361 | 38.09 357 | 0.85 371 | 42.76 359 | 16.90 354 | 13.03 361 | 32.00 359 |
|
wuyk23d | | | 13.32 344 | 12.52 345 | 15.71 354 | 47.54 360 | 26.27 352 | 31.06 361 | 1.98 372 | 4.93 365 | 5.18 368 | 1.94 368 | 0.45 372 | 18.54 366 | 6.81 365 | 12.83 362 | 2.33 365 |
|
LCM-MVSNet | | | 40.30 327 | 35.88 333 | 53.57 319 | 42.24 362 | 29.15 346 | 45.21 351 | 60.53 316 | 22.23 357 | 28.02 355 | 50.98 353 | 3.72 367 | 61.78 318 | 31.22 315 | 38.76 350 | 69.78 326 |
|
E-PMN | | | 23.77 340 | 22.73 341 | 26.90 351 | 42.02 363 | 20.67 360 | 42.66 354 | 35.70 362 | 17.43 359 | 10.28 365 | 25.05 361 | 6.42 364 | 42.39 361 | 10.28 361 | 14.71 358 | 17.63 361 |
|
EMVS | | | 22.97 341 | 21.84 343 | 26.36 352 | 40.20 364 | 19.53 362 | 41.95 356 | 34.64 363 | 17.09 360 | 9.73 366 | 22.83 363 | 7.29 362 | 42.22 362 | 9.18 363 | 13.66 360 | 17.32 362 |
|
ANet_high | | | 41.38 325 | 37.47 330 | 53.11 321 | 39.73 365 | 24.45 358 | 56.94 325 | 69.69 247 | 47.65 270 | 26.04 356 | 52.32 350 | 12.44 350 | 62.38 316 | 21.80 348 | 10.61 363 | 72.49 308 |
|
PMMVS2 | | | 27.40 339 | 25.91 339 | 31.87 349 | 39.46 366 | 6.57 369 | 31.17 360 | 28.52 367 | 23.96 354 | 20.45 359 | 48.94 356 | 4.20 366 | 37.94 363 | 16.51 356 | 19.97 356 | 51.09 351 |
|
PMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 28.69 22 | 36.22 330 | 33.29 335 | 45.02 338 | 36.82 367 | 35.98 316 | 54.68 331 | 48.74 352 | 26.31 352 | 21.02 358 | 51.61 352 | 2.88 369 | 60.10 323 | 9.99 362 | 47.58 342 | 38.99 357 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
wuykxyi23d | | | 28.12 337 | 22.54 342 | 44.87 339 | 34.97 368 | 32.11 338 | 37.96 358 | 47.31 356 | 13.32 362 | 9.29 367 | 23.72 362 | 0.45 372 | 56.58 338 | 21.85 347 | 13.98 359 | 45.93 354 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 17.77 23 | 21.41 342 | 17.77 344 | 32.34 348 | 34.34 369 | 25.44 355 | 16.11 363 | 24.11 368 | 11.19 363 | 13.22 363 | 31.92 358 | 1.58 370 | 30.95 365 | 10.47 360 | 17.03 357 | 40.62 356 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | 12.03 355 | 17.97 370 | 10.91 366 | | 10.60 371 | 7.46 364 | 11.07 364 | 28.36 359 | 3.28 368 | 11.29 367 | 8.01 364 | 9.74 365 | 13.89 363 |
|
tmp_tt | | | 9.43 345 | 11.14 346 | 4.30 356 | 2.38 371 | 4.40 370 | 13.62 364 | 16.08 370 | 0.39 366 | 15.89 362 | 13.06 364 | 15.80 344 | 5.54 368 | 12.63 359 | 10.46 364 | 2.95 364 |
|
testmvs | | | 4.52 348 | 6.03 349 | 0.01 358 | 0.01 372 | 0.00 373 | 53.86 333 | 0.00 374 | 0.01 367 | 0.04 369 | 0.27 369 | 0.00 375 | 0.00 369 | 0.04 366 | 0.00 366 | 0.03 367 |
|
test123 | | | 4.73 347 | 6.30 348 | 0.02 357 | 0.01 372 | 0.01 372 | 56.36 328 | 0.00 374 | 0.01 367 | 0.04 369 | 0.21 370 | 0.01 374 | 0.00 369 | 0.03 367 | 0.00 366 | 0.04 366 |
|
cdsmvs_eth3d_5k | | | 17.50 343 | 23.34 340 | 0.00 359 | 0.00 374 | 0.00 373 | 0.00 365 | 78.63 162 | 0.00 369 | 0.00 371 | 82.18 149 | 49.25 93 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
pcd_1.5k_mvsjas | | | 3.92 349 | 5.23 350 | 0.00 359 | 0.00 374 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 369 | 0.00 371 | 0.00 371 | 47.05 126 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
sosnet-low-res | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 375 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
sosnet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 375 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
uncertanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 375 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
Regformer | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 375 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
ab-mvs-re | | | 6.49 346 | 8.65 347 | 0.00 359 | 0.00 374 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 369 | 0.00 371 | 77.89 247 | 0.00 375 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
uanet | | | 0.00 350 | 0.00 351 | 0.00 359 | 0.00 374 | 0.00 373 | 0.00 365 | 0.00 374 | 0.00 369 | 0.00 371 | 0.00 371 | 0.00 375 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
GSMVS | | | | | | | | | | | | | | | | | 78.05 248 |
|
test_part1 | | | | | 0.00 359 | | 0.00 373 | 0.00 365 | 86.64 3 | | | | 0.00 375 | 0.00 369 | 0.00 368 | 0.00 366 | 0.00 369 |
|
sam_mvs1 | | | | | | | | | | | | | 34.74 257 | | | | 78.05 248 |
|
sam_mvs | | | | | | | | | | | | | 33.43 272 | | | | |
|
MTGPA | ![Method available as binary. binary](img/icon_binary.png) | | | | | | | | 80.97 105 | | | | | | | | |
|
test_post1 | | | | | | | | 68.67 272 | | | | 3.64 366 | 32.39 288 | 69.49 289 | 44.17 239 | | |
|
test_post | | | | | | | | | | | | 3.55 367 | 33.90 267 | 66.52 301 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 64.03 334 | 34.50 260 | 74.27 273 | | | |
|
MTMP | | | | | | | | 86.03 10 | 17.08 369 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 75.28 16 | 88.31 19 | 83.81 151 |
|
agg_prior2 | | | | | | | | | | | | | | | 73.09 32 | 87.93 26 | 84.33 128 |
|
test_prior4 | | | | | | | 62.51 17 | 82.08 63 | | | | | | | | | |
|
test_prior2 | | | | | | | | 81.75 65 | | 60.37 83 | 75.01 25 | 89.06 37 | 56.22 21 | | 72.19 35 | 88.96 10 | |
|
旧先验2 | | | | | | | | 76.08 170 | | 45.32 289 | 76.55 17 | | | 65.56 307 | 58.75 139 | | |
|
新几何2 | | | | | | | | 76.12 168 | | | | | | | | | |
|
无先验 | | | | | | | | 79.66 96 | 74.30 220 | 48.40 263 | | | | 80.78 191 | 53.62 170 | | 79.03 241 |
|
原ACMM2 | | | | | | | | 79.02 102 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 72.18 280 | 46.95 217 | | |
|
segment_acmp | | | | | | | | | | | | | 54.23 37 | | | | |
|
testdata1 | | | | | | | | 72.65 223 | | 60.50 80 | | | | | | | |
|
plane_prior5 | | | | | | | | | 84.01 34 | | | | | 87.21 36 | 68.16 54 | 80.58 79 | 84.65 122 |
|
plane_prior4 | | | | | | | | | | | | 86.10 80 | | | | | |
|
plane_prior3 | | | | | | | 56.09 95 | | | 63.92 30 | 69.27 106 | | | | | | |
|
plane_prior2 | | | | | | | | 84.22 26 | | 64.52 24 | | | | | | | |
|
plane_prior | | | | | | | 56.31 88 | 83.58 35 | | 63.19 40 | | | | | | 80.48 82 | |
|
n2 | | | | | | | | | 0.00 374 | | | | | | | | |
|
nn | | | | | | | | | 0.00 374 | | | | | | | | |
|
door-mid | | | | | | | | | 47.19 357 | | | | | | | | |
|
test11 | | | | | | | | | 83.47 50 | | | | | | | | |
|
door | | | | | | | | | 47.60 355 | | | | | | | | |
|
HQP5-MVS | | | | | | | 54.94 112 | | | | | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 67.04 65 | | |
|
HQP4-MVS | | | | | | | | | | | 67.85 129 | | | 86.93 42 | | | 84.32 129 |
|
HQP3-MVS | | | | | | | | | 83.90 38 | | | | | | | 80.35 85 | |
|
HQP2-MVS | | | | | | | | | | | | | 45.46 142 | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 25.89 353 | 61.22 313 | | 40.10 327 | 51.10 314 | | 32.97 277 | | 38.49 273 | | 78.61 243 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 74.07 154 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 72.16 189 | |
|
Test By Simon | | | | | | | | | | | | | 48.33 111 | | | | |
|