test_part1 | | | | | | | | | 98.26 25 | | | | 95.31 1 | | | 99.63 5 | 99.63 5 |
|
ESAPD | | | 97.57 4 | 97.29 7 | 98.41 2 | 99.28 17 | 95.74 3 | 97.50 96 | 98.26 25 | 93.81 45 | 98.10 7 | 98.53 12 | 95.31 1 | 99.87 5 | 95.19 49 | 99.63 5 | 99.63 5 |
|
SteuartSystems-ACMMP | | | 97.62 3 | 97.53 2 | 97.87 14 | 98.39 60 | 94.25 23 | 98.43 16 | 98.27 24 | 95.34 9 | 98.11 6 | 98.56 8 | 94.53 3 | 99.71 30 | 96.57 17 | 99.62 8 | 99.65 3 |
Skip Steuart: Steuart Systems R&D Blog. |
CNVR-MVS | | | 97.68 2 | 97.44 5 | 98.37 3 | 98.90 33 | 95.86 2 | 97.27 119 | 98.08 51 | 95.81 3 | 97.87 12 | 98.31 34 | 94.26 4 | 99.68 38 | 97.02 4 | 99.49 24 | 99.57 14 |
|
SD-MVS | | | 97.41 7 | 97.53 2 | 97.06 57 | 98.57 52 | 94.46 17 | 97.92 46 | 98.14 41 | 94.82 21 | 99.01 1 | 98.55 10 | 94.18 5 | 97.41 284 | 96.94 5 | 99.64 4 | 99.32 44 |
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HSP-MVS | | | 97.53 5 | 97.49 4 | 97.63 35 | 99.40 5 | 93.77 41 | 98.53 9 | 97.85 90 | 95.55 5 | 98.56 4 | 97.81 62 | 93.90 6 | 99.65 42 | 96.62 14 | 99.21 51 | 99.48 29 |
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MCST-MVS | | | 97.18 12 | 96.84 19 | 98.20 6 | 99.30 16 | 95.35 6 | 97.12 135 | 98.07 56 | 93.54 53 | 96.08 54 | 97.69 69 | 93.86 7 | 99.71 30 | 96.50 18 | 99.39 35 | 99.55 18 |
|
APDe-MVS | | | 97.82 1 | 97.73 1 | 98.08 9 | 99.15 25 | 94.82 13 | 98.81 2 | 98.30 22 | 94.76 24 | 98.30 5 | 98.90 2 | 93.77 8 | 99.68 38 | 97.93 1 | 99.69 1 | 99.75 1 |
|
TSAR-MVS + MP. | | | 97.42 6 | 97.33 6 | 97.69 29 | 99.25 20 | 94.24 24 | 98.07 34 | 97.85 90 | 93.72 47 | 98.57 3 | 98.35 25 | 93.69 9 | 99.40 90 | 97.06 3 | 99.46 26 | 99.44 33 |
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DeepPCF-MVS | | 93.97 1 | 96.61 37 | 97.09 8 | 95.15 140 | 98.09 84 | 86.63 261 | 96.00 235 | 98.15 39 | 95.43 7 | 97.95 10 | 98.56 8 | 93.40 10 | 99.36 94 | 96.77 12 | 99.48 25 | 99.45 31 |
|
SMA-MVS | | | 97.34 8 | 97.03 10 | 98.28 4 | 99.02 30 | 95.42 5 | 97.94 44 | 98.18 35 | 90.57 147 | 98.85 2 | 98.93 1 | 93.33 11 | 99.83 15 | 96.76 13 | 99.68 2 | 99.60 10 |
|
NCCC | | | 97.30 10 | 97.03 10 | 98.11 8 | 98.77 36 | 95.06 11 | 97.34 113 | 98.04 65 | 95.96 2 | 97.09 28 | 97.88 55 | 93.18 12 | 99.71 30 | 95.84 36 | 99.17 54 | 99.56 16 |
|
segment_acmp | | | | | | | | | | | | | 92.89 13 | | | | |
|
TSAR-MVS + GP. | | | 96.69 34 | 96.49 33 | 97.27 48 | 98.31 68 | 93.39 49 | 96.79 166 | 96.72 202 | 94.17 36 | 97.44 16 | 97.66 72 | 92.76 14 | 99.33 95 | 96.86 8 | 97.76 96 | 99.08 63 |
|
TEST9 | | | | | | 98.70 39 | 94.19 25 | 96.41 201 | 98.02 68 | 88.17 222 | 96.03 55 | 97.56 84 | 92.74 15 | 99.59 53 | | | |
|
train_agg | | | 96.30 45 | 95.83 48 | 97.72 26 | 98.70 39 | 94.19 25 | 96.41 201 | 98.02 68 | 88.58 201 | 96.03 55 | 97.56 84 | 92.73 16 | 99.59 53 | 95.04 55 | 99.37 40 | 99.39 37 |
|
test_8 | | | | | | 98.67 41 | 94.06 31 | 96.37 208 | 98.01 70 | 88.58 201 | 95.98 60 | 97.55 86 | 92.73 16 | 99.58 56 | | | |
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agg_prior1 | | | 96.22 48 | 95.77 49 | 97.56 37 | 98.67 41 | 93.79 38 | 96.28 217 | 98.00 72 | 88.76 198 | 95.68 70 | 97.55 86 | 92.70 18 | 99.57 64 | 95.01 57 | 99.32 42 | 99.32 44 |
|
CSCG | | | 96.05 51 | 95.91 47 | 96.46 79 | 99.24 21 | 90.47 133 | 98.30 21 | 98.57 11 | 89.01 183 | 93.97 100 | 97.57 82 | 92.62 19 | 99.76 24 | 94.66 68 | 99.27 46 | 99.15 56 |
|
Regformer-2 | | | 97.16 14 | 96.99 12 | 97.67 30 | 98.32 66 | 93.84 36 | 96.83 159 | 98.10 48 | 95.24 10 | 97.49 14 | 98.25 40 | 92.57 20 | 99.61 48 | 96.80 9 | 99.29 44 | 99.56 16 |
|
HPM-MVS++ | | | 97.34 8 | 96.97 13 | 98.47 1 | 99.08 27 | 96.16 1 | 97.55 93 | 97.97 79 | 95.59 4 | 96.61 36 | 97.89 53 | 92.57 20 | 99.84 14 | 95.95 33 | 99.51 20 | 99.40 36 |
|
PHI-MVS | | | 96.77 31 | 96.46 35 | 97.71 28 | 98.40 58 | 94.07 30 | 98.21 28 | 98.45 15 | 89.86 157 | 97.11 27 | 98.01 49 | 92.52 22 | 99.69 36 | 96.03 32 | 99.53 17 | 99.36 42 |
|
Regformer-1 | | | 97.10 16 | 96.96 14 | 97.54 38 | 98.32 66 | 93.48 47 | 96.83 159 | 97.99 77 | 95.20 12 | 97.46 15 | 98.25 40 | 92.48 23 | 99.58 56 | 96.79 11 | 99.29 44 | 99.55 18 |
|
agg_prior3 | | | 96.16 49 | 95.67 50 | 97.62 36 | 98.67 41 | 93.88 34 | 96.41 201 | 98.00 72 | 87.93 226 | 95.81 65 | 97.47 88 | 92.33 24 | 99.59 53 | 95.04 55 | 99.37 40 | 99.39 37 |
|
APD-MVS | | | 96.95 24 | 96.60 29 | 98.01 10 | 99.03 29 | 94.93 12 | 97.72 65 | 98.10 48 | 91.50 114 | 98.01 9 | 98.32 33 | 92.33 24 | 99.58 56 | 94.85 62 | 99.51 20 | 99.53 23 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
MVS_111021_HR | | | 96.68 36 | 96.58 31 | 96.99 59 | 98.46 54 | 92.31 74 | 96.20 225 | 98.90 2 | 94.30 35 | 95.86 63 | 97.74 67 | 92.33 24 | 99.38 93 | 96.04 31 | 99.42 31 | 99.28 49 |
|
MSLP-MVS++ | | | 96.94 25 | 97.06 9 | 96.59 69 | 98.72 38 | 91.86 89 | 97.67 71 | 98.49 12 | 94.66 27 | 97.24 19 | 98.41 22 | 92.31 27 | 98.94 131 | 96.61 15 | 99.46 26 | 98.96 73 |
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旧先验1 | | | | | | 98.38 61 | 93.38 50 | | 97.75 94 | | | 98.09 44 | 92.30 28 | | | 99.01 65 | 99.16 54 |
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HFP-MVS | | | 97.14 15 | 96.92 16 | 97.83 16 | 99.42 3 | 94.12 28 | 98.52 10 | 98.32 19 | 93.21 60 | 97.18 21 | 98.29 37 | 92.08 29 | 99.83 15 | 95.63 41 | 99.59 10 | 99.54 20 |
|
#test# | | | 97.02 21 | 96.75 26 | 97.83 16 | 99.42 3 | 94.12 28 | 98.15 29 | 98.32 19 | 92.57 83 | 97.18 21 | 98.29 37 | 92.08 29 | 99.83 15 | 95.12 53 | 99.59 10 | 99.54 20 |
|
test_prior3 | | | 96.46 41 | 96.20 43 | 97.23 50 | 98.67 41 | 92.99 58 | 96.35 209 | 98.00 72 | 92.80 79 | 96.03 55 | 97.59 80 | 92.01 31 | 99.41 88 | 95.01 57 | 99.38 36 | 99.29 46 |
|
test_prior2 | | | | | | | | 96.35 209 | | 92.80 79 | 96.03 55 | 97.59 80 | 92.01 31 | | 95.01 57 | 99.38 36 | |
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CDPH-MVS | | | 95.97 54 | 95.38 56 | 97.77 23 | 98.93 32 | 94.44 18 | 96.35 209 | 97.88 85 | 86.98 250 | 96.65 35 | 97.89 53 | 91.99 33 | 99.47 81 | 92.26 101 | 99.46 26 | 99.39 37 |
|
CP-MVS | | | 97.02 21 | 96.81 22 | 97.64 33 | 99.33 14 | 93.54 45 | 98.80 3 | 98.28 23 | 92.99 69 | 96.45 45 | 98.30 36 | 91.90 34 | 99.85 11 | 95.61 43 | 99.68 2 | 99.54 20 |
|
Regformer-4 | | | 96.97 23 | 96.87 17 | 97.25 49 | 98.34 63 | 92.66 67 | 96.96 145 | 98.01 70 | 95.12 13 | 97.14 24 | 98.42 19 | 91.82 35 | 99.61 48 | 96.90 6 | 99.13 57 | 99.50 25 |
|
Regformer-3 | | | 96.85 28 | 96.80 23 | 97.01 58 | 98.34 63 | 92.02 85 | 96.96 145 | 97.76 93 | 95.01 16 | 97.08 29 | 98.42 19 | 91.71 36 | 99.54 69 | 96.80 9 | 99.13 57 | 99.48 29 |
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XVS | | | 97.18 12 | 96.96 14 | 97.81 18 | 99.38 8 | 94.03 32 | 98.59 7 | 98.20 31 | 94.85 17 | 96.59 38 | 98.29 37 | 91.70 37 | 99.80 21 | 95.66 38 | 99.40 33 | 99.62 7 |
|
X-MVStestdata | | | 91.71 181 | 89.67 239 | 97.81 18 | 99.38 8 | 94.03 32 | 98.59 7 | 98.20 31 | 94.85 17 | 96.59 38 | 32.69 359 | 91.70 37 | 99.80 21 | 95.66 38 | 99.40 33 | 99.62 7 |
|
ACMMP_Plus | | | 97.20 11 | 96.86 18 | 98.23 5 | 99.09 26 | 95.16 9 | 97.60 87 | 98.19 33 | 92.82 78 | 97.93 11 | 98.74 4 | 91.60 39 | 99.86 8 | 96.26 21 | 99.52 18 | 99.67 2 |
|
region2R | | | 97.07 18 | 96.84 19 | 97.77 23 | 99.46 1 | 93.79 38 | 98.52 10 | 98.24 28 | 93.19 63 | 97.14 24 | 98.34 28 | 91.59 40 | 99.87 5 | 95.46 46 | 99.59 10 | 99.64 4 |
|
DELS-MVS | | | 96.61 37 | 96.38 38 | 97.30 45 | 97.79 103 | 93.19 54 | 95.96 236 | 98.18 35 | 95.23 11 | 95.87 62 | 97.65 73 | 91.45 41 | 99.70 35 | 95.87 34 | 99.44 30 | 99.00 71 |
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 |
ACMMPR | | | 97.07 18 | 96.84 19 | 97.79 20 | 99.44 2 | 93.88 34 | 98.52 10 | 98.31 21 | 93.21 60 | 97.15 23 | 98.33 31 | 91.35 42 | 99.86 8 | 95.63 41 | 99.59 10 | 99.62 7 |
|
DeepC-MVS_fast | | 93.89 2 | 96.93 26 | 96.64 28 | 97.78 21 | 98.64 47 | 94.30 21 | 97.41 104 | 98.04 65 | 94.81 22 | 96.59 38 | 98.37 24 | 91.24 43 | 99.64 47 | 95.16 51 | 99.52 18 | 99.42 35 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PGM-MVS | | | 96.81 29 | 96.53 32 | 97.65 31 | 99.35 13 | 93.53 46 | 97.65 74 | 98.98 1 | 92.22 89 | 97.14 24 | 98.44 17 | 91.17 44 | 99.85 11 | 94.35 70 | 99.46 26 | 99.57 14 |
|
MP-MVS-pluss | | | 96.70 33 | 96.27 40 | 97.98 11 | 99.23 23 | 94.71 14 | 96.96 145 | 98.06 58 | 90.67 138 | 95.55 76 | 98.78 3 | 91.07 45 | 99.86 8 | 96.58 16 | 99.55 15 | 99.38 40 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
mPP-MVS | | | 96.86 27 | 96.60 29 | 97.64 33 | 99.40 5 | 93.44 48 | 98.50 13 | 98.09 50 | 93.27 59 | 95.95 61 | 98.33 31 | 91.04 46 | 99.88 3 | 95.20 48 | 99.57 14 | 99.60 10 |
|
HPM-MVS | | | 96.69 34 | 96.45 36 | 97.40 41 | 99.36 12 | 93.11 56 | 98.87 1 | 98.06 58 | 91.17 126 | 96.40 46 | 97.99 51 | 90.99 47 | 99.58 56 | 95.61 43 | 99.61 9 | 99.49 27 |
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023 |
APD-MVS_3200maxsize | | | 96.81 29 | 96.71 27 | 97.12 56 | 99.01 31 | 92.31 74 | 97.98 43 | 98.06 58 | 93.11 66 | 97.44 16 | 98.55 10 | 90.93 48 | 99.55 67 | 96.06 30 | 99.25 47 | 99.51 24 |
|
test12 | | | | | 97.65 31 | 98.46 54 | 94.26 22 | | 97.66 106 | | 95.52 78 | | 90.89 49 | 99.46 82 | | 99.25 47 | 99.22 51 |
|
zzz-MVS | | | 97.07 18 | 96.77 25 | 97.97 12 | 99.37 10 | 94.42 19 | 97.15 133 | 98.08 51 | 95.07 14 | 96.11 52 | 98.59 6 | 90.88 50 | 99.90 1 | 96.18 28 | 99.50 22 | 99.58 12 |
|
MTAPA | | | 97.08 17 | 96.78 24 | 97.97 12 | 99.37 10 | 94.42 19 | 97.24 121 | 98.08 51 | 95.07 14 | 96.11 52 | 98.59 6 | 90.88 50 | 99.90 1 | 96.18 28 | 99.50 22 | 99.58 12 |
|
EI-MVSNet-Vis-set | | | 96.51 39 | 96.47 34 | 96.63 66 | 98.24 72 | 91.20 110 | 96.89 154 | 97.73 96 | 94.74 25 | 96.49 42 | 98.49 14 | 90.88 50 | 99.58 56 | 96.44 19 | 98.32 81 | 99.13 58 |
|
MP-MVS | | | 96.77 31 | 96.45 36 | 97.72 26 | 99.39 7 | 93.80 37 | 98.41 17 | 98.06 58 | 93.37 55 | 95.54 77 | 98.34 28 | 90.59 53 | 99.88 3 | 94.83 63 | 99.54 16 | 99.49 27 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
EI-MVSNet-UG-set | | | 96.34 44 | 96.30 39 | 96.47 77 | 98.20 77 | 90.93 121 | 96.86 156 | 97.72 99 | 94.67 26 | 96.16 51 | 98.46 15 | 90.43 54 | 99.58 56 | 96.23 22 | 97.96 90 | 98.90 80 |
|
原ACMM1 | | | | | 96.38 84 | 98.59 49 | 91.09 116 | | 97.89 83 | 87.41 238 | 95.22 80 | 97.68 70 | 90.25 55 | 99.54 69 | 87.95 180 | 99.12 60 | 98.49 109 |
|
1121 | | | 94.71 84 | 93.83 88 | 97.34 43 | 98.57 52 | 93.64 43 | 96.04 231 | 97.73 96 | 81.56 314 | 95.68 70 | 97.85 59 | 90.23 56 | 99.65 42 | 87.68 187 | 99.12 60 | 98.73 90 |
|
HPM-MVS_fast | | | 96.51 39 | 96.27 40 | 97.22 52 | 99.32 15 | 92.74 64 | 98.74 4 | 98.06 58 | 90.57 147 | 96.77 31 | 98.35 25 | 90.21 57 | 99.53 72 | 94.80 65 | 99.63 5 | 99.38 40 |
|
testdata | | | | | 95.46 128 | 98.18 81 | 88.90 195 | | 97.66 106 | 82.73 303 | 97.03 30 | 98.07 45 | 90.06 58 | 98.85 139 | 89.67 147 | 98.98 66 | 98.64 97 |
|
新几何1 | | | | | 97.32 44 | 98.60 48 | 93.59 44 | | 97.75 94 | 81.58 312 | 95.75 68 | 97.85 59 | 90.04 59 | 99.67 40 | 86.50 210 | 99.13 57 | 98.69 95 |
|
DP-MVS Recon | | | 95.68 58 | 95.12 63 | 97.37 42 | 99.19 24 | 94.19 25 | 97.03 138 | 98.08 51 | 88.35 215 | 95.09 82 | 97.65 73 | 89.97 60 | 99.48 80 | 92.08 110 | 98.59 76 | 98.44 116 |
|
MVS_111021_LR | | | 96.24 47 | 96.19 44 | 96.39 83 | 98.23 76 | 91.35 104 | 96.24 223 | 98.79 4 | 93.99 39 | 95.80 66 | 97.65 73 | 89.92 61 | 99.24 101 | 95.87 34 | 99.20 52 | 98.58 99 |
|
EPP-MVSNet | | | 95.22 68 | 95.04 64 | 95.76 109 | 97.49 125 | 89.56 162 | 98.67 5 | 97.00 181 | 90.69 137 | 94.24 95 | 97.62 78 | 89.79 62 | 98.81 142 | 93.39 91 | 96.49 129 | 98.92 78 |
|
PAPR | | | 94.18 92 | 93.42 105 | 96.48 76 | 97.64 112 | 91.42 103 | 95.55 253 | 97.71 103 | 88.99 184 | 92.34 138 | 95.82 162 | 89.19 63 | 99.11 112 | 86.14 215 | 97.38 105 | 98.90 80 |
|
MG-MVS | | | 95.61 59 | 95.38 56 | 96.31 88 | 98.42 57 | 90.53 131 | 96.04 231 | 97.48 123 | 93.47 54 | 95.67 73 | 98.10 43 | 89.17 64 | 99.25 100 | 91.27 130 | 98.77 71 | 99.13 58 |
|
PAPM_NR | | | 95.01 72 | 94.59 73 | 96.26 93 | 98.89 34 | 90.68 128 | 97.24 121 | 97.73 96 | 91.80 108 | 92.93 130 | 96.62 128 | 89.13 65 | 99.14 110 | 89.21 157 | 97.78 94 | 98.97 72 |
|
ACMMP | | | 96.27 46 | 95.93 46 | 97.28 47 | 99.24 21 | 92.62 68 | 98.25 25 | 98.81 3 | 92.99 69 | 94.56 89 | 98.39 23 | 88.96 66 | 99.85 11 | 94.57 69 | 97.63 97 | 99.36 42 |
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 |
UA-Net | | | 95.95 55 | 95.53 52 | 97.20 54 | 97.67 110 | 92.98 60 | 97.65 74 | 98.13 42 | 94.81 22 | 96.61 36 | 98.35 25 | 88.87 67 | 99.51 77 | 90.36 139 | 97.35 107 | 99.11 61 |
|
API-MVS | | | 94.84 81 | 94.49 78 | 95.90 104 | 97.90 99 | 92.00 86 | 97.80 56 | 97.48 123 | 89.19 173 | 94.81 85 | 96.71 114 | 88.84 68 | 99.17 106 | 88.91 165 | 98.76 72 | 96.53 184 |
|
test222 | | | | | | 98.24 72 | 92.21 77 | 95.33 262 | 97.60 111 | 79.22 327 | 95.25 79 | 97.84 61 | 88.80 69 | | | 99.15 55 | 98.72 91 |
|
Test By Simon | | | | | | | | | | | | | 88.73 70 | | | | |
|
pcd_1.5k_mvsjas | | | 7.39 342 | 9.85 343 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 88.65 71 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
PS-MVSNAJss | | | 93.74 109 | 93.51 99 | 94.44 178 | 93.91 280 | 89.28 185 | 97.75 59 | 97.56 117 | 92.50 85 | 89.94 198 | 96.54 131 | 88.65 71 | 98.18 195 | 93.83 81 | 90.90 221 | 95.86 209 |
|
PS-MVSNAJ | | | 95.37 62 | 95.33 58 | 95.49 125 | 97.35 126 | 90.66 129 | 95.31 264 | 97.48 123 | 93.85 42 | 96.51 41 | 95.70 173 | 88.65 71 | 99.65 42 | 94.80 65 | 98.27 82 | 96.17 194 |
|
xiu_mvs_v2_base | | | 95.32 64 | 95.29 59 | 95.40 130 | 97.22 128 | 90.50 132 | 95.44 259 | 97.44 135 | 93.70 49 | 96.46 44 | 96.18 144 | 88.59 74 | 99.53 72 | 94.79 67 | 97.81 93 | 96.17 194 |
|
PLC | | 91.00 6 | 94.11 96 | 93.43 103 | 96.13 97 | 98.58 51 | 91.15 115 | 96.69 181 | 97.39 140 | 87.29 241 | 91.37 156 | 96.71 114 | 88.39 75 | 99.52 76 | 87.33 198 | 97.13 112 | 97.73 145 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
UniMVSNet_NR-MVSNet | | | 93.37 119 | 92.67 122 | 95.47 127 | 95.34 204 | 92.83 62 | 97.17 131 | 98.58 10 | 92.98 74 | 90.13 190 | 95.80 163 | 88.37 76 | 97.85 251 | 91.71 119 | 83.93 296 | 95.73 222 |
|
PVSNet_BlendedMVS | | | 94.06 98 | 93.92 86 | 94.47 177 | 98.27 69 | 89.46 169 | 96.73 171 | 98.36 16 | 90.17 152 | 94.36 92 | 95.24 194 | 88.02 77 | 99.58 56 | 93.44 88 | 90.72 224 | 94.36 292 |
|
PVSNet_Blended | | | 94.87 80 | 94.56 74 | 95.81 107 | 98.27 69 | 89.46 169 | 95.47 258 | 98.36 16 | 88.84 192 | 94.36 92 | 96.09 151 | 88.02 77 | 99.58 56 | 93.44 88 | 98.18 84 | 98.40 119 |
|
TAPA-MVS | | 90.10 7 | 92.30 160 | 91.22 174 | 95.56 120 | 98.33 65 | 89.60 160 | 96.79 166 | 97.65 108 | 81.83 309 | 91.52 153 | 97.23 97 | 87.94 79 | 98.91 133 | 71.31 332 | 98.37 80 | 98.17 127 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
abl_6 | | | 96.40 42 | 96.21 42 | 96.98 60 | 98.89 34 | 92.20 79 | 97.89 48 | 98.03 67 | 93.34 58 | 97.22 20 | 98.42 19 | 87.93 80 | 99.72 29 | 95.10 54 | 99.07 62 | 99.02 65 |
|
MVS_Test | | | 94.89 79 | 94.62 72 | 95.68 116 | 96.83 145 | 89.55 163 | 96.70 179 | 97.17 157 | 91.17 126 | 95.60 74 | 96.11 150 | 87.87 81 | 98.76 148 | 93.01 97 | 97.17 111 | 98.72 91 |
|
casdiffmvs | | | 95.23 66 | 94.84 66 | 96.40 81 | 96.90 140 | 91.71 90 | 97.36 111 | 97.30 150 | 91.02 131 | 94.81 85 | 96.18 144 | 87.74 82 | 98.77 146 | 95.65 40 | 96.55 127 | 98.71 93 |
|
UniMVSNet (Re) | | | 93.31 121 | 92.55 127 | 95.61 118 | 95.39 201 | 93.34 53 | 97.39 108 | 98.71 5 | 93.14 65 | 90.10 194 | 94.83 211 | 87.71 83 | 98.03 224 | 91.67 123 | 83.99 295 | 95.46 230 |
|
FC-MVSNet-test | | | 93.94 103 | 93.57 95 | 95.04 146 | 95.48 198 | 91.45 102 | 98.12 30 | 98.71 5 | 93.37 55 | 90.23 185 | 96.70 116 | 87.66 84 | 97.85 251 | 91.49 125 | 90.39 229 | 95.83 213 |
|
canonicalmvs | | | 96.02 53 | 95.45 53 | 97.75 25 | 97.59 116 | 95.15 10 | 98.28 22 | 97.60 111 | 94.52 29 | 96.27 48 | 96.12 148 | 87.65 85 | 99.18 105 | 96.20 27 | 94.82 152 | 98.91 79 |
|
FIs | | | 94.09 97 | 93.70 91 | 95.27 132 | 95.70 192 | 92.03 84 | 98.10 31 | 98.68 7 | 93.36 57 | 90.39 182 | 96.70 116 | 87.63 86 | 97.94 241 | 92.25 103 | 90.50 228 | 95.84 212 |
|
CDS-MVSNet | | | 94.14 95 | 93.54 97 | 95.93 103 | 96.18 174 | 91.46 101 | 96.33 212 | 97.04 176 | 88.97 187 | 93.56 105 | 96.51 132 | 87.55 87 | 97.89 249 | 89.80 143 | 95.95 135 | 98.44 116 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
Effi-MVS+ | | | 94.93 77 | 94.45 80 | 96.36 86 | 96.61 150 | 91.47 100 | 96.41 201 | 97.41 139 | 91.02 131 | 94.50 90 | 95.92 156 | 87.53 88 | 98.78 144 | 93.89 78 | 96.81 118 | 98.84 86 |
|
PVSNet_Blended_VisFu | | | 95.27 65 | 94.91 65 | 96.38 84 | 98.20 77 | 90.86 123 | 97.27 119 | 98.25 27 | 90.21 151 | 94.18 96 | 97.27 94 | 87.48 89 | 99.73 26 | 93.53 84 | 97.77 95 | 98.55 100 |
|
mvs_anonymous | | | 93.82 106 | 93.74 90 | 94.06 192 | 96.44 163 | 85.41 273 | 95.81 243 | 97.05 173 | 89.85 159 | 90.09 195 | 96.36 139 | 87.44 90 | 97.75 261 | 93.97 74 | 96.69 123 | 99.02 65 |
|
CANet | | | 96.39 43 | 96.02 45 | 97.50 39 | 97.62 113 | 93.38 50 | 97.02 140 | 97.96 80 | 95.42 8 | 94.86 84 | 97.81 62 | 87.38 91 | 99.82 19 | 96.88 7 | 99.20 52 | 99.29 46 |
|
TAMVS | | | 94.01 101 | 93.46 101 | 95.64 117 | 96.16 176 | 90.45 134 | 96.71 176 | 96.89 195 | 89.27 171 | 93.46 110 | 96.92 108 | 87.29 92 | 97.94 241 | 88.70 171 | 95.74 139 | 98.53 102 |
|
nrg030 | | | 94.05 99 | 93.31 107 | 96.27 92 | 95.22 214 | 94.59 15 | 98.34 19 | 97.46 128 | 92.93 76 | 91.21 172 | 96.64 121 | 87.23 93 | 98.22 191 | 94.99 60 | 85.80 266 | 95.98 207 |
|
CPTT-MVS | | | 95.57 60 | 95.19 61 | 96.70 63 | 99.27 19 | 91.48 99 | 98.33 20 | 98.11 46 | 87.79 229 | 95.17 81 | 98.03 47 | 87.09 94 | 99.61 48 | 93.51 85 | 99.42 31 | 99.02 65 |
|
OMC-MVS | | | 95.09 71 | 94.70 71 | 96.25 94 | 98.46 54 | 91.28 105 | 96.43 199 | 97.57 114 | 92.04 103 | 94.77 87 | 97.96 52 | 87.01 95 | 99.09 121 | 91.31 129 | 96.77 119 | 98.36 123 |
|
DeepC-MVS | | 93.07 3 | 96.06 50 | 95.66 51 | 97.29 46 | 97.96 91 | 93.17 55 | 97.30 118 | 98.06 58 | 93.92 40 | 93.38 111 | 98.66 5 | 86.83 96 | 99.73 26 | 95.60 45 | 99.22 50 | 98.96 73 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
diffmvs | | | 94.47 87 | 94.23 83 | 95.18 134 | 96.32 168 | 88.22 211 | 96.27 218 | 97.04 176 | 92.55 84 | 93.60 104 | 95.94 155 | 86.79 97 | 98.70 153 | 92.98 98 | 96.61 125 | 98.63 98 |
|
IterMVS-LS | | | 92.29 161 | 91.94 143 | 93.34 238 | 96.25 170 | 86.97 254 | 96.57 195 | 97.05 173 | 90.67 138 | 89.50 220 | 94.80 214 | 86.59 98 | 97.64 269 | 89.91 141 | 86.11 264 | 95.40 237 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 93.03 130 | 92.88 115 | 93.48 231 | 95.77 190 | 86.98 253 | 96.44 197 | 97.12 163 | 90.66 140 | 91.30 161 | 97.64 76 | 86.56 99 | 98.05 218 | 89.91 141 | 90.55 226 | 95.41 233 |
|
1112_ss | | | 93.37 119 | 92.42 133 | 96.21 95 | 97.05 137 | 90.99 117 | 96.31 214 | 96.72 202 | 86.87 256 | 89.83 204 | 96.69 118 | 86.51 100 | 99.14 110 | 88.12 176 | 93.67 174 | 98.50 107 |
|
WTY-MVS | | | 94.71 84 | 94.02 85 | 96.79 62 | 97.71 109 | 92.05 83 | 96.59 192 | 97.35 146 | 90.61 144 | 94.64 88 | 96.93 106 | 86.41 101 | 99.39 91 | 91.20 132 | 94.71 156 | 98.94 76 |
|
EPNet | | | 95.20 69 | 94.56 74 | 97.14 55 | 92.80 312 | 92.68 66 | 97.85 53 | 94.87 291 | 96.64 1 | 92.46 133 | 97.80 64 | 86.23 102 | 99.65 42 | 93.72 82 | 98.62 75 | 99.10 62 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Fast-Effi-MVS+ | | | 93.46 117 | 92.75 119 | 95.59 119 | 96.77 147 | 90.03 137 | 96.81 163 | 97.13 162 | 88.19 220 | 91.30 161 | 94.27 250 | 86.21 103 | 98.63 156 | 87.66 189 | 96.46 131 | 98.12 129 |
|
MVSFormer | | | 95.37 62 | 95.16 62 | 95.99 102 | 96.34 166 | 91.21 108 | 98.22 26 | 97.57 114 | 91.42 118 | 96.22 49 | 97.32 92 | 86.20 104 | 97.92 245 | 94.07 72 | 99.05 63 | 98.85 84 |
|
lupinMVS | | | 94.99 76 | 94.56 74 | 96.29 91 | 96.34 166 | 91.21 108 | 95.83 242 | 96.27 221 | 88.93 189 | 96.22 49 | 96.88 109 | 86.20 104 | 98.85 139 | 95.27 47 | 99.05 63 | 98.82 87 |
|
114514_t | | | 93.95 102 | 93.06 111 | 96.63 66 | 99.07 28 | 91.61 95 | 97.46 103 | 97.96 80 | 77.99 332 | 93.00 125 | 97.57 82 | 86.14 106 | 99.33 95 | 89.22 156 | 99.15 55 | 98.94 76 |
|
alignmvs | | | 95.87 57 | 95.23 60 | 97.78 21 | 97.56 118 | 95.19 8 | 97.86 50 | 97.17 157 | 94.39 32 | 96.47 43 | 96.40 137 | 85.89 107 | 99.20 102 | 96.21 26 | 95.11 148 | 98.95 75 |
|
WR-MVS_H | | | 92.00 172 | 91.35 166 | 93.95 201 | 95.09 222 | 89.47 167 | 98.04 36 | 98.68 7 | 91.46 116 | 88.34 241 | 94.68 218 | 85.86 108 | 97.56 273 | 85.77 223 | 84.24 293 | 94.82 274 |
|
Test_1112_low_res | | | 92.84 139 | 91.84 145 | 95.85 106 | 97.04 138 | 89.97 143 | 95.53 255 | 96.64 210 | 85.38 272 | 89.65 214 | 95.18 195 | 85.86 108 | 99.10 118 | 87.70 185 | 93.58 179 | 98.49 109 |
|
HY-MVS | | 89.66 9 | 93.87 104 | 92.95 113 | 96.63 66 | 97.10 133 | 92.49 72 | 95.64 251 | 96.64 210 | 89.05 182 | 93.00 125 | 95.79 166 | 85.77 110 | 99.45 84 | 89.16 159 | 94.35 157 | 97.96 134 |
|
IS-MVSNet | | | 94.90 78 | 94.52 77 | 96.05 99 | 97.67 110 | 90.56 130 | 98.44 15 | 96.22 225 | 93.21 60 | 93.99 98 | 97.74 67 | 85.55 111 | 98.45 171 | 89.98 140 | 97.86 91 | 99.14 57 |
|
MVS | | | 91.71 181 | 90.44 208 | 95.51 123 | 95.20 216 | 91.59 97 | 96.04 231 | 97.45 132 | 73.44 344 | 87.36 262 | 95.60 177 | 85.42 112 | 99.10 118 | 85.97 220 | 97.46 100 | 95.83 213 |
|
VNet | | | 95.89 56 | 95.45 53 | 97.21 53 | 98.07 85 | 92.94 61 | 97.50 96 | 98.15 39 | 93.87 41 | 97.52 13 | 97.61 79 | 85.29 113 | 99.53 72 | 95.81 37 | 95.27 145 | 99.16 54 |
|
CNLPA | | | 94.28 90 | 93.53 98 | 96.52 71 | 98.38 61 | 92.55 70 | 96.59 192 | 96.88 196 | 90.13 153 | 91.91 146 | 97.24 96 | 85.21 114 | 99.09 121 | 87.64 190 | 97.83 92 | 97.92 136 |
|
F-COLMAP | | | 93.58 114 | 92.98 112 | 95.37 131 | 98.40 58 | 88.98 193 | 97.18 130 | 97.29 151 | 87.75 231 | 90.49 179 | 97.10 103 | 85.21 114 | 99.50 79 | 86.70 207 | 96.72 122 | 97.63 148 |
|
LCM-MVSNet-Re | | | 92.50 148 | 92.52 130 | 92.44 262 | 96.82 146 | 81.89 305 | 96.92 152 | 93.71 318 | 92.41 87 | 84.30 292 | 94.60 222 | 85.08 116 | 97.03 297 | 91.51 124 | 97.36 106 | 98.40 119 |
|
NR-MVSNet | | | 92.34 157 | 91.27 171 | 95.53 122 | 94.95 228 | 93.05 57 | 97.39 108 | 98.07 56 | 92.65 82 | 84.46 290 | 95.71 171 | 85.00 117 | 97.77 260 | 89.71 145 | 83.52 303 | 95.78 216 |
|
PAPM | | | 91.52 200 | 90.30 213 | 95.20 133 | 95.30 208 | 89.83 148 | 93.38 304 | 96.85 198 | 86.26 263 | 88.59 238 | 95.80 163 | 84.88 118 | 98.15 197 | 75.67 320 | 95.93 136 | 97.63 148 |
|
MAR-MVS | | | 94.22 91 | 93.46 101 | 96.51 74 | 98.00 86 | 92.19 80 | 97.67 71 | 97.47 126 | 88.13 224 | 93.00 125 | 95.84 160 | 84.86 119 | 99.51 77 | 87.99 179 | 98.17 85 | 97.83 142 |
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 |
jason | | | 94.84 81 | 94.39 82 | 96.18 96 | 95.52 196 | 90.93 121 | 96.09 229 | 96.52 214 | 89.28 170 | 96.01 59 | 97.32 92 | 84.70 120 | 98.77 146 | 95.15 52 | 98.91 69 | 98.85 84 |
jason: jason. |
sss | | | 94.51 86 | 93.80 89 | 96.64 64 | 97.07 134 | 91.97 87 | 96.32 213 | 98.06 58 | 88.94 188 | 94.50 90 | 96.78 111 | 84.60 121 | 99.27 99 | 91.90 113 | 96.02 133 | 98.68 96 |
|
LS3D | | | 93.57 115 | 92.61 125 | 96.47 77 | 97.59 116 | 91.61 95 | 97.67 71 | 97.72 99 | 85.17 276 | 90.29 184 | 98.34 28 | 84.60 121 | 99.73 26 | 83.85 256 | 98.27 82 | 98.06 133 |
|
Vis-MVSNet (Re-imp) | | | 94.15 93 | 93.88 87 | 94.95 153 | 97.61 114 | 87.92 233 | 98.10 31 | 95.80 247 | 92.22 89 | 93.02 124 | 97.45 89 | 84.53 123 | 97.91 248 | 88.24 174 | 97.97 89 | 99.02 65 |
|
cdsmvs_eth3d_5k | | | 23.24 338 | 30.99 338 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 97.63 110 | 0.00 363 | 0.00 364 | 96.88 109 | 84.38 124 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
0601test | | | 94.78 83 | 94.23 83 | 96.43 80 | 97.74 107 | 91.22 107 | 96.85 157 | 97.10 166 | 91.23 125 | 95.71 69 | 96.93 106 | 84.30 125 | 99.31 97 | 93.10 94 | 95.12 147 | 98.75 88 |
|
CHOSEN 280x420 | | | 93.12 126 | 92.72 121 | 94.34 183 | 96.71 149 | 87.27 244 | 90.29 334 | 97.72 99 | 86.61 260 | 91.34 158 | 95.29 191 | 84.29 126 | 98.41 178 | 93.25 92 | 98.94 68 | 97.35 160 |
|
PCF-MVS | | 89.48 11 | 91.56 197 | 89.95 228 | 96.36 86 | 96.60 151 | 92.52 71 | 92.51 318 | 97.26 152 | 79.41 325 | 88.90 231 | 96.56 130 | 84.04 127 | 99.55 67 | 77.01 316 | 97.30 108 | 97.01 163 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
1314 | | | 92.81 140 | 92.03 139 | 95.14 141 | 95.33 207 | 89.52 166 | 96.04 231 | 97.44 135 | 87.72 232 | 86.25 277 | 95.33 190 | 83.84 128 | 98.79 143 | 89.26 154 | 97.05 113 | 97.11 162 |
|
DP-MVS | | | 92.76 141 | 91.51 164 | 96.52 71 | 98.77 36 | 90.99 117 | 97.38 110 | 96.08 230 | 82.38 305 | 89.29 226 | 97.87 56 | 83.77 129 | 99.69 36 | 81.37 287 | 96.69 123 | 98.89 82 |
|
3Dnovator+ | | 91.43 4 | 95.40 61 | 94.48 79 | 98.16 7 | 96.90 140 | 95.34 7 | 98.48 14 | 97.87 87 | 94.65 28 | 88.53 239 | 98.02 48 | 83.69 130 | 99.71 30 | 93.18 93 | 98.96 67 | 99.44 33 |
|
AdaColmap | | | 94.34 89 | 93.68 93 | 96.31 88 | 98.59 49 | 91.68 94 | 96.59 192 | 97.81 92 | 89.87 156 | 92.15 142 | 97.06 104 | 83.62 131 | 99.54 69 | 89.34 152 | 98.07 87 | 97.70 147 |
|
DU-MVS | | | 92.90 135 | 92.04 138 | 95.49 125 | 94.95 228 | 92.83 62 | 97.16 132 | 98.24 28 | 93.02 68 | 90.13 190 | 95.71 171 | 83.47 132 | 97.85 251 | 91.71 119 | 83.93 296 | 95.78 216 |
|
Baseline_NR-MVSNet | | | 91.20 214 | 90.62 204 | 92.95 250 | 93.83 283 | 88.03 227 | 97.01 142 | 95.12 276 | 88.42 212 | 89.70 211 | 95.13 198 | 83.47 132 | 97.44 281 | 89.66 148 | 83.24 305 | 93.37 309 |
|
EPNet_dtu | | | 91.71 181 | 91.28 170 | 92.99 249 | 93.76 285 | 83.71 290 | 96.69 181 | 95.28 267 | 93.15 64 | 87.02 270 | 95.95 154 | 83.37 134 | 97.38 287 | 79.46 305 | 96.84 116 | 97.88 139 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
BH-untuned | | | 92.94 133 | 92.62 124 | 93.92 205 | 97.22 128 | 86.16 265 | 96.40 205 | 96.25 223 | 90.06 154 | 89.79 206 | 96.17 147 | 83.19 135 | 98.35 183 | 87.19 201 | 97.27 109 | 97.24 161 |
|
TranMVSNet+NR-MVSNet | | | 92.50 148 | 91.63 155 | 95.14 141 | 94.76 238 | 92.07 82 | 97.53 94 | 98.11 46 | 92.90 77 | 89.56 217 | 96.12 148 | 83.16 136 | 97.60 272 | 89.30 153 | 83.20 306 | 95.75 220 |
|
v18 | | | 88.71 268 | 87.52 267 | 92.27 264 | 94.16 260 | 88.11 223 | 96.82 162 | 95.96 233 | 87.03 246 | 80.76 314 | 89.81 315 | 83.15 137 | 96.22 310 | 84.69 238 | 75.31 332 | 92.49 320 |
|
CHOSEN 1792x2688 | | | 94.15 93 | 93.51 99 | 96.06 98 | 98.27 69 | 89.38 176 | 95.18 270 | 98.48 14 | 85.60 271 | 93.76 102 | 97.11 102 | 83.15 137 | 99.61 48 | 91.33 128 | 98.72 73 | 99.19 52 |
|
PMMVS | | | 92.86 137 | 92.34 134 | 94.42 180 | 94.92 231 | 86.73 257 | 94.53 279 | 96.38 217 | 84.78 283 | 94.27 94 | 95.12 199 | 83.13 139 | 98.40 179 | 91.47 126 | 96.49 129 | 98.12 129 |
|
v17 | | | 88.67 270 | 87.47 270 | 92.26 266 | 94.13 263 | 88.09 225 | 96.81 163 | 95.95 234 | 87.02 247 | 80.72 315 | 89.75 317 | 83.11 140 | 96.20 311 | 84.61 241 | 75.15 334 | 92.49 320 |
|
v1neww | | | 91.70 184 | 91.01 178 | 93.75 213 | 94.19 257 | 88.14 219 | 97.20 127 | 96.98 182 | 89.18 175 | 89.87 202 | 94.44 230 | 83.10 141 | 98.06 215 | 89.06 161 | 85.09 278 | 95.06 259 |
|
v7new | | | 91.70 184 | 91.01 178 | 93.75 213 | 94.19 257 | 88.14 219 | 97.20 127 | 96.98 182 | 89.18 175 | 89.87 202 | 94.44 230 | 83.10 141 | 98.06 215 | 89.06 161 | 85.09 278 | 95.06 259 |
|
Effi-MVS+-dtu | | | 93.08 127 | 93.21 109 | 92.68 259 | 96.02 182 | 83.25 296 | 97.14 134 | 96.72 202 | 93.85 42 | 91.20 173 | 93.44 280 | 83.08 143 | 98.30 188 | 91.69 121 | 95.73 140 | 96.50 186 |
|
mvs-test1 | | | 93.63 112 | 93.69 92 | 93.46 233 | 96.02 182 | 84.61 283 | 97.24 121 | 96.72 202 | 93.85 42 | 92.30 139 | 95.76 168 | 83.08 143 | 98.89 136 | 91.69 121 | 96.54 128 | 96.87 174 |
|
v16 | | | 88.69 269 | 87.50 268 | 92.26 266 | 94.19 257 | 88.11 223 | 96.81 163 | 95.95 234 | 87.01 248 | 80.71 316 | 89.80 316 | 83.08 143 | 96.20 311 | 84.61 241 | 75.34 331 | 92.48 322 |
|
v8 | | | 91.29 212 | 90.53 207 | 93.57 228 | 94.15 261 | 88.12 221 | 97.34 113 | 97.06 172 | 88.99 184 | 88.32 242 | 94.26 252 | 83.08 143 | 98.01 228 | 87.62 191 | 83.92 298 | 94.57 286 |
|
v6 | | | 91.69 186 | 91.00 180 | 93.75 213 | 94.14 262 | 88.12 221 | 97.20 127 | 96.98 182 | 89.19 173 | 89.90 199 | 94.42 232 | 83.04 147 | 98.07 210 | 89.07 160 | 85.10 277 | 95.07 256 |
|
V14 | | | 88.52 273 | 87.30 273 | 92.17 271 | 94.12 265 | 87.99 228 | 96.72 174 | 95.91 237 | 86.98 250 | 80.50 320 | 89.63 318 | 83.03 148 | 96.12 315 | 84.23 247 | 74.60 337 | 92.40 327 |
|
divwei89l23v2f112 | | | 91.61 192 | 90.89 182 | 93.78 210 | 94.01 275 | 88.22 211 | 96.96 145 | 96.96 186 | 89.17 177 | 89.75 208 | 94.28 248 | 83.02 149 | 98.03 224 | 88.86 166 | 84.98 286 | 95.08 254 |
|
v15 | | | 88.53 272 | 87.31 272 | 92.20 269 | 94.09 269 | 88.05 226 | 96.72 174 | 95.90 238 | 87.01 248 | 80.53 319 | 89.60 321 | 83.02 149 | 96.13 313 | 84.29 246 | 74.64 335 | 92.41 326 |
|
v1141 | | | 91.61 192 | 90.89 182 | 93.78 210 | 94.01 275 | 88.24 209 | 96.96 145 | 96.96 186 | 89.17 177 | 89.75 208 | 94.29 246 | 82.99 151 | 98.03 224 | 88.85 167 | 85.00 283 | 95.07 256 |
|
V9 | | | 88.49 276 | 87.26 275 | 92.18 270 | 94.12 265 | 87.97 231 | 96.73 171 | 95.90 238 | 86.95 252 | 80.40 322 | 89.61 319 | 82.98 152 | 96.13 313 | 84.14 248 | 74.55 338 | 92.44 324 |
|
v1 | | | 91.61 192 | 90.89 182 | 93.78 210 | 94.01 275 | 88.21 213 | 96.96 145 | 96.96 186 | 89.17 177 | 89.78 207 | 94.29 246 | 82.97 153 | 98.05 218 | 88.85 167 | 84.99 284 | 95.08 254 |
|
BH-w/o | | | 92.14 168 | 91.75 147 | 93.31 239 | 96.99 139 | 85.73 268 | 95.67 248 | 95.69 249 | 88.73 199 | 89.26 228 | 94.82 212 | 82.97 153 | 98.07 210 | 85.26 232 | 96.32 132 | 96.13 198 |
|
v148 | | | 90.99 221 | 90.38 211 | 92.81 254 | 93.83 283 | 85.80 267 | 96.78 168 | 96.68 207 | 89.45 167 | 88.75 235 | 93.93 261 | 82.96 155 | 97.82 255 | 87.83 182 | 83.25 304 | 94.80 276 |
|
v13 | | | 88.45 278 | 87.22 279 | 92.16 273 | 94.08 271 | 87.95 232 | 96.71 176 | 95.90 238 | 86.86 257 | 80.27 326 | 89.55 323 | 82.92 156 | 96.12 315 | 84.02 251 | 74.63 336 | 92.40 327 |
|
v12 | | | 88.46 277 | 87.23 278 | 92.17 271 | 94.10 268 | 87.99 228 | 96.71 176 | 95.90 238 | 86.91 253 | 80.34 324 | 89.58 322 | 82.92 156 | 96.11 317 | 84.09 249 | 74.50 340 | 92.42 325 |
|
HyFIR lowres test | | | 93.66 111 | 92.92 114 | 95.87 105 | 98.24 72 | 89.88 147 | 94.58 277 | 98.49 12 | 85.06 278 | 93.78 101 | 95.78 167 | 82.86 158 | 98.67 154 | 91.77 117 | 95.71 141 | 99.07 64 |
|
test_djsdf | | | 93.07 128 | 92.76 117 | 94.00 195 | 93.49 293 | 88.70 198 | 98.22 26 | 97.57 114 | 91.42 118 | 90.08 196 | 95.55 180 | 82.85 159 | 97.92 245 | 94.07 72 | 91.58 209 | 95.40 237 |
|
PatchmatchNet | | | 91.91 175 | 91.35 166 | 93.59 225 | 95.38 202 | 84.11 287 | 93.15 309 | 95.39 260 | 89.54 163 | 92.10 143 | 93.68 269 | 82.82 160 | 98.13 198 | 84.81 236 | 95.32 144 | 98.52 103 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
sam_mvs1 | | | | | | | | | | | | | 82.76 161 | | | | 98.45 114 |
|
xiu_mvs_v1_base_debu | | | 95.01 72 | 94.76 68 | 95.75 111 | 96.58 152 | 91.71 90 | 96.25 220 | 97.35 146 | 92.99 69 | 96.70 32 | 96.63 125 | 82.67 162 | 99.44 85 | 96.22 23 | 97.46 100 | 96.11 199 |
|
xiu_mvs_v1_base | | | 95.01 72 | 94.76 68 | 95.75 111 | 96.58 152 | 91.71 90 | 96.25 220 | 97.35 146 | 92.99 69 | 96.70 32 | 96.63 125 | 82.67 162 | 99.44 85 | 96.22 23 | 97.46 100 | 96.11 199 |
|
xiu_mvs_v1_base_debi | | | 95.01 72 | 94.76 68 | 95.75 111 | 96.58 152 | 91.71 90 | 96.25 220 | 97.35 146 | 92.99 69 | 96.70 32 | 96.63 125 | 82.67 162 | 99.44 85 | 96.22 23 | 97.46 100 | 96.11 199 |
|
patchmatchnet-post | | | | | | | | | | | | 90.45 311 | 82.65 165 | 98.10 203 | | | |
|
V42 | | | 91.58 196 | 90.87 185 | 93.73 216 | 94.05 274 | 88.50 202 | 97.32 116 | 96.97 185 | 88.80 197 | 89.71 210 | 94.33 237 | 82.54 166 | 98.05 218 | 89.01 163 | 85.07 280 | 94.64 284 |
|
WR-MVS | | | 92.34 157 | 91.53 161 | 94.77 164 | 95.13 220 | 90.83 124 | 96.40 205 | 97.98 78 | 91.88 107 | 89.29 226 | 95.54 181 | 82.50 167 | 97.80 256 | 89.79 144 | 85.27 273 | 95.69 223 |
|
v11 | | | 88.41 279 | 87.19 282 | 92.08 276 | 94.08 271 | 87.77 237 | 96.75 169 | 95.85 244 | 86.74 258 | 80.50 320 | 89.50 324 | 82.49 168 | 96.08 318 | 83.55 257 | 75.20 333 | 92.38 329 |
|
tpmrst | | | 91.44 203 | 91.32 168 | 91.79 284 | 95.15 218 | 79.20 327 | 93.42 303 | 95.37 262 | 88.55 203 | 93.49 109 | 93.67 270 | 82.49 168 | 98.27 189 | 90.41 138 | 89.34 238 | 97.90 137 |
|
MDTV_nov1_ep13_2view | | | | | | | 70.35 342 | 93.10 311 | | 83.88 293 | 93.55 106 | | 82.47 170 | | 86.25 213 | | 98.38 122 |
|
XVG-OURS-SEG-HR | | | 93.86 105 | 93.55 96 | 94.81 160 | 97.06 136 | 88.53 201 | 95.28 265 | 97.45 132 | 91.68 111 | 94.08 97 | 97.68 70 | 82.41 171 | 98.90 134 | 93.84 80 | 92.47 193 | 96.98 164 |
|
QAPM | | | 93.45 118 | 92.27 135 | 96.98 60 | 96.77 147 | 92.62 68 | 98.39 18 | 98.12 43 | 84.50 286 | 88.27 245 | 97.77 65 | 82.39 172 | 99.81 20 | 85.40 229 | 98.81 70 | 98.51 105 |
|
Patchmatch-test | | | 89.42 259 | 87.99 263 | 93.70 219 | 95.27 209 | 85.11 275 | 88.98 341 | 94.37 306 | 81.11 315 | 87.10 268 | 93.69 268 | 82.28 173 | 97.50 277 | 74.37 323 | 94.76 153 | 98.48 111 |
|
Vis-MVSNet | | | 95.23 66 | 94.81 67 | 96.51 74 | 97.18 130 | 91.58 98 | 98.26 24 | 98.12 43 | 94.38 33 | 94.90 83 | 98.15 42 | 82.28 173 | 98.92 132 | 91.45 127 | 98.58 77 | 99.01 69 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v7 | | | 91.47 202 | 90.73 194 | 93.68 221 | 94.13 263 | 88.16 217 | 97.09 136 | 97.05 173 | 88.38 213 | 89.80 205 | 94.52 223 | 82.21 175 | 98.01 228 | 88.00 178 | 85.42 269 | 94.87 268 |
|
3Dnovator | | 91.36 5 | 95.19 70 | 94.44 81 | 97.44 40 | 96.56 155 | 93.36 52 | 98.65 6 | 98.36 16 | 94.12 37 | 89.25 229 | 98.06 46 | 82.20 176 | 99.77 23 | 93.41 90 | 99.32 42 | 99.18 53 |
|
v10 | | | 91.04 220 | 90.23 218 | 93.49 230 | 94.12 265 | 88.16 217 | 97.32 116 | 97.08 169 | 88.26 217 | 88.29 244 | 94.22 253 | 82.17 177 | 97.97 235 | 86.45 211 | 84.12 294 | 94.33 293 |
|
v1144 | | | 91.37 207 | 90.60 205 | 93.68 221 | 93.89 281 | 88.23 210 | 96.84 158 | 97.03 179 | 88.37 214 | 89.69 212 | 94.39 233 | 82.04 178 | 97.98 232 | 87.80 183 | 85.37 271 | 94.84 270 |
|
MVSTER | | | 93.20 124 | 92.81 116 | 94.37 181 | 96.56 155 | 89.59 161 | 97.06 137 | 97.12 163 | 91.24 124 | 91.30 161 | 95.96 153 | 82.02 179 | 98.05 218 | 93.48 87 | 90.55 226 | 95.47 229 |
|
CP-MVSNet | | | 91.89 176 | 91.24 172 | 93.82 207 | 95.05 223 | 88.57 200 | 97.82 55 | 98.19 33 | 91.70 110 | 88.21 247 | 95.76 168 | 81.96 180 | 97.52 276 | 87.86 181 | 84.65 289 | 95.37 240 |
|
Patchmatch-RL test | | | 87.38 288 | 86.24 287 | 90.81 302 | 88.74 336 | 78.40 330 | 88.12 344 | 93.17 324 | 87.11 245 | 82.17 303 | 89.29 325 | 81.95 181 | 95.60 326 | 88.64 172 | 77.02 325 | 98.41 118 |
|
sam_mvs | | | | | | | | | | | | | 81.94 182 | | | | |
|
pmmvs4 | | | 90.93 223 | 89.85 232 | 94.17 188 | 93.34 297 | 90.79 126 | 94.60 276 | 96.02 231 | 84.62 284 | 87.45 258 | 95.15 196 | 81.88 183 | 97.45 280 | 87.70 185 | 87.87 251 | 94.27 297 |
|
test_post | | | | | | | | | | | | 17.58 362 | 81.76 184 | 98.08 206 | | | |
|
XVG-OURS | | | 93.72 110 | 93.35 106 | 94.80 161 | 97.07 134 | 88.61 199 | 94.79 274 | 97.46 128 | 91.97 106 | 93.99 98 | 97.86 58 | 81.74 185 | 98.88 138 | 92.64 100 | 92.67 192 | 96.92 172 |
|
Anonymous20240521 | | | 91.32 210 | 90.43 210 | 93.98 196 | 94.93 230 | 89.28 185 | 98.04 36 | 97.53 118 | 89.49 166 | 86.68 274 | 94.82 212 | 81.72 186 | 98.05 218 | 85.31 230 | 85.39 270 | 94.61 285 |
|
v2v482 | | | 91.59 195 | 90.85 187 | 93.80 208 | 93.87 282 | 88.17 216 | 96.94 151 | 96.88 196 | 89.54 163 | 89.53 218 | 94.90 204 | 81.70 187 | 98.02 227 | 89.25 155 | 85.04 282 | 95.20 251 |
|
v144192 | | | 91.06 219 | 90.28 214 | 93.39 235 | 93.66 288 | 87.23 247 | 96.83 159 | 97.07 170 | 87.43 237 | 89.69 212 | 94.28 248 | 81.48 188 | 98.00 231 | 87.18 202 | 84.92 287 | 94.93 266 |
|
pcd1.5k->3k | | | 38.37 336 | 40.51 337 | 31.96 349 | 94.29 255 | 0.00 368 | 0.00 360 | 97.69 104 | 0.00 363 | 0.00 364 | 0.00 365 | 81.45 189 | 0.00 366 | 0.00 363 | 91.11 217 | 95.89 208 |
|
MDTV_nov1_ep13 | | | | 90.76 192 | | 95.22 214 | 80.33 317 | 93.03 312 | 95.28 267 | 88.14 223 | 92.84 131 | 93.83 263 | 81.34 190 | 98.08 206 | 82.86 265 | 94.34 158 | |
|
HQP_MVS | | | 93.78 108 | 93.43 103 | 94.82 158 | 96.21 171 | 89.99 140 | 97.74 61 | 97.51 121 | 94.85 17 | 91.34 158 | 96.64 121 | 81.32 191 | 98.60 159 | 93.02 95 | 92.23 196 | 95.86 209 |
|
plane_prior6 | | | | | | 96.10 181 | 90.00 138 | | | | | | 81.32 191 | | | | |
|
v7n | | | 90.76 227 | 89.86 231 | 93.45 234 | 93.54 290 | 87.60 241 | 97.70 70 | 97.37 143 | 88.85 191 | 87.65 256 | 94.08 257 | 81.08 193 | 98.10 203 | 84.68 239 | 83.79 301 | 94.66 283 |
|
v748 | | | 90.34 241 | 89.54 242 | 92.75 256 | 93.25 300 | 85.71 269 | 97.61 86 | 97.17 157 | 88.54 204 | 87.20 265 | 93.54 274 | 81.02 194 | 98.01 228 | 85.73 225 | 81.80 311 | 94.52 287 |
|
MVS_0304 | | | 96.05 51 | 95.45 53 | 97.85 15 | 97.75 106 | 94.50 16 | 96.87 155 | 97.95 82 | 95.46 6 | 95.60 74 | 98.01 49 | 80.96 195 | 99.83 15 | 97.23 2 | 99.25 47 | 99.23 50 |
|
HQP2-MVS | | | | | | | | | | | | | 80.95 196 | | | | |
|
HQP-MVS | | | 93.19 125 | 92.74 120 | 94.54 176 | 95.86 185 | 89.33 180 | 96.65 184 | 97.39 140 | 93.55 50 | 90.14 186 | 95.87 158 | 80.95 196 | 98.50 167 | 92.13 107 | 92.10 201 | 95.78 216 |
|
V4 | | | 90.71 232 | 90.00 226 | 92.82 251 | 93.21 304 | 87.03 251 | 97.59 89 | 97.16 160 | 88.21 218 | 87.69 254 | 93.92 262 | 80.93 198 | 98.06 215 | 87.39 195 | 83.90 299 | 93.39 308 |
|
v52 | | | 90.70 233 | 90.00 226 | 92.82 251 | 93.24 301 | 87.03 251 | 97.60 87 | 97.14 161 | 88.21 218 | 87.69 254 | 93.94 260 | 80.91 199 | 98.07 210 | 87.39 195 | 83.87 300 | 93.36 310 |
|
CR-MVSNet | | | 90.82 226 | 89.77 235 | 93.95 201 | 94.45 249 | 87.19 248 | 90.23 335 | 95.68 251 | 86.89 255 | 92.40 134 | 92.36 298 | 80.91 199 | 97.05 295 | 81.09 296 | 93.95 170 | 97.60 153 |
|
Patchmtry | | | 88.64 271 | 87.25 276 | 92.78 255 | 94.09 269 | 86.64 258 | 89.82 338 | 95.68 251 | 80.81 319 | 87.63 257 | 92.36 298 | 80.91 199 | 97.03 297 | 78.86 308 | 85.12 276 | 94.67 282 |
|
v1192 | | | 91.07 218 | 90.23 218 | 93.58 227 | 93.70 286 | 87.82 236 | 96.73 171 | 97.07 170 | 87.77 230 | 89.58 215 | 94.32 238 | 80.90 202 | 97.97 235 | 86.52 209 | 85.48 267 | 94.95 262 |
|
PatchFormer-LS_test | | | 91.68 191 | 91.18 176 | 93.19 245 | 95.24 213 | 83.63 293 | 95.53 255 | 95.44 259 | 89.82 160 | 91.37 156 | 92.58 292 | 80.85 203 | 98.52 165 | 89.65 149 | 90.16 231 | 97.42 159 |
|
anonymousdsp | | | 92.16 166 | 91.55 160 | 93.97 199 | 92.58 316 | 89.55 163 | 97.51 95 | 97.42 138 | 89.42 168 | 88.40 240 | 94.84 209 | 80.66 204 | 97.88 250 | 91.87 115 | 91.28 215 | 94.48 288 |
|
CLD-MVS | | | 92.98 131 | 92.53 129 | 94.32 184 | 96.12 180 | 89.20 188 | 95.28 265 | 97.47 126 | 92.66 81 | 89.90 199 | 95.62 176 | 80.58 205 | 98.40 179 | 92.73 99 | 92.40 194 | 95.38 239 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
test_post1 | | | | | | | | 92.81 315 | | | | 16.58 363 | 80.53 206 | 97.68 265 | 86.20 214 | | |
|
VPA-MVSNet | | | 93.24 123 | 92.48 132 | 95.51 123 | 95.70 192 | 92.39 73 | 97.86 50 | 98.66 9 | 92.30 88 | 92.09 144 | 95.37 189 | 80.49 207 | 98.40 179 | 93.95 75 | 85.86 265 | 95.75 220 |
|
tpmvs | | | 89.83 254 | 89.15 250 | 91.89 280 | 94.92 231 | 80.30 318 | 93.11 310 | 95.46 258 | 86.28 262 | 88.08 248 | 92.65 289 | 80.44 208 | 98.52 165 | 81.47 282 | 89.92 234 | 96.84 175 |
|
PatchMatch-RL | | | 92.90 135 | 92.02 140 | 95.56 120 | 98.19 79 | 90.80 125 | 95.27 267 | 97.18 155 | 87.96 225 | 91.86 148 | 95.68 174 | 80.44 208 | 98.99 129 | 84.01 252 | 97.54 99 | 96.89 173 |
|
PEN-MVS | | | 91.20 214 | 90.44 208 | 93.48 231 | 94.49 247 | 87.91 235 | 97.76 58 | 98.18 35 | 91.29 121 | 87.78 252 | 95.74 170 | 80.35 210 | 97.33 289 | 85.46 228 | 82.96 307 | 95.19 252 |
|
Fast-Effi-MVS+-dtu | | | 92.29 161 | 91.99 141 | 93.21 244 | 95.27 209 | 85.52 272 | 97.03 138 | 96.63 212 | 92.09 97 | 89.11 230 | 95.14 197 | 80.33 211 | 98.08 206 | 87.54 193 | 94.74 155 | 96.03 206 |
|
MSDG | | | 91.42 204 | 90.24 217 | 94.96 152 | 97.15 132 | 88.91 194 | 93.69 297 | 96.32 219 | 85.72 270 | 86.93 271 | 96.47 134 | 80.24 212 | 98.98 130 | 80.57 297 | 95.05 149 | 96.98 164 |
|
v1921920 | | | 90.85 225 | 90.03 225 | 93.29 240 | 93.55 289 | 86.96 255 | 96.74 170 | 97.04 176 | 87.36 239 | 89.52 219 | 94.34 236 | 80.23 213 | 97.97 235 | 86.27 212 | 85.21 274 | 94.94 264 |
|
RPMNet | | | 88.52 273 | 86.72 286 | 93.95 201 | 94.45 249 | 87.19 248 | 90.23 335 | 94.99 283 | 77.87 334 | 92.40 134 | 87.55 338 | 80.17 214 | 97.05 295 | 68.84 336 | 93.95 170 | 97.60 153 |
|
PatchT | | | 88.87 265 | 87.42 271 | 93.22 243 | 94.08 271 | 85.10 276 | 89.51 339 | 94.64 297 | 81.92 308 | 92.36 137 | 88.15 334 | 80.05 215 | 97.01 299 | 72.43 328 | 93.65 175 | 97.54 156 |
|
our_test_3 | | | 88.78 266 | 87.98 264 | 91.20 296 | 92.45 318 | 82.53 299 | 93.61 301 | 95.69 249 | 85.77 269 | 84.88 287 | 93.71 267 | 79.99 216 | 96.78 304 | 79.47 304 | 86.24 261 | 94.28 296 |
|
DTE-MVSNet | | | 90.56 237 | 89.75 237 | 93.01 248 | 93.95 278 | 87.25 245 | 97.64 78 | 97.65 108 | 90.74 135 | 87.12 266 | 95.68 174 | 79.97 217 | 97.00 300 | 83.33 260 | 81.66 314 | 94.78 279 |
|
TransMVSNet (Re) | | | 88.94 262 | 87.56 266 | 93.08 247 | 94.35 252 | 88.45 204 | 97.73 63 | 95.23 271 | 87.47 236 | 84.26 293 | 95.29 191 | 79.86 218 | 97.33 289 | 79.44 306 | 74.44 341 | 93.45 307 |
|
ACMM | | 89.79 8 | 92.96 132 | 92.50 131 | 94.35 182 | 96.30 169 | 88.71 197 | 97.58 90 | 97.36 145 | 91.40 120 | 90.53 178 | 96.65 120 | 79.77 219 | 98.75 149 | 91.24 131 | 91.64 207 | 95.59 225 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
XXY-MVS | | | 92.16 166 | 91.23 173 | 94.95 153 | 94.75 239 | 90.94 120 | 97.47 102 | 97.43 137 | 89.14 180 | 88.90 231 | 96.43 136 | 79.71 220 | 98.24 190 | 89.56 150 | 87.68 252 | 95.67 224 |
|
PS-CasMVS | | | 91.55 198 | 90.84 189 | 93.69 220 | 94.96 227 | 88.28 206 | 97.84 54 | 98.24 28 | 91.46 116 | 88.04 249 | 95.80 163 | 79.67 221 | 97.48 278 | 87.02 204 | 84.54 291 | 95.31 243 |
|
ab-mvs | | | 93.57 115 | 92.55 127 | 96.64 64 | 97.28 127 | 91.96 88 | 95.40 260 | 97.45 132 | 89.81 161 | 93.22 119 | 96.28 141 | 79.62 222 | 99.46 82 | 90.74 135 | 93.11 186 | 98.50 107 |
|
v1240 | | | 90.70 233 | 89.85 232 | 93.23 242 | 93.51 292 | 86.80 256 | 96.61 189 | 97.02 180 | 87.16 244 | 89.58 215 | 94.31 239 | 79.55 223 | 97.98 232 | 85.52 227 | 85.44 268 | 94.90 267 |
|
CostFormer | | | 91.18 217 | 90.70 196 | 92.62 260 | 94.84 235 | 81.76 306 | 94.09 290 | 94.43 303 | 84.15 289 | 92.72 132 | 93.77 266 | 79.43 224 | 98.20 192 | 90.70 136 | 92.18 199 | 97.90 137 |
|
CANet_DTU | | | 94.37 88 | 93.65 94 | 96.55 70 | 96.46 162 | 92.13 81 | 96.21 224 | 96.67 209 | 94.38 33 | 93.53 108 | 97.03 105 | 79.34 225 | 99.71 30 | 90.76 134 | 98.45 79 | 97.82 143 |
|
OPM-MVS | | | 93.28 122 | 92.76 117 | 94.82 158 | 94.63 243 | 90.77 127 | 96.65 184 | 97.18 155 | 93.72 47 | 91.68 151 | 97.26 95 | 79.33 226 | 98.63 156 | 92.13 107 | 92.28 195 | 95.07 256 |
|
JIA-IIPM | | | 88.26 282 | 87.04 283 | 91.91 279 | 93.52 291 | 81.42 308 | 89.38 340 | 94.38 305 | 80.84 318 | 90.93 175 | 80.74 345 | 79.22 227 | 97.92 245 | 82.76 267 | 91.62 208 | 96.38 190 |
|
CVMVSNet | | | 91.23 213 | 91.75 147 | 89.67 315 | 95.77 190 | 74.69 335 | 96.44 197 | 94.88 288 | 85.81 268 | 92.18 141 | 97.64 76 | 79.07 228 | 95.58 327 | 88.06 177 | 95.86 138 | 98.74 89 |
|
LPG-MVS_test | | | 92.94 133 | 92.56 126 | 94.10 190 | 96.16 176 | 88.26 207 | 97.65 74 | 97.46 128 | 91.29 121 | 90.12 192 | 97.16 99 | 79.05 229 | 98.73 150 | 92.25 103 | 91.89 204 | 95.31 243 |
|
LGP-MVS_train | | | | | 94.10 190 | 96.16 176 | 88.26 207 | | 97.46 128 | 91.29 121 | 90.12 192 | 97.16 99 | 79.05 229 | 98.73 150 | 92.25 103 | 91.89 204 | 95.31 243 |
|
test-LLR | | | 91.42 204 | 91.19 175 | 92.12 274 | 94.59 244 | 80.66 312 | 94.29 284 | 92.98 331 | 91.11 128 | 90.76 176 | 92.37 295 | 79.02 231 | 98.07 210 | 88.81 169 | 96.74 120 | 97.63 148 |
|
test0.0.03 1 | | | 89.37 260 | 88.70 254 | 91.41 294 | 92.47 317 | 85.63 270 | 95.22 269 | 92.70 336 | 91.11 128 | 86.91 272 | 93.65 271 | 79.02 231 | 93.19 339 | 78.00 311 | 89.18 239 | 95.41 233 |
|
ADS-MVSNet2 | | | 89.45 258 | 88.59 256 | 92.03 277 | 95.86 185 | 82.26 303 | 90.93 330 | 94.32 308 | 83.23 300 | 91.28 164 | 91.81 305 | 79.01 233 | 95.99 319 | 79.52 302 | 91.39 213 | 97.84 140 |
|
ADS-MVSNet | | | 89.89 251 | 88.68 255 | 93.53 229 | 95.86 185 | 84.89 280 | 90.93 330 | 95.07 279 | 83.23 300 | 91.28 164 | 91.81 305 | 79.01 233 | 97.85 251 | 79.52 302 | 91.39 213 | 97.84 140 |
|
ppachtmachnet_test | | | 88.35 281 | 87.29 274 | 91.53 290 | 92.45 318 | 83.57 294 | 93.75 295 | 95.97 232 | 84.28 287 | 85.32 286 | 94.18 254 | 79.00 235 | 96.93 301 | 75.71 319 | 84.99 284 | 94.10 298 |
|
tfpn1000 | | | 91.99 173 | 91.05 177 | 94.80 161 | 97.78 104 | 89.66 158 | 97.91 47 | 92.90 334 | 88.99 184 | 91.73 149 | 94.84 209 | 78.99 236 | 98.33 186 | 82.41 272 | 93.91 172 | 96.40 189 |
|
conf0.01 | | | 91.74 179 | 90.67 198 | 94.94 156 | 97.55 119 | 89.68 152 | 97.64 78 | 93.14 325 | 88.43 206 | 91.24 166 | 94.30 240 | 78.91 237 | 98.45 171 | 81.28 289 | 93.57 180 | 96.70 179 |
|
conf0.002 | | | 91.74 179 | 90.67 198 | 94.94 156 | 97.55 119 | 89.68 152 | 97.64 78 | 93.14 325 | 88.43 206 | 91.24 166 | 94.30 240 | 78.91 237 | 98.45 171 | 81.28 289 | 93.57 180 | 96.70 179 |
|
thresconf0.02 | | | 91.69 186 | 90.67 198 | 94.75 165 | 97.55 119 | 89.68 152 | 97.64 78 | 93.14 325 | 88.43 206 | 91.24 166 | 94.30 240 | 78.91 237 | 98.45 171 | 81.28 289 | 93.57 180 | 96.11 199 |
|
tfpn_n400 | | | 91.69 186 | 90.67 198 | 94.75 165 | 97.55 119 | 89.68 152 | 97.64 78 | 93.14 325 | 88.43 206 | 91.24 166 | 94.30 240 | 78.91 237 | 98.45 171 | 81.28 289 | 93.57 180 | 96.11 199 |
|
tfpnconf | | | 91.69 186 | 90.67 198 | 94.75 165 | 97.55 119 | 89.68 152 | 97.64 78 | 93.14 325 | 88.43 206 | 91.24 166 | 94.30 240 | 78.91 237 | 98.45 171 | 81.28 289 | 93.57 180 | 96.11 199 |
|
tfpnview11 | | | 91.69 186 | 90.67 198 | 94.75 165 | 97.55 119 | 89.68 152 | 97.64 78 | 93.14 325 | 88.43 206 | 91.24 166 | 94.30 240 | 78.91 237 | 98.45 171 | 81.28 289 | 93.57 180 | 96.11 199 |
|
tfpn_ndepth | | | 91.88 177 | 90.96 181 | 94.62 170 | 97.73 108 | 89.93 146 | 97.75 59 | 92.92 333 | 88.93 189 | 91.73 149 | 93.80 265 | 78.91 237 | 98.49 170 | 83.02 264 | 93.86 173 | 95.45 231 |
|
OpenMVS | | 89.19 12 | 92.86 137 | 91.68 150 | 96.40 81 | 95.34 204 | 92.73 65 | 98.27 23 | 98.12 43 | 84.86 281 | 85.78 280 | 97.75 66 | 78.89 244 | 99.74 25 | 87.50 194 | 98.65 74 | 96.73 177 |
|
LTVRE_ROB | | 88.41 13 | 90.99 221 | 89.92 229 | 94.19 187 | 96.18 174 | 89.55 163 | 96.31 214 | 97.09 167 | 87.88 228 | 85.67 281 | 95.91 157 | 78.79 245 | 98.57 162 | 81.50 281 | 89.98 232 | 94.44 290 |
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
pm-mvs1 | | | 90.72 231 | 89.65 241 | 93.96 200 | 94.29 255 | 89.63 159 | 97.79 57 | 96.82 199 | 89.07 181 | 86.12 279 | 95.48 187 | 78.61 246 | 97.78 258 | 86.97 205 | 81.67 313 | 94.46 289 |
|
PVSNet | | 86.66 18 | 92.24 163 | 91.74 149 | 93.73 216 | 97.77 105 | 83.69 292 | 92.88 313 | 96.72 202 | 87.91 227 | 93.00 125 | 94.86 208 | 78.51 247 | 99.05 127 | 86.53 208 | 97.45 104 | 98.47 112 |
|
ACMP | | 89.59 10 | 92.62 143 | 92.14 136 | 94.05 193 | 96.40 164 | 88.20 214 | 97.36 111 | 97.25 154 | 91.52 113 | 88.30 243 | 96.64 121 | 78.46 248 | 98.72 152 | 91.86 116 | 91.48 211 | 95.23 250 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
BH-RMVSNet | | | 92.72 142 | 91.97 142 | 94.97 151 | 97.16 131 | 87.99 228 | 96.15 226 | 95.60 253 | 90.62 142 | 91.87 147 | 97.15 101 | 78.41 249 | 98.57 162 | 83.16 261 | 97.60 98 | 98.36 123 |
|
thres200 | | | 92.23 164 | 91.39 165 | 94.75 165 | 97.61 114 | 89.03 192 | 96.60 191 | 95.09 277 | 92.08 102 | 93.28 116 | 94.00 258 | 78.39 250 | 99.04 128 | 81.26 295 | 94.18 159 | 96.19 193 |
|
MDA-MVSNet_test_wron | | | 85.87 300 | 84.23 302 | 90.80 304 | 92.38 320 | 82.57 298 | 93.17 307 | 95.15 274 | 82.15 306 | 67.65 343 | 92.33 301 | 78.20 251 | 95.51 328 | 77.33 313 | 79.74 319 | 94.31 295 |
|
tfpn200view9 | | | 92.38 156 | 91.52 162 | 94.95 153 | 97.85 101 | 89.29 183 | 97.41 104 | 94.88 288 | 92.19 94 | 93.27 117 | 94.46 228 | 78.17 252 | 99.08 123 | 81.40 283 | 94.08 160 | 96.48 187 |
|
thres400 | | | 92.42 154 | 91.52 162 | 95.12 143 | 97.85 101 | 89.29 183 | 97.41 104 | 94.88 288 | 92.19 94 | 93.27 117 | 94.46 228 | 78.17 252 | 99.08 123 | 81.40 283 | 94.08 160 | 96.98 164 |
|
YYNet1 | | | 85.87 300 | 84.23 302 | 90.78 305 | 92.38 320 | 82.46 301 | 93.17 307 | 95.14 275 | 82.12 307 | 67.69 342 | 92.36 298 | 78.16 254 | 95.50 329 | 77.31 314 | 79.73 320 | 94.39 291 |
|
view600 | | | 92.55 144 | 91.68 150 | 95.18 134 | 97.98 87 | 89.44 171 | 98.00 38 | 94.57 298 | 92.09 97 | 93.17 120 | 95.52 182 | 78.14 255 | 99.11 112 | 81.61 276 | 94.04 166 | 96.98 164 |
|
view800 | | | 92.55 144 | 91.68 150 | 95.18 134 | 97.98 87 | 89.44 171 | 98.00 38 | 94.57 298 | 92.09 97 | 93.17 120 | 95.52 182 | 78.14 255 | 99.11 112 | 81.61 276 | 94.04 166 | 96.98 164 |
|
conf0.05thres1000 | | | 92.55 144 | 91.68 150 | 95.18 134 | 97.98 87 | 89.44 171 | 98.00 38 | 94.57 298 | 92.09 97 | 93.17 120 | 95.52 182 | 78.14 255 | 99.11 112 | 81.61 276 | 94.04 166 | 96.98 164 |
|
tfpn | | | 92.55 144 | 91.68 150 | 95.18 134 | 97.98 87 | 89.44 171 | 98.00 38 | 94.57 298 | 92.09 97 | 93.17 120 | 95.52 182 | 78.14 255 | 99.11 112 | 81.61 276 | 94.04 166 | 96.98 164 |
|
tfpn111 | | | 92.45 151 | 91.58 157 | 95.06 144 | 97.92 95 | 89.37 177 | 97.71 67 | 94.66 293 | 92.20 91 | 93.31 113 | 94.90 204 | 78.06 259 | 99.11 112 | 81.37 287 | 94.06 164 | 96.70 179 |
|
conf200view11 | | | 92.45 151 | 91.58 157 | 95.05 145 | 97.92 95 | 89.37 177 | 97.71 67 | 94.66 293 | 92.20 91 | 93.31 113 | 94.90 204 | 78.06 259 | 99.08 123 | 81.40 283 | 94.08 160 | 96.70 179 |
|
thres100view900 | | | 92.43 153 | 91.58 157 | 94.98 150 | 97.92 95 | 89.37 177 | 97.71 67 | 94.66 293 | 92.20 91 | 93.31 113 | 94.90 204 | 78.06 259 | 99.08 123 | 81.40 283 | 94.08 160 | 96.48 187 |
|
thres600view7 | | | 92.49 150 | 91.60 156 | 95.18 134 | 97.91 98 | 89.47 167 | 97.65 74 | 94.66 293 | 92.18 96 | 93.33 112 | 94.91 203 | 78.06 259 | 99.10 118 | 81.61 276 | 94.06 164 | 96.98 164 |
|
tpm cat1 | | | 88.36 280 | 87.21 280 | 91.81 283 | 95.13 220 | 80.55 315 | 92.58 317 | 95.70 248 | 74.97 340 | 87.45 258 | 91.96 303 | 78.01 263 | 98.17 196 | 80.39 299 | 88.74 244 | 96.72 178 |
|
MVP-Stereo | | | 90.74 230 | 90.08 222 | 92.71 257 | 93.19 306 | 88.20 214 | 95.86 240 | 96.27 221 | 86.07 266 | 84.86 288 | 94.76 215 | 77.84 264 | 97.75 261 | 83.88 255 | 98.01 88 | 92.17 333 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
EPMVS | | | 90.70 233 | 89.81 234 | 93.37 237 | 94.73 240 | 84.21 285 | 93.67 298 | 88.02 350 | 89.50 165 | 92.38 136 | 93.49 277 | 77.82 265 | 97.78 258 | 86.03 219 | 92.68 191 | 98.11 132 |
|
tfpnnormal | | | 89.70 255 | 88.40 259 | 93.60 224 | 95.15 218 | 90.10 136 | 97.56 92 | 98.16 38 | 87.28 242 | 86.16 278 | 94.63 221 | 77.57 266 | 98.05 218 | 74.48 321 | 84.59 290 | 92.65 316 |
|
tpm | | | 90.25 243 | 89.74 238 | 91.76 287 | 93.92 279 | 79.73 323 | 93.98 291 | 93.54 322 | 88.28 216 | 91.99 145 | 93.25 283 | 77.51 267 | 97.44 281 | 87.30 199 | 87.94 250 | 98.12 129 |
|
FMVSNet3 | | | 91.78 178 | 90.69 197 | 95.03 147 | 96.53 157 | 92.27 76 | 97.02 140 | 96.93 191 | 89.79 162 | 89.35 223 | 94.65 220 | 77.01 268 | 97.47 279 | 86.12 216 | 88.82 241 | 95.35 241 |
|
TR-MVS | | | 91.48 201 | 90.59 206 | 94.16 189 | 96.40 164 | 87.33 242 | 95.67 248 | 95.34 266 | 87.68 233 | 91.46 154 | 95.52 182 | 76.77 269 | 98.35 183 | 82.85 266 | 93.61 177 | 96.79 176 |
|
RPSCF | | | 90.75 229 | 90.86 186 | 90.42 309 | 96.84 143 | 76.29 333 | 95.61 252 | 96.34 218 | 83.89 292 | 91.38 155 | 97.87 56 | 76.45 270 | 98.78 144 | 87.16 203 | 92.23 196 | 96.20 192 |
|
tpm2 | | | 89.96 249 | 89.21 248 | 92.23 268 | 94.91 233 | 81.25 309 | 93.78 294 | 94.42 304 | 80.62 320 | 91.56 152 | 93.44 280 | 76.44 271 | 97.94 241 | 85.60 226 | 92.08 203 | 97.49 157 |
|
EU-MVSNet | | | 88.72 267 | 88.90 252 | 88.20 318 | 93.15 307 | 74.21 336 | 96.63 188 | 94.22 312 | 85.18 275 | 87.32 263 | 95.97 152 | 76.16 272 | 94.98 331 | 85.27 231 | 86.17 262 | 95.41 233 |
|
dp | | | 88.90 264 | 88.26 262 | 90.81 302 | 94.58 246 | 76.62 332 | 92.85 314 | 94.93 286 | 85.12 277 | 90.07 197 | 93.07 284 | 75.81 273 | 98.12 201 | 80.53 298 | 87.42 256 | 97.71 146 |
|
Patchmatch-test1 | | | 91.54 199 | 90.85 187 | 93.59 225 | 95.59 194 | 84.95 279 | 94.72 275 | 95.58 255 | 90.82 133 | 92.25 140 | 93.58 273 | 75.80 274 | 97.41 284 | 83.35 258 | 95.98 134 | 98.40 119 |
|
IterMVS | | | 90.15 247 | 89.67 239 | 91.61 289 | 95.48 198 | 83.72 289 | 94.33 283 | 96.12 229 | 89.99 155 | 87.31 264 | 94.15 255 | 75.78 275 | 96.27 309 | 86.97 205 | 86.89 259 | 94.83 272 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
semantic-postprocess | | | | | 91.82 282 | 95.52 196 | 84.20 286 | | 96.15 228 | 90.61 144 | 87.39 261 | 94.27 250 | 75.63 276 | 96.44 306 | 87.34 197 | 86.88 260 | 94.82 274 |
|
jajsoiax | | | 92.42 154 | 91.89 144 | 94.03 194 | 93.33 299 | 88.50 202 | 97.73 63 | 97.53 118 | 92.00 105 | 88.85 233 | 96.50 133 | 75.62 277 | 98.11 202 | 93.88 79 | 91.56 210 | 95.48 227 |
|
cascas | | | 91.20 214 | 90.08 222 | 94.58 175 | 94.97 226 | 89.16 191 | 93.65 299 | 97.59 113 | 79.90 323 | 89.40 221 | 92.92 286 | 75.36 278 | 98.36 182 | 92.14 106 | 94.75 154 | 96.23 191 |
|
tpmp4_e23 | | | 89.58 256 | 88.59 256 | 92.54 261 | 95.16 217 | 81.53 307 | 94.11 289 | 95.09 277 | 81.66 310 | 88.60 237 | 93.44 280 | 75.11 279 | 98.33 186 | 82.45 271 | 91.72 206 | 97.75 144 |
|
VPNet | | | 92.23 164 | 91.31 169 | 94.99 148 | 95.56 195 | 90.96 119 | 97.22 126 | 97.86 89 | 92.96 75 | 90.96 174 | 96.62 128 | 75.06 280 | 98.20 192 | 91.90 113 | 83.65 302 | 95.80 215 |
|
test_normal | | | 92.01 170 | 90.75 193 | 95.80 108 | 93.24 301 | 89.97 143 | 95.93 238 | 96.24 224 | 90.62 142 | 81.63 308 | 93.45 279 | 74.98 281 | 98.89 136 | 93.61 83 | 97.04 114 | 98.55 100 |
|
DI_MVS_plusplus_test | | | 92.01 170 | 90.77 191 | 95.73 114 | 93.34 297 | 89.78 150 | 96.14 227 | 96.18 227 | 90.58 146 | 81.80 307 | 93.50 276 | 74.95 282 | 98.90 134 | 93.51 85 | 96.94 115 | 98.51 105 |
|
N_pmnet | | | 78.73 318 | 78.71 317 | 78.79 335 | 92.80 312 | 46.50 362 | 94.14 288 | 43.71 365 | 78.61 330 | 80.83 311 | 91.66 308 | 74.94 283 | 96.36 307 | 67.24 337 | 84.45 292 | 93.50 305 |
|
mvs_tets | | | 92.31 159 | 91.76 146 | 93.94 204 | 93.41 295 | 88.29 205 | 97.63 85 | 97.53 118 | 92.04 103 | 88.76 234 | 96.45 135 | 74.62 284 | 98.09 205 | 93.91 77 | 91.48 211 | 95.45 231 |
|
DSMNet-mixed | | | 86.34 296 | 86.12 290 | 87.00 323 | 89.88 332 | 70.43 340 | 94.93 273 | 90.08 347 | 77.97 333 | 85.42 285 | 92.78 288 | 74.44 285 | 93.96 335 | 74.43 322 | 95.14 146 | 96.62 183 |
|
pmmvs5 | | | 89.86 253 | 88.87 253 | 92.82 251 | 92.86 310 | 86.23 264 | 96.26 219 | 95.39 260 | 84.24 288 | 87.12 266 | 94.51 224 | 74.27 286 | 97.36 288 | 87.61 192 | 87.57 253 | 94.86 269 |
|
OurMVSNet-221017-0 | | | 90.51 239 | 90.19 221 | 91.44 293 | 93.41 295 | 81.25 309 | 96.98 144 | 96.28 220 | 91.68 111 | 86.55 275 | 96.30 140 | 74.20 287 | 97.98 232 | 88.96 164 | 87.40 257 | 95.09 253 |
|
GBi-Net | | | 91.35 208 | 90.27 215 | 94.59 171 | 96.51 158 | 91.18 112 | 97.50 96 | 96.93 191 | 88.82 194 | 89.35 223 | 94.51 224 | 73.87 288 | 97.29 291 | 86.12 216 | 88.82 241 | 95.31 243 |
|
test1 | | | 91.35 208 | 90.27 215 | 94.59 171 | 96.51 158 | 91.18 112 | 97.50 96 | 96.93 191 | 88.82 194 | 89.35 223 | 94.51 224 | 73.87 288 | 97.29 291 | 86.12 216 | 88.82 241 | 95.31 243 |
|
FMVSNet2 | | | 91.31 211 | 90.08 222 | 94.99 148 | 96.51 158 | 92.21 77 | 97.41 104 | 96.95 189 | 88.82 194 | 88.62 236 | 94.75 216 | 73.87 288 | 97.42 283 | 85.20 233 | 88.55 247 | 95.35 241 |
|
COLMAP_ROB | | 87.81 15 | 90.40 240 | 89.28 247 | 93.79 209 | 97.95 92 | 87.13 250 | 96.92 152 | 95.89 243 | 82.83 302 | 86.88 273 | 97.18 98 | 73.77 291 | 99.29 98 | 78.44 310 | 93.62 176 | 94.95 262 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
DWT-MVSNet_test | | | 90.76 227 | 89.89 230 | 93.38 236 | 95.04 224 | 83.70 291 | 95.85 241 | 94.30 309 | 88.19 220 | 90.46 180 | 92.80 287 | 73.61 292 | 98.50 167 | 88.16 175 | 90.58 225 | 97.95 135 |
|
Anonymous20231206 | | | 87.09 291 | 86.14 289 | 89.93 314 | 91.22 326 | 80.35 316 | 96.11 228 | 95.35 263 | 83.57 297 | 84.16 294 | 93.02 285 | 73.54 293 | 95.61 325 | 72.16 329 | 86.14 263 | 93.84 303 |
|
UGNet | | | 94.04 100 | 93.28 108 | 96.31 88 | 96.85 142 | 91.19 111 | 97.88 49 | 97.68 105 | 94.40 31 | 93.00 125 | 96.18 144 | 73.39 294 | 99.61 48 | 91.72 118 | 98.46 78 | 98.13 128 |
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 |
LP | | | 84.13 307 | 81.85 312 | 90.97 299 | 93.20 305 | 82.12 304 | 87.68 345 | 94.27 311 | 76.80 335 | 81.93 305 | 88.52 329 | 72.97 295 | 95.95 320 | 59.53 346 | 81.73 312 | 94.84 270 |
|
Anonymous20231211 | | | 90.63 236 | 89.42 244 | 94.27 185 | 98.24 72 | 89.19 190 | 98.05 35 | 97.89 83 | 79.95 322 | 88.25 246 | 94.96 200 | 72.56 296 | 98.13 198 | 89.70 146 | 85.14 275 | 95.49 226 |
|
ACMH | | 87.59 16 | 90.53 238 | 89.42 244 | 93.87 206 | 96.21 171 | 87.92 233 | 97.24 121 | 96.94 190 | 88.45 205 | 83.91 298 | 96.27 142 | 71.92 297 | 98.62 158 | 84.43 244 | 89.43 237 | 95.05 261 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
GA-MVS | | | 91.38 206 | 90.31 212 | 94.59 171 | 94.65 242 | 87.62 240 | 94.34 282 | 96.19 226 | 90.73 136 | 90.35 183 | 93.83 263 | 71.84 298 | 97.96 239 | 87.22 200 | 93.61 177 | 98.21 126 |
|
SixPastTwentyTwo | | | 89.15 261 | 88.54 258 | 90.98 298 | 93.49 293 | 80.28 319 | 96.70 179 | 94.70 292 | 90.78 134 | 84.15 295 | 95.57 178 | 71.78 299 | 97.71 264 | 84.63 240 | 85.07 280 | 94.94 264 |
|
gg-mvs-nofinetune | | | 87.82 285 | 85.61 292 | 94.44 178 | 94.46 248 | 89.27 187 | 91.21 329 | 84.61 356 | 80.88 317 | 89.89 201 | 74.98 348 | 71.50 300 | 97.53 275 | 85.75 224 | 97.21 110 | 96.51 185 |
|
test20.03 | | | 86.14 298 | 85.40 294 | 88.35 316 | 90.12 329 | 80.06 321 | 95.90 239 | 95.20 272 | 88.59 200 | 81.29 310 | 93.62 272 | 71.43 301 | 92.65 340 | 71.26 333 | 81.17 316 | 92.34 330 |
|
MS-PatchMatch | | | 90.27 242 | 89.77 235 | 91.78 285 | 94.33 253 | 84.72 282 | 95.55 253 | 96.73 201 | 86.17 265 | 86.36 276 | 95.28 193 | 71.28 302 | 97.80 256 | 84.09 249 | 98.14 86 | 92.81 315 |
|
PVSNet_0 | | 82.17 19 | 85.46 303 | 83.64 304 | 90.92 300 | 95.27 209 | 79.49 324 | 90.55 333 | 95.60 253 | 83.76 295 | 83.00 301 | 89.95 312 | 71.09 303 | 97.97 235 | 82.75 268 | 60.79 350 | 95.31 243 |
|
GG-mvs-BLEND | | | | | 93.62 223 | 93.69 287 | 89.20 188 | 92.39 321 | 83.33 357 | | 87.98 251 | 89.84 314 | 71.00 304 | 96.87 302 | 82.08 275 | 95.40 143 | 94.80 276 |
|
ITE_SJBPF | | | | | 92.43 263 | 95.34 204 | 85.37 274 | | 95.92 236 | 91.47 115 | 87.75 253 | 96.39 138 | 71.00 304 | 97.96 239 | 82.36 273 | 89.86 235 | 93.97 301 |
|
IB-MVS | | 87.33 17 | 89.91 250 | 88.28 261 | 94.79 163 | 95.26 212 | 87.70 239 | 95.12 271 | 93.95 317 | 89.35 169 | 87.03 269 | 92.49 293 | 70.74 306 | 99.19 103 | 89.18 158 | 81.37 315 | 97.49 157 |
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 |
MDA-MVSNet-bldmvs | | | 85.00 304 | 82.95 306 | 91.17 297 | 93.13 308 | 83.33 295 | 94.56 278 | 95.00 281 | 84.57 285 | 65.13 347 | 92.65 289 | 70.45 307 | 95.85 321 | 73.57 326 | 77.49 324 | 94.33 293 |
|
AllTest | | | 90.23 244 | 88.98 251 | 93.98 196 | 97.94 93 | 86.64 258 | 96.51 196 | 95.54 256 | 85.38 272 | 85.49 283 | 96.77 112 | 70.28 308 | 99.15 108 | 80.02 300 | 92.87 187 | 96.15 196 |
|
TestCases | | | | | 93.98 196 | 97.94 93 | 86.64 258 | | 95.54 256 | 85.38 272 | 85.49 283 | 96.77 112 | 70.28 308 | 99.15 108 | 80.02 300 | 92.87 187 | 96.15 196 |
|
ACMH+ | | 87.92 14 | 90.20 245 | 89.18 249 | 93.25 241 | 96.48 161 | 86.45 262 | 96.99 143 | 96.68 207 | 88.83 193 | 84.79 289 | 96.22 143 | 70.16 310 | 98.53 164 | 84.42 245 | 88.04 249 | 94.77 280 |
|
Anonymous20240529 | | | 91.98 174 | 90.73 194 | 95.73 114 | 98.14 82 | 89.40 175 | 97.99 42 | 97.72 99 | 79.63 324 | 93.54 107 | 97.41 91 | 69.94 311 | 99.56 66 | 91.04 133 | 91.11 217 | 98.22 125 |
|
pmmvs-eth3d | | | 86.22 297 | 84.45 300 | 91.53 290 | 88.34 337 | 87.25 245 | 94.47 280 | 95.01 280 | 83.47 298 | 79.51 330 | 89.61 319 | 69.75 312 | 95.71 324 | 83.13 262 | 76.73 327 | 91.64 334 |
|
LFMVS | | | 93.60 113 | 92.63 123 | 96.52 71 | 98.13 83 | 91.27 106 | 97.94 44 | 93.39 323 | 90.57 147 | 96.29 47 | 98.31 34 | 69.00 313 | 99.16 107 | 94.18 71 | 95.87 137 | 99.12 60 |
|
TESTMET0.1,1 | | | 90.06 248 | 89.42 244 | 91.97 278 | 94.41 251 | 80.62 314 | 94.29 284 | 91.97 340 | 87.28 242 | 90.44 181 | 92.47 294 | 68.79 314 | 97.67 266 | 88.50 173 | 96.60 126 | 97.61 152 |
|
XVG-ACMP-BASELINE | | | 90.93 223 | 90.21 220 | 93.09 246 | 94.31 254 | 85.89 266 | 95.33 262 | 97.26 152 | 91.06 130 | 89.38 222 | 95.44 188 | 68.61 315 | 98.60 159 | 89.46 151 | 91.05 219 | 94.79 278 |
|
MVS-HIRNet | | | 82.47 313 | 81.21 314 | 86.26 325 | 95.38 202 | 69.21 345 | 88.96 342 | 89.49 349 | 66.28 347 | 80.79 313 | 74.08 350 | 68.48 316 | 97.39 286 | 71.93 330 | 95.47 142 | 92.18 332 |
|
VDD-MVS | | | 93.82 106 | 93.08 110 | 96.02 100 | 97.88 100 | 89.96 145 | 97.72 65 | 95.85 244 | 92.43 86 | 95.86 63 | 98.44 17 | 68.42 317 | 99.39 91 | 96.31 20 | 94.85 150 | 98.71 93 |
|
test_0402 | | | 86.46 295 | 84.79 298 | 91.45 292 | 95.02 225 | 85.55 271 | 96.29 216 | 94.89 287 | 80.90 316 | 82.21 302 | 93.97 259 | 68.21 318 | 97.29 291 | 62.98 342 | 88.68 246 | 91.51 336 |
|
test-mter | | | 90.19 246 | 89.54 242 | 92.12 274 | 94.59 244 | 80.66 312 | 94.29 284 | 92.98 331 | 87.68 233 | 90.76 176 | 92.37 295 | 67.67 319 | 98.07 210 | 88.81 169 | 96.74 120 | 97.63 148 |
|
VDDNet | | | 93.05 129 | 92.07 137 | 96.02 100 | 96.84 143 | 90.39 135 | 98.08 33 | 95.85 244 | 86.22 264 | 95.79 67 | 98.46 15 | 67.59 320 | 99.19 103 | 94.92 61 | 94.85 150 | 98.47 112 |
|
USDC | | | 88.94 262 | 87.83 265 | 92.27 264 | 94.66 241 | 84.96 278 | 93.86 293 | 95.90 238 | 87.34 240 | 83.40 300 | 95.56 179 | 67.43 321 | 98.19 194 | 82.64 270 | 89.67 236 | 93.66 304 |
|
pmmvs6 | | | 87.81 286 | 86.19 288 | 92.69 258 | 91.32 325 | 86.30 263 | 97.34 113 | 96.41 216 | 80.59 321 | 84.05 297 | 94.37 235 | 67.37 322 | 97.67 266 | 84.75 237 | 79.51 321 | 94.09 300 |
|
K. test v3 | | | 87.64 287 | 86.75 285 | 90.32 310 | 93.02 309 | 79.48 325 | 96.61 189 | 92.08 339 | 90.66 140 | 80.25 327 | 94.09 256 | 67.21 323 | 96.65 305 | 85.96 221 | 80.83 318 | 94.83 272 |
|
CMPMVS | | 62.92 21 | 85.62 302 | 84.92 297 | 87.74 320 | 89.14 335 | 73.12 338 | 94.17 287 | 96.80 200 | 73.98 342 | 73.65 337 | 94.93 202 | 66.36 324 | 97.61 271 | 83.95 254 | 91.28 215 | 92.48 322 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
lessismore_v0 | | | | | 90.45 308 | 91.96 323 | 79.09 328 | | 87.19 353 | | 80.32 325 | 94.39 233 | 66.31 325 | 97.55 274 | 84.00 253 | 76.84 326 | 94.70 281 |
|
Anonymous202405211 | | | 92.07 169 | 90.83 190 | 95.76 109 | 98.19 79 | 88.75 196 | 97.58 90 | 95.00 281 | 86.00 267 | 93.64 103 | 97.45 89 | 66.24 326 | 99.53 72 | 90.68 137 | 92.71 190 | 99.01 69 |
|
new-patchmatchnet | | | 83.18 309 | 81.87 310 | 87.11 322 | 86.88 342 | 75.99 334 | 93.70 296 | 95.18 273 | 85.02 279 | 77.30 334 | 88.40 331 | 65.99 327 | 93.88 336 | 74.19 325 | 70.18 345 | 91.47 338 |
|
FMVSNet1 | | | 89.88 252 | 88.31 260 | 94.59 171 | 95.41 200 | 91.18 112 | 97.50 96 | 96.93 191 | 86.62 259 | 87.41 260 | 94.51 224 | 65.94 328 | 97.29 291 | 83.04 263 | 87.43 255 | 95.31 243 |
|
TDRefinement | | | 86.53 294 | 84.76 299 | 91.85 281 | 82.23 349 | 84.25 284 | 96.38 207 | 95.35 263 | 84.97 280 | 84.09 296 | 94.94 201 | 65.76 329 | 98.34 185 | 84.60 243 | 74.52 339 | 92.97 311 |
|
UnsupCasMVSNet_eth | | | 85.99 299 | 84.45 300 | 90.62 306 | 89.97 331 | 82.40 302 | 93.62 300 | 97.37 143 | 89.86 157 | 78.59 332 | 92.37 295 | 65.25 330 | 95.35 330 | 82.27 274 | 70.75 344 | 94.10 298 |
|
LF4IMVS | | | 87.94 284 | 87.25 276 | 89.98 313 | 92.38 320 | 80.05 322 | 94.38 281 | 95.25 270 | 87.59 235 | 84.34 291 | 94.74 217 | 64.31 331 | 97.66 268 | 84.83 235 | 87.45 254 | 92.23 331 |
|
MIMVSNet | | | 88.50 275 | 86.76 284 | 93.72 218 | 94.84 235 | 87.77 237 | 91.39 325 | 94.05 314 | 86.41 261 | 87.99 250 | 92.59 291 | 63.27 332 | 95.82 323 | 77.44 312 | 92.84 189 | 97.57 155 |
|
FMVSNet5 | | | 87.29 290 | 85.79 291 | 91.78 285 | 94.80 237 | 87.28 243 | 95.49 257 | 95.28 267 | 84.09 290 | 83.85 299 | 91.82 304 | 62.95 333 | 94.17 334 | 78.48 309 | 85.34 272 | 93.91 302 |
|
testgi | | | 87.97 283 | 87.21 280 | 90.24 311 | 92.86 310 | 80.76 311 | 96.67 183 | 94.97 284 | 91.74 109 | 85.52 282 | 95.83 161 | 62.66 334 | 94.47 333 | 76.25 317 | 88.36 248 | 95.48 227 |
|
TinyColmap | | | 86.82 293 | 85.35 295 | 91.21 295 | 94.91 233 | 82.99 297 | 93.94 292 | 94.02 316 | 83.58 296 | 81.56 309 | 94.68 218 | 62.34 335 | 98.13 198 | 75.78 318 | 87.35 258 | 92.52 319 |
|
testpf | | | 80.97 315 | 81.40 313 | 79.65 333 | 91.53 324 | 72.43 339 | 73.47 356 | 89.55 348 | 78.63 329 | 80.81 312 | 89.06 326 | 61.36 336 | 91.36 345 | 83.34 259 | 84.89 288 | 75.15 352 |
|
new_pmnet | | | 82.89 310 | 81.12 315 | 88.18 319 | 89.63 333 | 80.18 320 | 91.77 324 | 92.57 337 | 76.79 336 | 75.56 336 | 88.23 333 | 61.22 337 | 94.48 332 | 71.43 331 | 82.92 308 | 89.87 341 |
|
test2356 | | | 82.77 311 | 82.14 309 | 84.65 326 | 85.77 343 | 70.36 341 | 91.22 328 | 93.69 321 | 81.58 312 | 81.82 306 | 89.00 327 | 60.63 338 | 90.77 346 | 64.74 340 | 90.80 223 | 92.82 313 |
|
OpenMVS_ROB | | 81.14 20 | 84.42 306 | 82.28 307 | 90.83 301 | 90.06 330 | 84.05 288 | 95.73 247 | 94.04 315 | 73.89 343 | 80.17 328 | 91.53 309 | 59.15 339 | 97.64 269 | 66.92 338 | 89.05 240 | 90.80 339 |
|
test1235678 | | | 79.82 317 | 78.53 318 | 83.69 328 | 82.55 348 | 67.55 347 | 92.50 319 | 94.13 313 | 79.28 326 | 72.10 340 | 86.45 341 | 57.27 340 | 90.68 347 | 61.60 344 | 80.90 317 | 92.82 313 |
|
MIMVSNet1 | | | 84.93 305 | 83.05 305 | 90.56 307 | 89.56 334 | 84.84 281 | 95.40 260 | 95.35 263 | 83.91 291 | 80.38 323 | 92.21 302 | 57.23 341 | 93.34 338 | 70.69 335 | 82.75 310 | 93.50 305 |
|
EG-PatchMatch MVS | | | 87.02 292 | 85.44 293 | 91.76 287 | 92.67 314 | 85.00 277 | 96.08 230 | 96.45 215 | 83.41 299 | 79.52 329 | 93.49 277 | 57.10 342 | 97.72 263 | 79.34 307 | 90.87 222 | 92.56 318 |
|
UnsupCasMVSNet_bld | | | 82.13 314 | 79.46 316 | 90.14 312 | 88.00 338 | 82.47 300 | 90.89 332 | 96.62 213 | 78.94 328 | 75.61 335 | 84.40 343 | 56.63 343 | 96.31 308 | 77.30 315 | 66.77 349 | 91.63 335 |
|
1111 | | | 78.29 319 | 77.55 319 | 80.50 331 | 83.89 344 | 59.98 354 | 91.89 322 | 93.71 318 | 75.06 338 | 73.60 338 | 87.67 336 | 55.66 344 | 92.60 341 | 58.54 348 | 77.92 323 | 88.93 343 |
|
.test1245 | | | 65.38 327 | 69.22 325 | 53.86 347 | 83.89 344 | 59.98 354 | 91.89 322 | 93.71 318 | 75.06 338 | 73.60 338 | 87.67 336 | 55.66 344 | 92.60 341 | 58.54 348 | 2.96 361 | 9.00 361 |
|
Test4 | | | 89.48 257 | 87.50 268 | 95.44 129 | 90.76 328 | 89.72 151 | 95.78 246 | 97.09 167 | 90.28 150 | 77.67 333 | 91.74 307 | 55.42 346 | 98.08 206 | 91.92 112 | 96.83 117 | 98.52 103 |
|
testing_2 | | | 87.33 289 | 85.03 296 | 94.22 186 | 87.77 340 | 89.32 182 | 94.97 272 | 97.11 165 | 89.22 172 | 71.64 341 | 88.73 328 | 55.16 347 | 97.94 241 | 91.95 111 | 88.73 245 | 95.41 233 |
|
testus | | | 82.63 312 | 82.15 308 | 84.07 327 | 87.31 341 | 67.67 346 | 93.18 305 | 94.29 310 | 82.47 304 | 82.14 304 | 90.69 310 | 53.01 348 | 91.94 343 | 66.30 339 | 89.96 233 | 92.62 317 |
|
tmp_tt | | | 51.94 335 | 53.82 332 | 46.29 348 | 33.73 364 | 45.30 364 | 78.32 355 | 67.24 364 | 18.02 359 | 50.93 353 | 87.05 340 | 52.99 349 | 53.11 362 | 70.76 334 | 25.29 359 | 40.46 359 |
|
pmmvs3 | | | 79.97 316 | 77.50 320 | 87.39 321 | 82.80 347 | 79.38 326 | 92.70 316 | 90.75 345 | 70.69 346 | 78.66 331 | 87.47 339 | 51.34 350 | 93.40 337 | 73.39 327 | 69.65 346 | 89.38 342 |
|
DeepMVS_CX | | | | | 74.68 340 | 90.84 327 | 64.34 351 | | 81.61 360 | 65.34 348 | 67.47 345 | 88.01 335 | 48.60 351 | 80.13 357 | 62.33 343 | 73.68 343 | 79.58 350 |
|
test12356 | | | 74.97 320 | 74.13 321 | 77.49 336 | 78.81 350 | 56.23 358 | 88.53 343 | 92.75 335 | 75.14 337 | 67.50 344 | 85.07 342 | 44.88 352 | 89.96 348 | 58.71 347 | 75.75 329 | 86.26 344 |
|
PM-MVS | | | 83.48 308 | 81.86 311 | 88.31 317 | 87.83 339 | 77.59 331 | 93.43 302 | 91.75 341 | 86.91 253 | 80.63 317 | 89.91 313 | 44.42 353 | 95.84 322 | 85.17 234 | 76.73 327 | 91.50 337 |
|
ambc | | | | | 86.56 324 | 83.60 346 | 70.00 344 | 85.69 348 | 94.97 284 | | 80.60 318 | 88.45 330 | 37.42 354 | 96.84 303 | 82.69 269 | 75.44 330 | 92.86 312 |
|
testmv | | | 72.22 322 | 70.02 322 | 78.82 334 | 73.06 357 | 61.75 352 | 91.24 327 | 92.31 338 | 74.45 341 | 61.06 349 | 80.51 346 | 34.21 355 | 88.63 351 | 55.31 351 | 68.07 348 | 86.06 345 |
|
Gipuma | | | 67.86 326 | 65.41 327 | 75.18 339 | 92.66 315 | 73.45 337 | 66.50 358 | 94.52 302 | 53.33 352 | 57.80 351 | 66.07 354 | 30.81 356 | 89.20 350 | 48.15 355 | 78.88 322 | 62.90 356 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
no-one | | | 68.12 325 | 63.78 328 | 81.13 330 | 74.01 354 | 70.22 343 | 87.61 346 | 90.71 346 | 72.63 345 | 53.13 352 | 71.89 351 | 30.29 357 | 91.45 344 | 61.53 345 | 32.21 355 | 81.72 349 |
|
EMVS | | | 52.08 334 | 51.31 334 | 54.39 346 | 72.62 358 | 45.39 363 | 83.84 350 | 75.51 362 | 41.13 357 | 40.77 357 | 59.65 357 | 30.08 358 | 73.60 360 | 28.31 359 | 29.90 358 | 44.18 358 |
|
FPMVS | | | 71.27 323 | 69.85 323 | 75.50 338 | 74.64 352 | 59.03 356 | 91.30 326 | 91.50 342 | 58.80 350 | 57.92 350 | 88.28 332 | 29.98 359 | 85.53 354 | 53.43 352 | 82.84 309 | 81.95 348 |
|
E-PMN | | | 53.28 332 | 52.56 333 | 55.43 345 | 74.43 353 | 47.13 361 | 83.63 351 | 76.30 361 | 42.23 356 | 42.59 355 | 62.22 356 | 28.57 360 | 74.40 359 | 31.53 358 | 31.51 356 | 44.78 357 |
|
PMMVS2 | | | 70.19 324 | 66.92 326 | 80.01 332 | 76.35 351 | 65.67 349 | 86.22 347 | 87.58 352 | 64.83 349 | 62.38 348 | 80.29 347 | 26.78 361 | 88.49 352 | 63.79 341 | 54.07 351 | 85.88 346 |
|
ANet_high | | | 63.94 328 | 59.58 329 | 77.02 337 | 61.24 362 | 66.06 348 | 85.66 349 | 87.93 351 | 78.53 331 | 42.94 354 | 71.04 352 | 25.42 362 | 80.71 356 | 52.60 353 | 30.83 357 | 84.28 347 |
|
LCM-MVSNet | | | 72.55 321 | 69.39 324 | 82.03 329 | 70.81 359 | 65.42 350 | 90.12 337 | 94.36 307 | 55.02 351 | 65.88 346 | 81.72 344 | 24.16 363 | 89.96 348 | 74.32 324 | 68.10 347 | 90.71 340 |
|
PNet_i23d | | | 59.01 329 | 55.87 330 | 68.44 342 | 73.98 355 | 51.37 359 | 81.36 352 | 82.41 358 | 52.37 353 | 42.49 356 | 70.39 353 | 11.39 364 | 79.99 358 | 49.77 354 | 38.71 353 | 73.97 353 |
|
PMVS | | 53.92 22 | 58.58 330 | 55.40 331 | 68.12 343 | 51.00 363 | 48.64 360 | 78.86 354 | 87.10 354 | 46.77 355 | 35.84 359 | 74.28 349 | 8.76 365 | 86.34 353 | 42.07 356 | 73.91 342 | 69.38 354 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
wuyk23d | | | 25.11 337 | 24.57 339 | 26.74 350 | 73.98 355 | 39.89 365 | 57.88 359 | 9.80 366 | 12.27 360 | 10.39 361 | 6.97 364 | 7.03 366 | 36.44 363 | 25.43 360 | 17.39 360 | 3.89 363 |
|
wuykxyi23d | | | 56.92 331 | 51.11 335 | 74.38 341 | 62.30 361 | 61.47 353 | 80.09 353 | 84.87 355 | 49.62 354 | 30.80 360 | 57.20 358 | 7.03 366 | 82.94 355 | 55.69 350 | 32.36 354 | 78.72 351 |
|
MVE | | 50.73 23 | 53.25 333 | 48.81 336 | 66.58 344 | 65.34 360 | 57.50 357 | 72.49 357 | 70.94 363 | 40.15 358 | 39.28 358 | 63.51 355 | 6.89 368 | 73.48 361 | 38.29 357 | 42.38 352 | 68.76 355 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test123 | | | 13.04 340 | 15.66 341 | 5.18 351 | 4.51 366 | 3.45 366 | 92.50 319 | 1.81 368 | 2.50 362 | 7.58 363 | 20.15 361 | 3.67 369 | 2.18 365 | 7.13 362 | 1.07 363 | 9.90 360 |
|
testmvs | | | 13.36 339 | 16.33 340 | 4.48 352 | 5.04 365 | 2.26 367 | 93.18 305 | 3.28 367 | 2.70 361 | 8.24 362 | 21.66 360 | 2.29 370 | 2.19 364 | 7.58 361 | 2.96 361 | 9.00 361 |
|
sosnet-low-res | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
sosnet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
uncertanet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
Regformer | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
ab-mvs-re | | | 8.06 341 | 10.74 342 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 96.69 118 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
uanet | | | 0.00 343 | 0.00 344 | 0.00 353 | 0.00 367 | 0.00 368 | 0.00 360 | 0.00 369 | 0.00 363 | 0.00 364 | 0.00 365 | 0.00 371 | 0.00 366 | 0.00 363 | 0.00 364 | 0.00 364 |
|
GSMVS | | | | | | | | | | | | | | | | | 98.45 114 |
|
test_part3 | | | | | | | | 97.50 96 | | 93.81 45 | | 98.53 12 | | 99.87 5 | 95.19 49 | | |
|
test_part2 | | | | | | 99.28 17 | 95.74 3 | | | | 98.10 7 | | | | | | |
|
MTGPA | | | | | | | | | 98.08 51 | | | | | | | | |
|
MTMP | | | | | | | | 97.86 50 | 82.03 359 | | | | | | | | |
|
gm-plane-assit | | | | | | 93.22 303 | 78.89 329 | | | 84.82 282 | | 93.52 275 | | 98.64 155 | 87.72 184 | | |
|
test9_res | | | | | | | | | | | | | | | 94.81 64 | 99.38 36 | 99.45 31 |
|
agg_prior2 | | | | | | | | | | | | | | | 93.94 76 | 99.38 36 | 99.50 25 |
|
agg_prior | | | | | | 98.67 41 | 93.79 38 | | 98.00 72 | | 95.68 70 | | | 99.57 64 | | | |
|
test_prior4 | | | | | | | 93.66 42 | 96.42 200 | | | | | | | | | |
|
test_prior | | | | | 97.23 50 | 98.67 41 | 92.99 58 | | 98.00 72 | | | | | 99.41 88 | | | 99.29 46 |
|
旧先验2 | | | | | | | | 95.94 237 | | 81.66 310 | 97.34 18 | | | 98.82 141 | 92.26 101 | | |
|
新几何2 | | | | | | | | 95.79 244 | | | | | | | | | |
|
无先验 | | | | | | | | 95.79 244 | 97.87 87 | 83.87 294 | | | | 99.65 42 | 87.68 187 | | 98.89 82 |
|
原ACMM2 | | | | | | | | 95.67 248 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.67 40 | 85.96 221 | | |
|
testdata1 | | | | | | | | 95.26 268 | | 93.10 67 | | | | | | | |
|
plane_prior7 | | | | | | 96.21 171 | 89.98 142 | | | | | | | | | | |
|
plane_prior5 | | | | | | | | | 97.51 121 | | | | | 98.60 159 | 93.02 95 | 92.23 196 | 95.86 209 |
|
plane_prior4 | | | | | | | | | | | | 96.64 121 | | | | | |
|
plane_prior3 | | | | | | | 90.00 138 | | | 94.46 30 | 91.34 158 | | | | | | |
|
plane_prior2 | | | | | | | | 97.74 61 | | 94.85 17 | | | | | | | |
|
plane_prior1 | | | | | | 96.14 179 | | | | | | | | | | | |
|
plane_prior | | | | | | | 89.99 140 | 97.24 121 | | 94.06 38 | | | | | | 92.16 200 | |
|
n2 | | | | | | | | | 0.00 369 | | | | | | | | |
|
nn | | | | | | | | | 0.00 369 | | | | | | | | |
|
door-mid | | | | | | | | | 91.06 344 | | | | | | | | |
|
test11 | | | | | | | | | 97.88 85 | | | | | | | | |
|
door | | | | | | | | | 91.13 343 | | | | | | | | |
|
HQP5-MVS | | | | | | | 89.33 180 | | | | | | | | | | |
|
HQP-NCC | | | | | | 95.86 185 | | 96.65 184 | | 93.55 50 | 90.14 186 | | | | | | |
|
ACMP_Plane | | | | | | 95.86 185 | | 96.65 184 | | 93.55 50 | 90.14 186 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 92.13 107 | | |
|
HQP4-MVS | | | | | | | | | | | 90.14 186 | | | 98.50 167 | | | 95.78 216 |
|
HQP3-MVS | | | | | | | | | 97.39 140 | | | | | | | 92.10 201 | |
|
NP-MVS | | | | | | 95.99 184 | 89.81 149 | | | | | 95.87 158 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 90.30 230 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 91.02 220 | |
|