SteuartSystems-ACMMP | | | 98.90 2 | 98.75 2 | 99.36 12 | 99.22 72 | 98.43 17 | 99.10 49 | 98.87 49 | 97.38 17 | 99.35 5 | 99.40 6 | 97.78 1 | 99.87 35 | 97.77 39 | 99.85 2 | 99.78 7 |
Skip Steuart: Steuart Systems R&D Blog. |
test_part1 | | | | | | | | | 98.84 54 | | | | 97.38 2 | | | 99.78 14 | 99.76 20 |
|
CNVR-MVS | | | 98.78 3 | 98.56 6 | 99.45 8 | 99.32 46 | 98.87 6 | 98.47 160 | 98.81 61 | 97.72 4 | 98.76 34 | 99.16 42 | 97.05 3 | 99.78 74 | 98.06 25 | 99.66 43 | 99.69 36 |
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segment_acmp | | | | | | | | | | | | | 96.85 4 | | | | |
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MCST-MVS | | | 98.65 9 | 98.37 17 | 99.48 5 | 99.60 23 | 98.87 6 | 98.41 166 | 98.68 97 | 97.04 38 | 98.52 45 | 98.80 84 | 96.78 5 | 99.83 43 | 97.93 28 | 99.61 49 | 99.74 26 |
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APDe-MVS | | | 99.02 1 | 98.84 1 | 99.55 1 | 99.57 24 | 98.96 3 | 99.39 5 | 98.93 36 | 97.38 17 | 99.41 3 | 99.54 1 | 96.66 6 | 99.84 42 | 98.86 2 | 99.85 2 | 99.87 1 |
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NCCC | | | 98.61 13 | 98.35 20 | 99.38 10 | 99.28 61 | 98.61 11 | 98.45 161 | 98.76 75 | 97.82 3 | 98.45 49 | 98.93 73 | 96.65 7 | 99.83 43 | 97.38 57 | 99.41 77 | 99.71 33 |
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SD-MVS | | | 98.64 10 | 98.68 3 | 98.53 73 | 99.33 43 | 98.36 22 | 98.90 71 | 98.85 53 | 97.28 21 | 99.72 1 | 99.39 7 | 96.63 8 | 97.60 291 | 98.17 23 | 99.85 2 | 99.64 54 |
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PHI-MVS | | | 98.34 36 | 98.06 37 | 99.18 32 | 99.15 79 | 98.12 38 | 99.04 57 | 99.09 19 | 93.32 185 | 98.83 30 | 99.10 48 | 96.54 9 | 99.83 43 | 97.70 43 | 99.76 24 | 99.59 62 |
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MSLP-MVS++ | | | 98.56 21 | 98.57 5 | 98.55 71 | 99.26 64 | 96.80 84 | 98.71 121 | 99.05 23 | 97.28 21 | 98.84 28 | 99.28 25 | 96.47 10 | 99.40 131 | 98.52 14 | 99.70 38 | 99.47 78 |
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TSAR-MVS + MP. | | | 98.78 3 | 98.62 4 | 99.24 25 | 99.69 17 | 98.28 28 | 99.14 42 | 98.66 107 | 96.84 43 | 99.56 2 | 99.31 21 | 96.34 11 | 99.70 91 | 98.32 20 | 99.73 35 | 99.73 28 |
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TSAR-MVS + GP. | | | 98.38 32 | 98.24 31 | 98.81 58 | 99.22 72 | 97.25 70 | 98.11 203 | 98.29 167 | 97.19 30 | 98.99 21 | 99.02 58 | 96.22 12 | 99.67 96 | 98.52 14 | 98.56 111 | 99.51 70 |
|
TEST9 | | | | | | 99.31 48 | 98.50 13 | 97.92 220 | 98.73 84 | 92.63 205 | 97.74 82 | 98.68 94 | 96.20 13 | 99.80 57 | | | |
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train_agg | | | 97.97 44 | 97.52 54 | 99.33 15 | 99.31 48 | 98.50 13 | 97.92 220 | 98.73 84 | 92.98 196 | 97.74 82 | 98.68 94 | 96.20 13 | 99.80 57 | 96.59 85 | 99.57 56 | 99.68 42 |
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test_8 | | | | | | 99.29 56 | 98.44 15 | 97.89 228 | 98.72 86 | 92.98 196 | 97.70 85 | 98.66 97 | 96.20 13 | 99.80 57 | | | |
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agg_prior1 | | | 97.95 46 | 97.51 55 | 99.28 20 | 99.30 53 | 98.38 18 | 97.81 235 | 98.72 86 | 93.16 190 | 97.57 94 | 98.66 97 | 96.14 16 | 99.81 50 | 96.63 84 | 99.56 62 | 99.66 49 |
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Regformer-2 | | | 98.69 7 | 98.52 8 | 99.19 28 | 99.35 38 | 98.01 42 | 98.37 169 | 98.81 61 | 97.48 11 | 99.21 10 | 99.21 32 | 96.13 17 | 99.80 57 | 98.40 18 | 99.73 35 | 99.75 21 |
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DeepPCF-MVS | | 96.37 2 | 97.93 48 | 98.48 13 | 96.30 227 | 99.00 87 | 89.54 289 | 97.43 259 | 98.87 49 | 98.16 2 | 99.26 7 | 99.38 11 | 96.12 18 | 99.64 100 | 98.30 21 | 99.77 18 | 99.72 31 |
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agg_prior3 | | | 97.87 50 | 97.42 60 | 99.23 27 | 99.29 56 | 98.23 29 | 97.92 220 | 98.72 86 | 92.38 222 | 97.59 93 | 98.64 99 | 96.09 19 | 99.79 69 | 96.59 85 | 99.57 56 | 99.68 42 |
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Regformer-1 | | | 98.66 8 | 98.51 10 | 99.12 40 | 99.35 38 | 97.81 51 | 98.37 169 | 98.76 75 | 97.49 10 | 99.20 11 | 99.21 32 | 96.08 20 | 99.79 69 | 98.42 16 | 99.73 35 | 99.75 21 |
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HFP-MVS | | | 98.63 12 | 98.40 14 | 99.32 16 | 99.72 11 | 98.29 26 | 99.23 22 | 98.96 31 | 96.10 65 | 98.94 22 | 99.17 39 | 96.06 21 | 99.92 13 | 97.62 45 | 99.78 14 | 99.75 21 |
|
#test# | | | 98.54 24 | 98.27 27 | 99.32 16 | 99.72 11 | 98.29 26 | 98.98 64 | 98.96 31 | 95.65 78 | 98.94 22 | 99.17 39 | 96.06 21 | 99.92 13 | 97.21 60 | 99.78 14 | 99.75 21 |
|
CP-MVS | | | 98.57 20 | 98.36 18 | 99.19 28 | 99.66 19 | 97.86 47 | 99.34 11 | 98.87 49 | 95.96 68 | 98.60 42 | 99.13 44 | 96.05 23 | 99.94 3 | 97.77 39 | 99.86 1 | 99.77 14 |
|
HSP-MVS | | | 98.70 5 | 98.52 8 | 99.24 25 | 99.75 3 | 98.23 29 | 99.26 17 | 98.58 120 | 97.52 7 | 99.41 3 | 98.78 85 | 96.00 24 | 99.79 69 | 97.79 38 | 99.59 53 | 99.69 36 |
|
MVS_111021_HR | | | 98.47 28 | 98.34 21 | 98.88 56 | 99.22 72 | 97.32 65 | 97.91 223 | 99.58 3 | 97.20 29 | 98.33 54 | 99.00 63 | 95.99 25 | 99.64 100 | 98.05 26 | 99.76 24 | 99.69 36 |
|
test_prior3 | | | 98.22 42 | 97.90 43 | 99.19 28 | 99.31 48 | 98.22 31 | 97.80 236 | 98.84 54 | 96.12 63 | 97.89 76 | 98.69 92 | 95.96 26 | 99.70 91 | 96.89 71 | 99.60 50 | 99.65 51 |
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test_prior2 | | | | | | | | 97.80 236 | | 96.12 63 | 97.89 76 | 98.69 92 | 95.96 26 | | 96.89 71 | 99.60 50 | |
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CDPH-MVS | | | 97.94 47 | 97.49 56 | 99.28 20 | 99.47 32 | 98.44 15 | 97.91 223 | 98.67 104 | 92.57 209 | 98.77 33 | 98.85 79 | 95.93 28 | 99.72 86 | 95.56 118 | 99.69 39 | 99.68 42 |
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region2R | | | 98.61 13 | 98.38 16 | 99.29 18 | 99.74 7 | 98.16 35 | 99.23 22 | 98.93 36 | 96.15 60 | 98.94 22 | 99.17 39 | 95.91 29 | 99.94 3 | 97.55 50 | 99.79 10 | 99.78 7 |
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XVS | | | 98.70 5 | 98.49 12 | 99.34 13 | 99.70 15 | 98.35 23 | 99.29 14 | 98.88 47 | 97.40 14 | 98.46 46 | 99.20 35 | 95.90 30 | 99.89 27 | 97.85 34 | 99.74 33 | 99.78 7 |
|
X-MVStestdata | | | 94.06 239 | 92.30 257 | 99.34 13 | 99.70 15 | 98.35 23 | 99.29 14 | 98.88 47 | 97.40 14 | 98.46 46 | 43.50 346 | 95.90 30 | 99.89 27 | 97.85 34 | 99.74 33 | 99.78 7 |
|
Regformer-4 | | | 98.64 10 | 98.53 7 | 98.99 47 | 99.43 36 | 97.37 64 | 98.40 167 | 98.79 69 | 97.46 12 | 99.09 14 | 99.31 21 | 95.86 32 | 99.80 57 | 98.64 4 | 99.76 24 | 99.79 4 |
|
Regformer-3 | | | 98.59 16 | 98.50 11 | 98.86 57 | 99.43 36 | 97.05 75 | 98.40 167 | 98.68 97 | 97.43 13 | 99.06 15 | 99.31 21 | 95.80 33 | 99.77 79 | 98.62 6 | 99.76 24 | 99.78 7 |
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HPM-MVS++ | | | 98.58 18 | 98.25 29 | 99.55 1 | 99.50 28 | 99.08 2 | 98.72 120 | 98.66 107 | 97.51 8 | 98.15 56 | 98.83 81 | 95.70 34 | 99.92 13 | 97.53 52 | 99.67 40 | 99.66 49 |
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ACMMPR | | | 98.59 16 | 98.36 18 | 99.29 18 | 99.74 7 | 98.15 36 | 99.23 22 | 98.95 33 | 96.10 65 | 98.93 26 | 99.19 38 | 95.70 34 | 99.94 3 | 97.62 45 | 99.79 10 | 99.78 7 |
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旧先验1 | | | | | | 99.29 56 | 97.48 60 | | 98.70 93 | | | 99.09 52 | 95.56 36 | | | 99.47 70 | 99.61 57 |
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PGM-MVS | | | 98.49 27 | 98.23 32 | 99.27 23 | 99.72 11 | 98.08 39 | 98.99 61 | 99.49 5 | 95.43 86 | 99.03 16 | 99.32 20 | 95.56 36 | 99.94 3 | 96.80 79 | 99.77 18 | 99.78 7 |
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APD-MVS | | | 98.35 35 | 98.00 40 | 99.42 9 | 99.51 27 | 98.72 8 | 98.80 99 | 98.82 58 | 94.52 130 | 99.23 9 | 99.25 28 | 95.54 38 | 99.80 57 | 96.52 89 | 99.77 18 | 99.74 26 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
mPP-MVS | | | 98.51 26 | 98.26 28 | 99.25 24 | 99.75 3 | 98.04 40 | 99.28 16 | 98.81 61 | 96.24 58 | 98.35 53 | 99.23 29 | 95.46 39 | 99.94 3 | 97.42 55 | 99.81 8 | 99.77 14 |
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EI-MVSNet-Vis-set | | | 98.47 28 | 98.39 15 | 98.69 62 | 99.46 33 | 96.49 97 | 98.30 180 | 98.69 94 | 97.21 28 | 98.84 28 | 99.36 16 | 95.41 40 | 99.78 74 | 98.62 6 | 99.65 44 | 99.80 3 |
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ACMMP_Plus | | | 98.61 13 | 98.30 25 | 99.55 1 | 99.62 22 | 98.95 4 | 98.82 90 | 98.81 61 | 95.80 72 | 99.16 13 | 99.47 4 | 95.37 41 | 99.92 13 | 97.89 32 | 99.75 30 | 99.79 4 |
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CSCG | | | 97.85 52 | 97.74 46 | 98.20 92 | 99.67 18 | 95.16 157 | 99.22 28 | 99.32 7 | 93.04 193 | 97.02 108 | 98.92 75 | 95.36 42 | 99.91 22 | 97.43 54 | 99.64 46 | 99.52 67 |
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DP-MVS Recon | | | 97.86 51 | 97.46 58 | 99.06 45 | 99.53 26 | 98.35 23 | 98.33 173 | 98.89 44 | 92.62 206 | 98.05 61 | 98.94 72 | 95.34 43 | 99.65 98 | 96.04 100 | 99.42 76 | 99.19 105 |
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APD-MVS_3200maxsize | | | 98.53 25 | 98.33 24 | 99.15 37 | 99.50 28 | 97.92 46 | 99.15 41 | 98.81 61 | 96.24 58 | 99.20 11 | 99.37 12 | 95.30 44 | 99.80 57 | 97.73 41 | 99.67 40 | 99.72 31 |
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DeepC-MVS_fast | | 96.70 1 | 98.55 22 | 98.34 21 | 99.18 32 | 99.25 65 | 98.04 40 | 98.50 157 | 98.78 71 | 97.72 4 | 98.92 27 | 99.28 25 | 95.27 45 | 99.82 48 | 97.55 50 | 99.77 18 | 99.69 36 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MP-MVS-pluss | | | 98.31 39 | 97.92 42 | 99.49 4 | 99.72 11 | 98.88 5 | 98.43 164 | 98.78 71 | 94.10 140 | 97.69 86 | 99.42 5 | 95.25 46 | 99.92 13 | 98.09 24 | 99.80 9 | 99.67 47 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
EI-MVSNet-UG-set | | | 98.41 30 | 98.34 21 | 98.61 67 | 99.45 34 | 96.32 104 | 98.28 182 | 98.68 97 | 97.17 31 | 98.74 35 | 99.37 12 | 95.25 46 | 99.79 69 | 98.57 8 | 99.54 65 | 99.73 28 |
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原ACMM1 | | | | | 98.65 65 | 99.32 46 | 96.62 90 | | 98.67 104 | 93.27 188 | 97.81 78 | 98.97 65 | 95.18 48 | 99.83 43 | 93.84 161 | 99.46 73 | 99.50 72 |
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HPM-MVS_fast | | | 98.38 32 | 98.13 35 | 99.12 40 | 99.75 3 | 97.86 47 | 99.44 4 | 98.82 58 | 94.46 134 | 98.94 22 | 99.20 35 | 95.16 49 | 99.74 85 | 97.58 47 | 99.85 2 | 99.77 14 |
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test12 | | | | | 99.18 32 | 99.16 77 | 98.19 33 | | 98.53 128 | | 98.07 60 | | 95.13 50 | 99.72 86 | | 99.56 62 | 99.63 56 |
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HPM-MVS | | | 98.36 34 | 98.10 36 | 99.13 38 | 99.74 7 | 97.82 50 | 99.53 1 | 98.80 68 | 94.63 127 | 98.61 41 | 98.97 65 | 95.13 50 | 99.77 79 | 97.65 44 | 99.83 7 | 99.79 4 |
|
MVS_111021_LR | | | 98.34 36 | 98.23 32 | 98.67 64 | 99.27 62 | 96.90 81 | 97.95 218 | 99.58 3 | 97.14 33 | 98.44 50 | 99.01 62 | 95.03 52 | 99.62 105 | 97.91 29 | 99.75 30 | 99.50 72 |
|
DELS-MVS | | | 98.40 31 | 98.20 34 | 98.99 47 | 99.00 87 | 97.66 53 | 97.75 240 | 98.89 44 | 97.71 6 | 98.33 54 | 98.97 65 | 94.97 53 | 99.88 34 | 98.42 16 | 99.76 24 | 99.42 86 |
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 |
PLC | | 95.07 4 | 97.20 83 | 96.78 85 | 98.44 80 | 99.29 56 | 96.31 106 | 98.14 198 | 98.76 75 | 92.41 220 | 96.39 149 | 98.31 130 | 94.92 54 | 99.78 74 | 94.06 156 | 98.77 102 | 99.23 103 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MPTG | | | 98.55 22 | 98.25 29 | 99.46 6 | 99.76 1 | 98.64 9 | 98.55 149 | 98.74 79 | 97.27 25 | 98.02 65 | 99.39 7 | 94.81 55 | 99.96 1 | 97.91 29 | 99.79 10 | 99.77 14 |
|
MTAPA | | | 98.58 18 | 98.29 26 | 99.46 6 | 99.76 1 | 98.64 9 | 98.90 71 | 98.74 79 | 97.27 25 | 98.02 65 | 99.39 7 | 94.81 55 | 99.96 1 | 97.91 29 | 99.79 10 | 99.77 14 |
|
1121 | | | 97.37 77 | 96.77 87 | 99.16 35 | 99.34 40 | 97.99 45 | 98.19 192 | 98.68 97 | 90.14 271 | 98.01 67 | 98.97 65 | 94.80 57 | 99.87 35 | 93.36 172 | 99.46 73 | 99.61 57 |
|
Test By Simon | | | | | | | | | | | | | 94.64 58 | | | | |
|
新几何1 | | | | | 99.16 35 | 99.34 40 | 98.01 42 | | 98.69 94 | 90.06 273 | 98.13 57 | 98.95 71 | 94.60 59 | 99.89 27 | 91.97 213 | 99.47 70 | 99.59 62 |
|
MP-MVS | | | 98.33 38 | 98.01 39 | 99.28 20 | 99.75 3 | 98.18 34 | 99.22 28 | 98.79 69 | 96.13 62 | 97.92 74 | 99.23 29 | 94.54 60 | 99.94 3 | 96.74 81 | 99.78 14 | 99.73 28 |
|
pcd_1.5k_mvsjas | | | 7.88 330 | 10.50 331 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 94.51 61 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
PS-MVSNAJss | | | 96.43 110 | 96.26 105 | 96.92 174 | 95.84 289 | 95.08 161 | 99.16 40 | 98.50 137 | 95.87 70 | 93.84 223 | 98.34 127 | 94.51 61 | 98.61 210 | 96.88 74 | 93.45 225 | 97.06 203 |
|
PS-MVSNAJ | | | 97.73 55 | 97.77 44 | 97.62 128 | 98.68 119 | 95.58 141 | 97.34 268 | 98.51 132 | 97.29 20 | 98.66 38 | 97.88 161 | 94.51 61 | 99.90 25 | 97.87 33 | 99.17 87 | 97.39 191 |
|
API-MVS | | | 97.41 74 | 97.25 65 | 97.91 109 | 98.70 116 | 96.80 84 | 98.82 90 | 98.69 94 | 94.53 129 | 98.11 58 | 98.28 131 | 94.50 64 | 99.57 113 | 94.12 155 | 99.49 68 | 97.37 193 |
|
ACMMP | | | 98.23 41 | 97.95 41 | 99.09 42 | 99.74 7 | 97.62 56 | 99.03 58 | 99.41 6 | 95.98 67 | 97.60 92 | 99.36 16 | 94.45 65 | 99.93 9 | 97.14 61 | 98.85 98 | 99.70 35 |
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 |
testdata | | | | | 98.26 89 | 99.20 75 | 95.36 150 | | 98.68 97 | 91.89 233 | 98.60 42 | 99.10 48 | 94.44 66 | 99.82 48 | 94.27 151 | 99.44 75 | 99.58 64 |
|
xiu_mvs_v2_base | | | 97.66 60 | 97.70 47 | 97.56 136 | 98.61 125 | 95.46 147 | 97.44 257 | 98.46 142 | 97.15 32 | 98.65 39 | 98.15 141 | 94.33 67 | 99.80 57 | 97.84 36 | 98.66 107 | 97.41 189 |
|
PAPR | | | 96.84 97 | 96.24 106 | 98.65 65 | 98.72 115 | 96.92 80 | 97.36 266 | 98.57 121 | 93.33 184 | 96.67 126 | 97.57 189 | 94.30 68 | 99.56 115 | 91.05 234 | 98.59 109 | 99.47 78 |
|
PAPM_NR | | | 97.46 67 | 97.11 71 | 98.50 75 | 99.50 28 | 96.41 100 | 98.63 136 | 98.60 114 | 95.18 104 | 97.06 106 | 98.06 147 | 94.26 69 | 99.57 113 | 93.80 163 | 98.87 97 | 99.52 67 |
|
test222 | | | | | | 99.23 71 | 97.17 73 | 97.40 260 | 98.66 107 | 88.68 297 | 98.05 61 | 98.96 69 | 94.14 70 | | | 99.53 66 | 99.61 57 |
|
EPP-MVSNet | | | 97.46 67 | 97.28 64 | 97.99 106 | 98.64 122 | 95.38 149 | 99.33 13 | 98.31 162 | 93.61 172 | 97.19 100 | 99.07 55 | 94.05 71 | 99.23 143 | 96.89 71 | 98.43 118 | 99.37 88 |
|
F-COLMAP | | | 97.09 89 | 96.80 82 | 97.97 107 | 99.45 34 | 94.95 169 | 98.55 149 | 98.62 113 | 93.02 194 | 96.17 153 | 98.58 105 | 94.01 72 | 99.81 50 | 93.95 158 | 98.90 94 | 99.14 113 |
|
TAPA-MVS | | 93.98 7 | 95.35 165 | 94.56 176 | 97.74 118 | 99.13 80 | 94.83 186 | 98.33 173 | 98.64 112 | 86.62 306 | 96.29 151 | 98.61 100 | 94.00 73 | 99.29 139 | 80.00 318 | 99.41 77 | 99.09 116 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MG-MVS | | | 97.81 53 | 97.60 49 | 98.44 80 | 99.12 81 | 95.97 116 | 97.75 240 | 98.78 71 | 96.89 42 | 98.46 46 | 99.22 31 | 93.90 74 | 99.68 95 | 94.81 137 | 99.52 67 | 99.67 47 |
|
CDS-MVSNet | | | 96.99 91 | 96.69 89 | 97.90 110 | 98.05 155 | 95.98 112 | 98.20 188 | 98.33 161 | 93.67 170 | 96.95 109 | 98.49 111 | 93.54 75 | 98.42 241 | 95.24 130 | 97.74 142 | 99.31 92 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
TAMVS | | | 97.02 90 | 96.79 84 | 97.70 122 | 98.06 154 | 95.31 154 | 98.52 152 | 98.31 162 | 93.95 149 | 97.05 107 | 98.61 100 | 93.49 76 | 98.52 224 | 95.33 124 | 97.81 138 | 99.29 97 |
|
abl_6 | | | 98.30 40 | 98.03 38 | 99.13 38 | 99.56 25 | 97.76 52 | 99.13 45 | 98.82 58 | 96.14 61 | 99.26 7 | 99.37 12 | 93.33 77 | 99.93 9 | 96.96 67 | 99.67 40 | 99.69 36 |
|
CNLPA | | | 97.45 70 | 97.03 75 | 98.73 60 | 99.05 82 | 97.44 63 | 98.07 207 | 98.53 128 | 95.32 98 | 96.80 123 | 98.53 107 | 93.32 78 | 99.72 86 | 94.31 150 | 99.31 83 | 99.02 122 |
|
OMC-MVS | | | 97.55 66 | 97.34 62 | 98.20 92 | 99.33 43 | 95.92 128 | 98.28 182 | 98.59 115 | 95.52 83 | 97.97 70 | 99.10 48 | 93.28 79 | 99.49 125 | 95.09 132 | 98.88 95 | 99.19 105 |
|
UA-Net | | | 97.96 45 | 97.62 48 | 98.98 49 | 98.86 105 | 97.47 61 | 98.89 75 | 99.08 20 | 96.67 49 | 98.72 36 | 99.54 1 | 93.15 80 | 99.81 50 | 94.87 134 | 98.83 99 | 99.65 51 |
|
CPTT-MVS | | | 97.72 56 | 97.32 63 | 98.92 53 | 99.64 20 | 97.10 74 | 99.12 47 | 98.81 61 | 92.34 223 | 98.09 59 | 99.08 54 | 93.01 81 | 99.92 13 | 96.06 99 | 99.77 18 | 99.75 21 |
|
114514_t | | | 96.93 93 | 96.27 104 | 98.92 53 | 99.50 28 | 97.63 55 | 98.85 84 | 98.90 42 | 84.80 318 | 97.77 79 | 99.11 46 | 92.84 82 | 99.66 97 | 94.85 135 | 99.77 18 | 99.47 78 |
|
PVSNet_Blended_VisFu | | | 97.70 57 | 97.46 58 | 98.44 80 | 99.27 62 | 95.91 130 | 98.63 136 | 99.16 17 | 94.48 133 | 97.67 87 | 98.88 77 | 92.80 83 | 99.91 22 | 97.11 62 | 99.12 88 | 99.50 72 |
|
PVSNet_BlendedMVS | | | 96.73 100 | 96.60 93 | 97.12 160 | 99.25 65 | 95.35 152 | 98.26 184 | 99.26 8 | 94.28 136 | 97.94 72 | 97.46 193 | 92.74 84 | 99.81 50 | 96.88 74 | 93.32 228 | 96.20 283 |
|
PVSNet_Blended | | | 97.38 76 | 97.12 70 | 98.14 95 | 99.25 65 | 95.35 152 | 97.28 272 | 99.26 8 | 93.13 191 | 97.94 72 | 98.21 138 | 92.74 84 | 99.81 50 | 96.88 74 | 99.40 79 | 99.27 99 |
|
MVS_Test | | | 97.28 80 | 97.00 76 | 98.13 97 | 98.33 136 | 95.97 116 | 98.74 115 | 98.07 213 | 94.27 137 | 98.44 50 | 98.07 146 | 92.48 86 | 99.26 140 | 96.43 92 | 98.19 126 | 99.16 110 |
|
MVSFormer | | | 97.57 64 | 97.49 56 | 97.84 112 | 98.07 152 | 95.76 136 | 99.47 2 | 98.40 152 | 94.98 113 | 98.79 31 | 98.83 81 | 92.34 87 | 98.41 248 | 96.91 69 | 99.59 53 | 99.34 89 |
|
lupinMVS | | | 97.44 71 | 97.22 68 | 98.12 98 | 98.07 152 | 95.76 136 | 97.68 245 | 97.76 227 | 94.50 131 | 98.79 31 | 98.61 100 | 92.34 87 | 99.30 137 | 97.58 47 | 99.59 53 | 99.31 92 |
|
CHOSEN 280x420 | | | 97.18 84 | 97.18 69 | 97.20 154 | 98.81 109 | 93.27 241 | 95.78 315 | 99.15 18 | 95.25 101 | 96.79 124 | 98.11 144 | 92.29 89 | 99.07 166 | 98.56 9 | 99.85 2 | 99.25 101 |
|
canonicalmvs | | | 97.67 59 | 97.23 67 | 98.98 49 | 98.70 116 | 98.38 18 | 99.34 11 | 98.39 154 | 96.76 45 | 97.67 87 | 97.40 197 | 92.26 90 | 99.49 125 | 98.28 22 | 96.28 178 | 99.08 119 |
|
IterMVS-LS | | | 95.46 154 | 95.21 142 | 96.22 230 | 98.12 150 | 93.72 233 | 98.32 177 | 98.13 196 | 93.71 163 | 94.26 202 | 97.31 205 | 92.24 91 | 98.10 270 | 94.63 139 | 90.12 256 | 96.84 226 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 95.96 124 | 95.83 117 | 96.36 222 | 97.93 161 | 93.70 234 | 98.12 201 | 98.27 168 | 93.70 165 | 95.07 166 | 99.02 58 | 92.23 92 | 98.54 217 | 94.68 138 | 93.46 223 | 96.84 226 |
|
WTY-MVS | | | 97.37 77 | 96.92 79 | 98.72 61 | 98.86 105 | 96.89 83 | 98.31 178 | 98.71 91 | 95.26 100 | 97.67 87 | 98.56 106 | 92.21 93 | 99.78 74 | 95.89 104 | 96.85 154 | 99.48 77 |
|
Effi-MVS+ | | | 97.12 87 | 96.69 89 | 98.39 84 | 98.19 145 | 96.72 88 | 97.37 264 | 98.43 149 | 93.71 163 | 97.65 90 | 98.02 149 | 92.20 94 | 99.25 141 | 96.87 77 | 97.79 139 | 99.19 105 |
|
1112_ss | | | 96.63 102 | 96.00 113 | 98.50 75 | 98.56 127 | 96.37 101 | 98.18 196 | 98.10 208 | 92.92 198 | 94.84 171 | 98.43 115 | 92.14 95 | 99.58 112 | 94.35 148 | 96.51 163 | 99.56 66 |
|
LS3D | | | 97.16 85 | 96.66 92 | 98.68 63 | 98.53 130 | 97.19 72 | 98.93 69 | 98.90 42 | 92.83 203 | 95.99 158 | 99.37 12 | 92.12 96 | 99.87 35 | 93.67 166 | 99.57 56 | 98.97 127 |
|
nrg030 | | | 96.28 118 | 95.72 120 | 97.96 108 | 96.90 224 | 98.15 36 | 99.39 5 | 98.31 162 | 95.47 84 | 94.42 191 | 98.35 123 | 92.09 97 | 98.69 204 | 97.50 53 | 89.05 270 | 97.04 205 |
|
mvs_anonymous | | | 96.70 101 | 96.53 97 | 97.18 156 | 98.19 145 | 93.78 229 | 98.31 178 | 98.19 182 | 94.01 144 | 94.47 182 | 98.27 134 | 92.08 98 | 98.46 233 | 97.39 56 | 97.91 133 | 99.31 92 |
|
FC-MVSNet-test | | | 96.42 111 | 96.05 110 | 97.53 137 | 96.95 219 | 97.27 67 | 99.36 8 | 99.23 12 | 95.83 71 | 93.93 218 | 98.37 121 | 92.00 99 | 98.32 257 | 96.02 101 | 92.72 236 | 97.00 207 |
|
FIs | | | 96.51 108 | 96.12 109 | 97.67 125 | 97.13 212 | 97.54 59 | 99.36 8 | 99.22 14 | 95.89 69 | 94.03 216 | 98.35 123 | 91.98 100 | 98.44 238 | 96.40 93 | 92.76 235 | 97.01 206 |
|
sss | | | 97.39 75 | 96.98 77 | 98.61 67 | 98.60 126 | 96.61 92 | 98.22 186 | 98.93 36 | 93.97 148 | 98.01 67 | 98.48 112 | 91.98 100 | 99.85 40 | 96.45 91 | 98.15 127 | 99.39 87 |
|
DP-MVS | | | 96.59 105 | 95.93 114 | 98.57 69 | 99.34 40 | 96.19 108 | 98.70 124 | 98.39 154 | 89.45 290 | 94.52 180 | 99.35 18 | 91.85 102 | 99.85 40 | 92.89 191 | 98.88 95 | 99.68 42 |
|
Test_1112_low_res | | | 96.34 114 | 95.66 127 | 98.36 85 | 98.56 127 | 95.94 120 | 97.71 242 | 98.07 213 | 92.10 229 | 94.79 175 | 97.29 206 | 91.75 103 | 99.56 115 | 94.17 153 | 96.50 164 | 99.58 64 |
|
UniMVSNet_NR-MVSNet | | | 95.71 136 | 95.15 144 | 97.40 148 | 96.84 227 | 96.97 77 | 98.74 115 | 99.24 10 | 95.16 105 | 93.88 220 | 97.72 177 | 91.68 104 | 98.31 259 | 95.81 107 | 87.25 297 | 96.92 212 |
|
UniMVSNet (Re) | | | 95.78 132 | 95.19 143 | 97.58 134 | 96.99 218 | 97.47 61 | 98.79 104 | 99.18 16 | 95.60 79 | 93.92 219 | 97.04 231 | 91.68 104 | 98.48 228 | 95.80 109 | 87.66 292 | 96.79 230 |
|
HY-MVS | | 93.96 8 | 96.82 98 | 96.23 107 | 98.57 69 | 98.46 131 | 97.00 76 | 98.14 198 | 98.21 178 | 93.95 149 | 96.72 125 | 97.99 153 | 91.58 106 | 99.76 81 | 94.51 145 | 96.54 162 | 98.95 131 |
|
xiu_mvs_v1_base_debu | | | 97.60 61 | 97.56 51 | 97.72 119 | 98.35 132 | 95.98 112 | 97.86 231 | 98.51 132 | 97.13 34 | 99.01 18 | 98.40 117 | 91.56 107 | 99.80 57 | 98.53 10 | 98.68 103 | 97.37 193 |
|
xiu_mvs_v1_base | | | 97.60 61 | 97.56 51 | 97.72 119 | 98.35 132 | 95.98 112 | 97.86 231 | 98.51 132 | 97.13 34 | 99.01 18 | 98.40 117 | 91.56 107 | 99.80 57 | 98.53 10 | 98.68 103 | 97.37 193 |
|
xiu_mvs_v1_base_debi | | | 97.60 61 | 97.56 51 | 97.72 119 | 98.35 132 | 95.98 112 | 97.86 231 | 98.51 132 | 97.13 34 | 99.01 18 | 98.40 117 | 91.56 107 | 99.80 57 | 98.53 10 | 98.68 103 | 97.37 193 |
|
MAR-MVS | | | 96.91 94 | 96.40 100 | 98.45 79 | 98.69 118 | 96.90 81 | 98.66 134 | 98.68 97 | 92.40 221 | 97.07 105 | 97.96 154 | 91.54 110 | 99.75 83 | 93.68 165 | 98.92 93 | 98.69 143 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
CANet | | | 98.05 43 | 97.76 45 | 98.90 55 | 98.73 113 | 97.27 67 | 98.35 171 | 98.78 71 | 97.37 19 | 97.72 84 | 98.96 69 | 91.53 111 | 99.92 13 | 98.79 3 | 99.65 44 | 99.51 70 |
|
EPNet | | | 97.28 80 | 96.87 81 | 98.51 74 | 94.98 307 | 96.14 109 | 98.90 71 | 97.02 283 | 98.28 1 | 95.99 158 | 99.11 46 | 91.36 112 | 99.89 27 | 96.98 64 | 99.19 86 | 99.50 72 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
1314 | | | 96.25 120 | 95.73 119 | 97.79 116 | 97.13 212 | 95.55 145 | 98.19 192 | 98.59 115 | 93.47 176 | 92.03 268 | 97.82 169 | 91.33 113 | 99.49 125 | 94.62 140 | 98.44 116 | 98.32 167 |
|
PAPM | | | 94.95 184 | 94.00 205 | 97.78 117 | 97.04 215 | 95.65 139 | 96.03 310 | 98.25 173 | 91.23 257 | 94.19 207 | 97.80 171 | 91.27 114 | 98.86 194 | 82.61 313 | 97.61 144 | 98.84 136 |
|
jason | | | 97.32 79 | 97.08 73 | 98.06 104 | 97.45 191 | 95.59 140 | 97.87 230 | 97.91 223 | 94.79 120 | 98.55 44 | 98.83 81 | 91.12 115 | 99.23 143 | 97.58 47 | 99.60 50 | 99.34 89 |
jason: jason. |
IS-MVSNet | | | 97.22 82 | 96.88 80 | 98.25 90 | 98.85 107 | 96.36 102 | 99.19 34 | 97.97 220 | 95.39 88 | 97.23 99 | 98.99 64 | 91.11 116 | 98.93 184 | 94.60 141 | 98.59 109 | 99.47 78 |
|
PMMVS | | | 96.60 103 | 96.33 102 | 97.41 146 | 97.90 163 | 93.93 225 | 97.35 267 | 98.41 150 | 92.84 202 | 97.76 80 | 97.45 195 | 91.10 117 | 99.20 149 | 96.26 95 | 97.91 133 | 99.11 115 |
|
MVS | | | 94.67 205 | 93.54 234 | 98.08 101 | 96.88 225 | 96.56 94 | 98.19 192 | 98.50 137 | 78.05 332 | 92.69 251 | 98.02 149 | 91.07 118 | 99.63 103 | 90.09 250 | 98.36 120 | 98.04 172 |
|
Fast-Effi-MVS+ | | | 96.28 118 | 95.70 124 | 98.03 105 | 98.29 138 | 95.97 116 | 98.58 142 | 98.25 173 | 91.74 237 | 95.29 165 | 97.23 209 | 91.03 119 | 99.15 152 | 92.90 189 | 97.96 132 | 98.97 127 |
|
Effi-MVS+-dtu | | | 96.29 116 | 96.56 94 | 95.51 252 | 97.89 164 | 90.22 283 | 98.80 99 | 98.10 208 | 96.57 52 | 96.45 148 | 96.66 260 | 90.81 120 | 98.91 186 | 95.72 111 | 97.99 131 | 97.40 190 |
|
mvs-test1 | | | 96.60 103 | 96.68 91 | 96.37 221 | 97.89 164 | 91.81 260 | 98.56 147 | 98.10 208 | 96.57 52 | 96.52 137 | 97.94 156 | 90.81 120 | 99.45 130 | 95.72 111 | 98.01 130 | 97.86 177 |
|
alignmvs | | | 97.56 65 | 97.07 74 | 99.01 46 | 98.66 120 | 98.37 21 | 98.83 88 | 98.06 215 | 96.74 46 | 98.00 69 | 97.65 182 | 90.80 122 | 99.48 129 | 98.37 19 | 96.56 161 | 99.19 105 |
|
AdaColmap | | | 97.15 86 | 96.70 88 | 98.48 77 | 99.16 77 | 96.69 89 | 98.01 212 | 98.89 44 | 94.44 135 | 96.83 119 | 98.68 94 | 90.69 123 | 99.76 81 | 94.36 147 | 99.29 84 | 98.98 126 |
|
cdsmvs_eth3d_5k | | | 23.98 326 | 31.98 326 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 98.59 115 | 0.00 350 | 0.00 352 | 98.61 100 | 90.60 124 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
DeepC-MVS | | 95.98 3 | 97.88 49 | 97.58 50 | 98.77 59 | 99.25 65 | 96.93 79 | 98.83 88 | 98.75 78 | 96.96 41 | 96.89 116 | 99.50 3 | 90.46 125 | 99.87 35 | 97.84 36 | 99.76 24 | 99.52 67 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
WR-MVS_H | | | 95.05 178 | 94.46 180 | 96.81 177 | 96.86 226 | 95.82 135 | 99.24 20 | 99.24 10 | 93.87 153 | 92.53 256 | 96.84 254 | 90.37 126 | 98.24 265 | 93.24 175 | 87.93 287 | 96.38 278 |
|
EPNet_dtu | | | 95.21 173 | 94.95 154 | 95.99 237 | 96.17 273 | 90.45 281 | 98.16 197 | 97.27 272 | 96.77 44 | 93.14 242 | 98.33 128 | 90.34 127 | 98.42 241 | 85.57 305 | 98.81 101 | 99.09 116 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
VNet | | | 97.79 54 | 97.40 61 | 98.96 51 | 98.88 103 | 97.55 58 | 98.63 136 | 98.93 36 | 96.74 46 | 99.02 17 | 98.84 80 | 90.33 128 | 99.83 43 | 98.53 10 | 96.66 157 | 99.50 72 |
|
MSDG | | | 95.93 126 | 95.30 139 | 97.83 113 | 98.90 96 | 95.36 150 | 96.83 294 | 98.37 157 | 91.32 252 | 94.43 190 | 98.73 91 | 90.27 129 | 99.60 106 | 90.05 253 | 98.82 100 | 98.52 152 |
|
LCM-MVSNet-Re | | | 95.22 172 | 95.32 137 | 94.91 275 | 98.18 147 | 87.85 312 | 98.75 111 | 95.66 322 | 95.11 107 | 88.96 293 | 96.85 253 | 90.26 130 | 97.65 289 | 95.65 116 | 98.44 116 | 99.22 104 |
|
diffmvs | | | 96.32 115 | 95.74 118 | 98.07 103 | 98.26 139 | 96.14 109 | 98.53 151 | 98.23 176 | 90.10 272 | 96.88 117 | 97.73 174 | 90.16 131 | 99.15 152 | 93.90 160 | 97.85 137 | 98.91 133 |
|
Vis-MVSNet (Re-imp) | | | 96.87 96 | 96.55 95 | 97.83 113 | 98.73 113 | 95.46 147 | 99.20 32 | 98.30 165 | 94.96 115 | 96.60 130 | 98.87 78 | 90.05 132 | 98.59 213 | 93.67 166 | 98.60 108 | 99.46 82 |
|
MDTV_nov1_ep13_2view | | | | | | | 84.26 320 | 96.89 290 | | 90.97 261 | 97.90 75 | | 89.89 133 | | 93.91 159 | | 99.18 109 |
|
tpmrst | | | 95.63 140 | 95.69 125 | 95.44 258 | 97.54 183 | 88.54 305 | 96.97 282 | 97.56 235 | 93.50 175 | 97.52 96 | 96.93 246 | 89.49 134 | 99.16 151 | 95.25 129 | 96.42 166 | 98.64 148 |
|
sam_mvs1 | | | | | | | | | | | | | 89.45 135 | | | | |
|
patchmatchnet-post | | | | | | | | | | | | 95.10 298 | 89.42 136 | 98.89 190 | | | |
|
3Dnovator+ | | 94.38 6 | 97.43 72 | 96.78 85 | 99.38 10 | 97.83 167 | 98.52 12 | 99.37 7 | 98.71 91 | 97.09 37 | 92.99 246 | 99.13 44 | 89.36 137 | 99.89 27 | 96.97 65 | 99.57 56 | 99.71 33 |
|
NR-MVSNet | | | 94.98 182 | 94.16 194 | 97.44 144 | 96.53 241 | 97.22 71 | 98.74 115 | 98.95 33 | 94.96 115 | 89.25 291 | 97.69 178 | 89.32 138 | 98.18 267 | 94.59 142 | 87.40 294 | 96.92 212 |
|
HyFIR lowres test | | | 96.90 95 | 96.49 98 | 98.14 95 | 99.33 43 | 95.56 143 | 97.38 262 | 99.65 2 | 92.34 223 | 97.61 91 | 98.20 139 | 89.29 139 | 99.10 163 | 96.97 65 | 97.60 145 | 99.77 14 |
|
3Dnovator | | 94.51 5 | 97.46 67 | 96.93 78 | 99.07 43 | 97.78 169 | 97.64 54 | 99.35 10 | 99.06 21 | 97.02 39 | 93.75 225 | 99.16 42 | 89.25 140 | 99.92 13 | 97.22 59 | 99.75 30 | 99.64 54 |
|
PatchmatchNet | | | 95.71 136 | 95.52 128 | 96.29 228 | 97.58 180 | 90.72 276 | 96.84 293 | 97.52 241 | 94.06 142 | 97.08 103 | 96.96 239 | 89.24 141 | 98.90 189 | 92.03 211 | 98.37 119 | 99.26 100 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MDTV_nov1_ep13 | | | | 95.40 129 | | 97.48 186 | 88.34 307 | 96.85 292 | 97.29 270 | 93.74 160 | 97.48 97 | 97.26 207 | 89.18 142 | 99.05 167 | 91.92 215 | 97.43 147 | |
|
test_djsdf | | | 96.00 123 | 95.69 125 | 96.93 172 | 95.72 293 | 95.49 146 | 99.47 2 | 98.40 152 | 94.98 113 | 94.58 178 | 97.86 162 | 89.16 143 | 98.41 248 | 96.91 69 | 94.12 210 | 96.88 222 |
|
QAPM | | | 96.29 116 | 95.40 129 | 98.96 51 | 97.85 166 | 97.60 57 | 99.23 22 | 98.93 36 | 89.76 282 | 93.11 243 | 99.02 58 | 89.11 144 | 99.93 9 | 91.99 212 | 99.62 48 | 99.34 89 |
|
pmmvs4 | | | 94.69 201 | 93.99 207 | 96.81 177 | 95.74 291 | 95.94 120 | 97.40 260 | 97.67 231 | 90.42 266 | 93.37 234 | 97.59 187 | 89.08 145 | 98.20 266 | 92.97 184 | 91.67 247 | 96.30 282 |
|
sam_mvs | | | | | | | | | | | | | 88.99 146 | | | | |
|
Patchmatch-test | | | 94.42 218 | 93.68 227 | 96.63 195 | 97.60 178 | 91.76 262 | 94.83 325 | 97.49 253 | 89.45 290 | 94.14 210 | 97.10 219 | 88.99 146 | 98.83 197 | 85.37 308 | 98.13 128 | 99.29 97 |
|
Patchmatch-RL test | | | 91.49 279 | 90.85 271 | 93.41 299 | 91.37 325 | 84.40 319 | 92.81 333 | 95.93 316 | 91.87 235 | 87.25 299 | 94.87 299 | 88.99 146 | 96.53 318 | 92.54 200 | 82.00 316 | 99.30 95 |
|
Fast-Effi-MVS+-dtu | | | 95.87 128 | 95.85 116 | 95.91 240 | 97.74 171 | 91.74 264 | 98.69 125 | 98.15 193 | 95.56 81 | 94.92 169 | 97.68 181 | 88.98 149 | 98.79 201 | 93.19 177 | 97.78 140 | 97.20 201 |
|
BH-untuned | | | 95.95 125 | 95.72 120 | 96.65 192 | 98.55 129 | 92.26 254 | 98.23 185 | 97.79 226 | 93.73 161 | 94.62 177 | 98.01 151 | 88.97 150 | 99.00 175 | 93.04 182 | 98.51 112 | 98.68 144 |
|
XVG-OURS | | | 96.55 107 | 96.41 99 | 96.99 166 | 98.75 112 | 93.76 230 | 97.50 256 | 98.52 130 | 95.67 76 | 96.83 119 | 99.30 24 | 88.95 151 | 99.53 122 | 95.88 105 | 96.26 179 | 97.69 184 |
|
v17 | | | 92.08 268 | 90.94 268 | 95.48 255 | 96.34 256 | 94.83 186 | 98.81 96 | 97.52 241 | 89.95 276 | 85.32 310 | 93.24 311 | 88.91 152 | 96.91 304 | 88.76 278 | 79.63 324 | 94.71 311 |
|
v18 | | | 92.10 267 | 90.97 267 | 95.50 253 | 96.34 256 | 94.85 175 | 98.82 90 | 97.52 241 | 89.99 274 | 85.31 312 | 93.26 310 | 88.90 153 | 96.92 303 | 88.82 277 | 79.77 323 | 94.73 309 |
|
v16 | | | 92.08 268 | 90.94 268 | 95.49 254 | 96.38 252 | 94.84 184 | 98.81 96 | 97.51 244 | 89.94 277 | 85.25 313 | 93.28 309 | 88.86 154 | 96.91 304 | 88.70 279 | 79.78 322 | 94.72 310 |
|
PVSNet | | 91.96 18 | 96.35 113 | 96.15 108 | 96.96 169 | 99.17 76 | 92.05 257 | 96.08 307 | 98.68 97 | 93.69 166 | 97.75 81 | 97.80 171 | 88.86 154 | 99.69 94 | 94.26 152 | 99.01 90 | 99.15 111 |
|
divwei89l23v2f112 | | | 94.76 195 | 94.12 199 | 96.67 190 | 96.28 266 | 94.85 175 | 98.69 125 | 98.12 198 | 92.44 217 | 94.29 200 | 96.94 242 | 88.85 156 | 98.48 228 | 92.67 194 | 88.79 280 | 96.67 248 |
|
v13 | | | 91.88 274 | 90.69 276 | 95.43 260 | 96.33 260 | 94.78 196 | 98.75 111 | 97.50 247 | 89.68 285 | 84.93 319 | 92.98 318 | 88.84 157 | 96.83 308 | 88.14 286 | 79.09 327 | 94.69 312 |
|
test_post | | | | | | | | | | | | 31.83 349 | 88.83 158 | 98.91 186 | | | |
|
v1neww | | | 94.83 189 | 94.22 189 | 96.68 187 | 96.39 249 | 94.85 175 | 98.87 78 | 98.11 203 | 92.45 215 | 94.45 183 | 97.06 226 | 88.82 159 | 98.54 217 | 92.93 186 | 88.91 274 | 96.65 253 |
|
v7new | | | 94.83 189 | 94.22 189 | 96.68 187 | 96.39 249 | 94.85 175 | 98.87 78 | 98.11 203 | 92.45 215 | 94.45 183 | 97.06 226 | 88.82 159 | 98.54 217 | 92.93 186 | 88.91 274 | 96.65 253 |
|
v12 | | | 91.89 273 | 90.70 275 | 95.43 260 | 96.31 264 | 94.80 191 | 98.76 110 | 97.50 247 | 89.76 282 | 84.95 318 | 93.00 317 | 88.82 159 | 96.82 310 | 88.23 285 | 79.00 330 | 94.68 314 |
|
v8 | | | 94.47 216 | 93.77 220 | 96.57 204 | 96.36 253 | 94.83 186 | 99.05 55 | 98.19 182 | 91.92 232 | 93.16 239 | 96.97 238 | 88.82 159 | 98.48 228 | 91.69 221 | 87.79 290 | 96.39 277 |
|
V14 | | | 91.93 271 | 90.76 273 | 95.42 263 | 96.33 260 | 94.81 190 | 98.77 107 | 97.51 244 | 89.86 280 | 85.09 315 | 93.13 312 | 88.80 163 | 96.83 308 | 88.32 283 | 79.06 328 | 94.60 316 |
|
v1141 | | | 94.75 197 | 94.11 200 | 96.67 190 | 96.27 268 | 94.86 174 | 98.69 125 | 98.12 198 | 92.43 218 | 94.31 197 | 96.94 242 | 88.78 164 | 98.48 228 | 92.63 196 | 88.85 278 | 96.67 248 |
|
v15 | | | 91.94 270 | 90.77 272 | 95.43 260 | 96.31 264 | 94.83 186 | 98.77 107 | 97.50 247 | 89.92 278 | 85.13 314 | 93.08 314 | 88.76 165 | 96.86 306 | 88.40 282 | 79.10 326 | 94.61 315 |
|
v6 | | | 94.83 189 | 94.21 191 | 96.69 184 | 96.36 253 | 94.85 175 | 98.87 78 | 98.11 203 | 92.46 210 | 94.44 189 | 97.05 230 | 88.76 165 | 98.57 215 | 92.95 185 | 88.92 273 | 96.65 253 |
|
V9 | | | 91.91 272 | 90.73 274 | 95.45 257 | 96.32 263 | 94.80 191 | 98.77 107 | 97.50 247 | 89.81 281 | 85.03 317 | 93.08 314 | 88.76 165 | 96.86 306 | 88.24 284 | 79.03 329 | 94.69 312 |
|
v1 | | | 94.75 197 | 94.11 200 | 96.69 184 | 96.27 268 | 94.87 173 | 98.69 125 | 98.12 198 | 92.43 218 | 94.32 196 | 96.94 242 | 88.71 168 | 98.54 217 | 92.66 195 | 88.84 279 | 96.67 248 |
|
BH-w/o | | | 95.38 161 | 95.08 147 | 96.26 229 | 98.34 135 | 91.79 261 | 97.70 243 | 97.43 258 | 92.87 201 | 94.24 204 | 97.22 210 | 88.66 169 | 98.84 195 | 91.55 223 | 97.70 143 | 98.16 170 |
|
tpmvs | | | 94.60 208 | 94.36 185 | 95.33 267 | 97.46 188 | 88.60 303 | 96.88 291 | 97.68 230 | 91.29 254 | 93.80 224 | 96.42 271 | 88.58 170 | 99.24 142 | 91.06 232 | 96.04 191 | 98.17 169 |
|
DU-MVS | | | 95.42 157 | 94.76 168 | 97.40 148 | 96.53 241 | 96.97 77 | 98.66 134 | 98.99 28 | 95.43 86 | 93.88 220 | 97.69 178 | 88.57 171 | 98.31 259 | 95.81 107 | 87.25 297 | 96.92 212 |
|
Baseline_NR-MVSNet | | | 94.35 221 | 93.81 216 | 95.96 238 | 96.20 271 | 94.05 223 | 98.61 139 | 96.67 304 | 91.44 244 | 93.85 222 | 97.60 186 | 88.57 171 | 98.14 268 | 94.39 146 | 86.93 300 | 95.68 296 |
|
PCF-MVS | | 93.45 11 | 94.68 204 | 93.43 240 | 98.42 83 | 98.62 124 | 96.77 86 | 95.48 317 | 98.20 181 | 84.63 319 | 93.34 235 | 98.32 129 | 88.55 173 | 99.81 50 | 84.80 309 | 98.96 92 | 98.68 144 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
v148 | | | 94.29 224 | 93.76 222 | 95.91 240 | 96.10 277 | 92.93 248 | 98.58 142 | 97.97 220 | 92.59 208 | 93.47 233 | 96.95 240 | 88.53 174 | 98.32 257 | 92.56 198 | 87.06 299 | 96.49 274 |
|
v11 | | | 91.85 275 | 90.68 277 | 95.36 265 | 96.34 256 | 94.74 198 | 98.80 99 | 97.43 258 | 89.60 288 | 85.09 315 | 93.03 316 | 88.53 174 | 96.75 311 | 87.37 294 | 79.96 321 | 94.58 317 |
|
PatchMatch-RL | | | 96.59 105 | 96.03 112 | 98.27 88 | 99.31 48 | 96.51 96 | 97.91 223 | 99.06 21 | 93.72 162 | 96.92 114 | 98.06 147 | 88.50 176 | 99.65 98 | 91.77 219 | 99.00 91 | 98.66 146 |
|
V42 | | | 94.78 194 | 94.14 196 | 96.70 183 | 96.33 260 | 95.22 156 | 98.97 65 | 98.09 211 | 92.32 225 | 94.31 197 | 97.06 226 | 88.39 177 | 98.55 216 | 92.90 189 | 88.87 276 | 96.34 280 |
|
v7n | | | 94.19 229 | 93.43 240 | 96.47 214 | 95.90 285 | 94.38 214 | 99.26 17 | 98.34 160 | 91.99 231 | 92.76 250 | 97.13 218 | 88.31 178 | 98.52 224 | 89.48 266 | 87.70 291 | 96.52 270 |
|
TranMVSNet+NR-MVSNet | | | 95.14 176 | 94.48 178 | 97.11 161 | 96.45 246 | 96.36 102 | 99.03 58 | 99.03 24 | 95.04 111 | 93.58 227 | 97.93 157 | 88.27 179 | 98.03 275 | 94.13 154 | 86.90 302 | 96.95 211 |
|
MVSTER | | | 96.06 122 | 95.72 120 | 97.08 163 | 98.23 141 | 95.93 123 | 98.73 118 | 98.27 168 | 94.86 119 | 95.07 166 | 98.09 145 | 88.21 180 | 98.54 217 | 96.59 85 | 93.46 223 | 96.79 230 |
|
CHOSEN 1792x2688 | | | 97.12 87 | 96.80 82 | 98.08 101 | 99.30 53 | 94.56 208 | 98.05 208 | 99.71 1 | 93.57 173 | 97.09 102 | 98.91 76 | 88.17 181 | 99.89 27 | 96.87 77 | 99.56 62 | 99.81 2 |
|
CR-MVSNet | | | 94.76 195 | 94.15 195 | 96.59 200 | 97.00 216 | 93.43 238 | 94.96 321 | 97.56 235 | 92.46 210 | 96.93 112 | 96.24 274 | 88.15 182 | 97.88 286 | 87.38 293 | 96.65 158 | 98.46 155 |
|
Patchmtry | | | 93.22 255 | 92.35 256 | 95.84 243 | 96.77 229 | 93.09 247 | 94.66 327 | 97.56 235 | 87.37 304 | 92.90 247 | 96.24 274 | 88.15 182 | 97.90 282 | 87.37 294 | 90.10 257 | 96.53 269 |
|
v7 | | | 94.69 201 | 94.04 202 | 96.62 197 | 96.41 248 | 94.79 194 | 98.78 106 | 98.13 196 | 91.89 233 | 94.30 199 | 97.16 212 | 88.13 184 | 98.45 235 | 91.96 214 | 89.65 261 | 96.61 258 |
|
v10 | | | 94.29 224 | 93.55 233 | 96.51 211 | 96.39 249 | 94.80 191 | 98.99 61 | 98.19 182 | 91.35 250 | 93.02 245 | 96.99 236 | 88.09 185 | 98.41 248 | 90.50 246 | 88.41 283 | 96.33 281 |
|
Vis-MVSNet | | | 97.42 73 | 97.11 71 | 98.34 86 | 98.66 120 | 96.23 107 | 99.22 28 | 99.00 26 | 96.63 51 | 98.04 63 | 99.21 32 | 88.05 186 | 99.35 136 | 96.01 102 | 99.21 85 | 99.45 84 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v1144 | | | 94.59 210 | 93.92 210 | 96.60 199 | 96.21 270 | 94.78 196 | 98.59 140 | 98.14 195 | 91.86 236 | 94.21 206 | 97.02 233 | 87.97 187 | 98.41 248 | 91.72 220 | 89.57 262 | 96.61 258 |
|
PatchT | | | 93.06 258 | 91.97 260 | 96.35 223 | 96.69 235 | 92.67 250 | 94.48 328 | 97.08 278 | 86.62 306 | 97.08 103 | 92.23 328 | 87.94 188 | 97.90 282 | 78.89 322 | 96.69 156 | 98.49 154 |
|
V4 | | | 94.18 231 | 93.52 235 | 96.13 234 | 95.89 286 | 94.31 216 | 99.23 22 | 98.22 177 | 91.42 245 | 92.82 249 | 96.89 249 | 87.93 189 | 98.52 224 | 91.51 225 | 87.81 288 | 95.58 298 |
|
ADS-MVSNet2 | | | 94.58 211 | 94.40 184 | 95.11 272 | 98.00 156 | 88.74 300 | 96.04 308 | 97.30 269 | 90.15 269 | 96.47 146 | 96.64 262 | 87.89 190 | 97.56 293 | 90.08 251 | 97.06 150 | 99.02 122 |
|
ADS-MVSNet | | | 95.00 179 | 94.45 182 | 96.63 195 | 98.00 156 | 91.91 259 | 96.04 308 | 97.74 229 | 90.15 269 | 96.47 146 | 96.64 262 | 87.89 190 | 98.96 179 | 90.08 251 | 97.06 150 | 99.02 122 |
|
XVG-OURS-SEG-HR | | | 96.51 108 | 96.34 101 | 97.02 165 | 98.77 111 | 93.76 230 | 97.79 238 | 98.50 137 | 95.45 85 | 96.94 111 | 99.09 52 | 87.87 192 | 99.55 121 | 96.76 80 | 95.83 194 | 97.74 180 |
|
test_post1 | | | | | | | | 96.68 297 | | | | 30.43 350 | 87.85 193 | 98.69 204 | 92.59 197 | | |
|
v52 | | | 94.18 231 | 93.52 235 | 96.13 234 | 95.95 284 | 94.29 217 | 99.23 22 | 98.21 178 | 91.42 245 | 92.84 248 | 96.89 249 | 87.85 193 | 98.53 223 | 91.51 225 | 87.81 288 | 95.57 299 |
|
pcd1.5k->3k | | | 39.42 324 | 41.78 325 | 32.35 337 | 96.17 273 | 0.00 356 | 0.00 346 | 98.54 125 | 0.00 350 | 0.00 352 | 0.00 352 | 87.78 195 | 0.00 353 | 0.00 350 | 93.56 222 | 97.06 203 |
|
test-LLR | | | 95.10 177 | 94.87 161 | 95.80 245 | 96.77 229 | 89.70 287 | 96.91 286 | 95.21 326 | 95.11 107 | 94.83 173 | 95.72 291 | 87.71 196 | 98.97 176 | 93.06 180 | 98.50 113 | 98.72 140 |
|
test0.0.03 1 | | | 94.08 237 | 93.51 237 | 95.80 245 | 95.53 299 | 92.89 249 | 97.38 262 | 95.97 314 | 95.11 107 | 92.51 258 | 96.66 260 | 87.71 196 | 96.94 302 | 87.03 296 | 93.67 218 | 97.57 186 |
|
JIA-IIPM | | | 93.35 250 | 92.49 254 | 95.92 239 | 96.48 245 | 90.65 278 | 95.01 320 | 96.96 289 | 85.93 312 | 96.08 154 | 87.33 333 | 87.70 198 | 98.78 202 | 91.35 228 | 95.58 196 | 98.34 165 |
|
v2v482 | | | 94.69 201 | 94.03 203 | 96.65 192 | 96.17 273 | 94.79 194 | 98.67 132 | 98.08 212 | 92.72 204 | 94.00 217 | 97.16 212 | 87.69 199 | 98.45 235 | 92.91 188 | 88.87 276 | 96.72 238 |
|
PatchFormer-LS_test | | | 95.47 153 | 95.27 140 | 96.08 236 | 97.59 179 | 90.66 277 | 98.10 205 | 97.34 265 | 93.98 147 | 96.08 154 | 96.15 280 | 87.65 200 | 99.12 156 | 95.27 128 | 95.24 198 | 98.44 157 |
|
v748 | | | 93.75 245 | 93.06 245 | 95.82 244 | 95.73 292 | 92.64 251 | 99.25 19 | 98.24 175 | 91.60 240 | 92.22 265 | 96.52 267 | 87.60 201 | 98.46 233 | 90.64 239 | 85.72 309 | 96.36 279 |
|
CVMVSNet | | | 95.43 156 | 96.04 111 | 93.57 298 | 97.93 161 | 83.62 321 | 98.12 201 | 98.59 115 | 95.68 75 | 96.56 131 | 99.02 58 | 87.51 202 | 97.51 294 | 93.56 169 | 97.44 146 | 99.60 60 |
|
WR-MVS | | | 95.15 175 | 94.46 180 | 97.22 153 | 96.67 237 | 96.45 98 | 98.21 187 | 98.81 61 | 94.15 138 | 93.16 239 | 97.69 178 | 87.51 202 | 98.30 261 | 95.29 127 | 88.62 281 | 96.90 219 |
|
anonymousdsp | | | 95.42 157 | 94.91 159 | 96.94 171 | 95.10 306 | 95.90 131 | 99.14 42 | 98.41 150 | 93.75 158 | 93.16 239 | 97.46 193 | 87.50 204 | 98.41 248 | 95.63 117 | 94.03 212 | 96.50 273 |
|
v144192 | | | 94.39 220 | 93.70 225 | 96.48 213 | 96.06 279 | 94.35 215 | 98.58 142 | 98.16 192 | 91.45 243 | 94.33 195 | 97.02 233 | 87.50 204 | 98.45 235 | 91.08 231 | 89.11 269 | 96.63 256 |
|
EU-MVSNet | | | 93.66 246 | 94.14 196 | 92.25 307 | 95.96 283 | 83.38 322 | 98.52 152 | 98.12 198 | 94.69 121 | 92.61 253 | 98.13 143 | 87.36 206 | 96.39 320 | 91.82 216 | 90.00 258 | 96.98 208 |
|
CP-MVSNet | | | 94.94 186 | 94.30 187 | 96.83 176 | 96.72 234 | 95.56 143 | 99.11 48 | 98.95 33 | 93.89 151 | 92.42 261 | 97.90 159 | 87.19 207 | 98.12 269 | 94.32 149 | 88.21 284 | 96.82 229 |
|
HQP_MVS | | | 96.14 121 | 95.90 115 | 96.85 175 | 97.42 192 | 94.60 206 | 98.80 99 | 98.56 122 | 97.28 21 | 95.34 162 | 98.28 131 | 87.09 208 | 99.03 172 | 96.07 97 | 94.27 202 | 96.92 212 |
|
plane_prior6 | | | | | | 97.35 197 | 94.61 204 | | | | | | 87.09 208 | | | | |
|
RPSCF | | | 94.87 188 | 95.40 129 | 93.26 302 | 98.89 102 | 82.06 327 | 98.33 173 | 98.06 215 | 90.30 268 | 96.56 131 | 99.26 27 | 87.09 208 | 99.49 125 | 93.82 162 | 96.32 173 | 98.24 168 |
|
RPMNet | | | 92.52 262 | 91.17 265 | 96.59 200 | 97.00 216 | 93.43 238 | 94.96 321 | 97.26 273 | 82.27 325 | 96.93 112 | 92.12 329 | 86.98 211 | 97.88 286 | 76.32 327 | 96.65 158 | 98.46 155 |
|
v1192 | | | 94.32 222 | 93.58 232 | 96.53 209 | 96.10 277 | 94.45 210 | 98.50 157 | 98.17 190 | 91.54 241 | 94.19 207 | 97.06 226 | 86.95 212 | 98.43 240 | 90.14 249 | 89.57 262 | 96.70 242 |
|
CANet_DTU | | | 96.96 92 | 96.55 95 | 98.21 91 | 98.17 149 | 96.07 111 | 97.98 215 | 98.21 178 | 97.24 27 | 97.13 101 | 98.93 73 | 86.88 213 | 99.91 22 | 95.00 133 | 99.37 81 | 98.66 146 |
|
HQP2-MVS | | | | | | | | | | | | | 86.75 214 | | | | |
|
HQP-MVS | | | 95.72 134 | 95.40 129 | 96.69 184 | 97.20 206 | 94.25 219 | 98.05 208 | 98.46 142 | 96.43 54 | 94.45 183 | 97.73 174 | 86.75 214 | 98.96 179 | 95.30 125 | 94.18 206 | 96.86 225 |
|
OpenMVS | | 93.04 13 | 95.83 130 | 95.00 149 | 98.32 87 | 97.18 209 | 97.32 65 | 99.21 31 | 98.97 29 | 89.96 275 | 91.14 274 | 99.05 57 | 86.64 216 | 99.92 13 | 93.38 171 | 99.47 70 | 97.73 181 |
|
YYNet1 | | | 90.70 288 | 89.39 289 | 94.62 285 | 94.79 311 | 90.65 278 | 97.20 275 | 97.46 254 | 87.54 303 | 72.54 335 | 95.74 288 | 86.51 217 | 96.66 316 | 86.00 302 | 86.76 304 | 96.54 268 |
|
MDA-MVSNet_test_wron | | | 90.71 287 | 89.38 290 | 94.68 283 | 94.83 310 | 90.78 275 | 97.19 276 | 97.46 254 | 87.60 302 | 72.41 336 | 95.72 291 | 86.51 217 | 96.71 315 | 85.92 303 | 86.80 303 | 96.56 266 |
|
v1921920 | | | 94.20 228 | 93.47 239 | 96.40 220 | 95.98 282 | 94.08 222 | 98.52 152 | 98.15 193 | 91.33 251 | 94.25 203 | 97.20 211 | 86.41 219 | 98.42 241 | 90.04 254 | 89.39 267 | 96.69 247 |
|
COLMAP_ROB | | 93.27 12 | 95.33 167 | 94.87 161 | 96.71 181 | 99.29 56 | 93.24 243 | 98.58 142 | 98.11 203 | 89.92 278 | 93.57 228 | 99.10 48 | 86.37 220 | 99.79 69 | 90.78 236 | 98.10 129 | 97.09 202 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
MVP-Stereo | | | 94.28 226 | 93.92 210 | 95.35 266 | 94.95 308 | 92.60 252 | 97.97 216 | 97.65 232 | 91.61 239 | 90.68 280 | 97.09 221 | 86.32 221 | 98.42 241 | 89.70 261 | 99.34 82 | 95.02 306 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
CLD-MVS | | | 95.62 141 | 95.34 134 | 96.46 217 | 97.52 185 | 93.75 232 | 97.27 273 | 98.46 142 | 95.53 82 | 94.42 191 | 98.00 152 | 86.21 222 | 98.97 176 | 96.25 96 | 94.37 200 | 96.66 251 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
tpm cat1 | | | 93.36 249 | 92.80 249 | 95.07 273 | 97.58 180 | 87.97 310 | 96.76 295 | 97.86 224 | 82.17 326 | 93.53 229 | 96.04 283 | 86.13 223 | 99.13 155 | 89.24 269 | 95.87 193 | 98.10 171 |
|
thresconf0.02 | | | 95.50 148 | 94.84 163 | 97.51 138 | 98.90 96 | 95.93 123 | 99.17 35 | 95.70 318 | 93.42 178 | 96.50 142 | 97.16 212 | 86.12 224 | 99.22 145 | 90.51 242 | 96.06 187 | 97.37 193 |
|
tfpn_n400 | | | 95.50 148 | 94.84 163 | 97.51 138 | 98.90 96 | 95.93 123 | 99.17 35 | 95.70 318 | 93.42 178 | 96.50 142 | 97.16 212 | 86.12 224 | 99.22 145 | 90.51 242 | 96.06 187 | 97.37 193 |
|
tfpnconf | | | 95.50 148 | 94.84 163 | 97.51 138 | 98.90 96 | 95.93 123 | 99.17 35 | 95.70 318 | 93.42 178 | 96.50 142 | 97.16 212 | 86.12 224 | 99.22 145 | 90.51 242 | 96.06 187 | 97.37 193 |
|
tfpnview11 | | | 95.50 148 | 94.84 163 | 97.51 138 | 98.90 96 | 95.93 123 | 99.17 35 | 95.70 318 | 93.42 178 | 96.50 142 | 97.16 212 | 86.12 224 | 99.22 145 | 90.51 242 | 96.06 187 | 97.37 193 |
|
MVS_0304 | | | 97.70 57 | 97.25 65 | 99.07 43 | 98.90 96 | 97.83 49 | 98.20 188 | 98.74 79 | 97.51 8 | 98.03 64 | 99.06 56 | 86.12 224 | 99.93 9 | 99.02 1 | 99.64 46 | 99.44 85 |
|
PEN-MVS | | | 94.42 218 | 93.73 224 | 96.49 212 | 96.28 266 | 94.84 184 | 99.17 35 | 99.00 26 | 93.51 174 | 92.23 264 | 97.83 168 | 86.10 229 | 97.90 282 | 92.55 199 | 86.92 301 | 96.74 235 |
|
v1240 | | | 94.06 239 | 93.29 243 | 96.34 225 | 96.03 281 | 93.90 226 | 98.44 162 | 98.17 190 | 91.18 259 | 94.13 211 | 97.01 235 | 86.05 230 | 98.42 241 | 89.13 271 | 89.50 265 | 96.70 242 |
|
CostFormer | | | 94.95 184 | 94.73 169 | 95.60 251 | 97.28 200 | 89.06 296 | 97.53 254 | 96.89 296 | 89.66 286 | 96.82 121 | 96.72 258 | 86.05 230 | 98.95 183 | 95.53 119 | 96.13 185 | 98.79 138 |
|
ACMM | | 93.85 9 | 95.69 138 | 95.38 133 | 96.61 198 | 97.61 177 | 93.84 228 | 98.91 70 | 98.44 146 | 95.25 101 | 94.28 201 | 98.47 113 | 86.04 232 | 99.12 156 | 95.50 120 | 93.95 215 | 96.87 223 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
DTE-MVSNet | | | 93.98 241 | 93.26 244 | 96.14 233 | 96.06 279 | 94.39 213 | 99.20 32 | 98.86 52 | 93.06 192 | 91.78 269 | 97.81 170 | 85.87 233 | 97.58 292 | 90.53 241 | 86.17 306 | 96.46 276 |
|
tfpn1000 | | | 95.72 134 | 95.11 145 | 97.58 134 | 99.00 87 | 95.73 138 | 99.24 20 | 95.49 324 | 94.08 141 | 96.87 118 | 97.45 195 | 85.81 234 | 99.30 137 | 91.78 218 | 96.22 183 | 97.71 183 |
|
VPA-MVSNet | | | 95.75 133 | 95.11 145 | 97.69 123 | 97.24 202 | 97.27 67 | 98.94 68 | 99.23 12 | 95.13 106 | 95.51 161 | 97.32 204 | 85.73 235 | 98.91 186 | 97.33 58 | 89.55 264 | 96.89 220 |
|
EPMVS | | | 94.99 180 | 94.48 178 | 96.52 210 | 97.22 204 | 91.75 263 | 97.23 274 | 91.66 343 | 94.11 139 | 97.28 98 | 96.81 255 | 85.70 236 | 98.84 195 | 93.04 182 | 97.28 148 | 98.97 127 |
|
TransMVSNet (Re) | | | 92.67 260 | 91.51 264 | 96.15 232 | 96.58 239 | 94.65 199 | 98.90 71 | 96.73 300 | 90.86 262 | 89.46 289 | 97.86 162 | 85.62 237 | 98.09 272 | 86.45 299 | 81.12 319 | 95.71 295 |
|
tfpn_ndepth | | | 95.53 147 | 94.90 160 | 97.39 151 | 98.96 93 | 95.88 133 | 99.05 55 | 95.27 325 | 93.80 157 | 96.95 109 | 96.93 246 | 85.53 238 | 99.40 131 | 91.54 224 | 96.10 186 | 96.89 220 |
|
dp | | | 94.15 234 | 93.90 212 | 94.90 276 | 97.31 199 | 86.82 317 | 96.97 282 | 97.19 276 | 91.22 258 | 96.02 157 | 96.61 264 | 85.51 239 | 99.02 174 | 90.00 255 | 94.30 201 | 98.85 134 |
|
LPG-MVS_test | | | 95.62 141 | 95.34 134 | 96.47 214 | 97.46 188 | 93.54 235 | 98.99 61 | 98.54 125 | 94.67 123 | 94.36 193 | 98.77 87 | 85.39 240 | 99.11 160 | 95.71 113 | 94.15 208 | 96.76 233 |
|
LGP-MVS_train | | | | | 96.47 214 | 97.46 188 | 93.54 235 | | 98.54 125 | 94.67 123 | 94.36 193 | 98.77 87 | 85.39 240 | 99.11 160 | 95.71 113 | 94.15 208 | 96.76 233 |
|
PS-CasMVS | | | 94.67 205 | 93.99 207 | 96.71 181 | 96.68 236 | 95.26 155 | 99.13 45 | 99.03 24 | 93.68 168 | 92.33 262 | 97.95 155 | 85.35 242 | 98.10 270 | 93.59 168 | 88.16 286 | 96.79 230 |
|
ab-mvs | | | 96.42 111 | 95.71 123 | 98.55 71 | 98.63 123 | 96.75 87 | 97.88 229 | 98.74 79 | 93.84 154 | 96.54 135 | 98.18 140 | 85.34 243 | 99.75 83 | 95.93 103 | 96.35 171 | 99.15 111 |
|
N_pmnet | | | 87.12 303 | 87.77 300 | 85.17 323 | 95.46 301 | 61.92 346 | 97.37 264 | 70.66 354 | 85.83 313 | 88.73 295 | 96.04 283 | 85.33 244 | 97.76 288 | 80.02 317 | 90.48 255 | 95.84 291 |
|
OPM-MVS | | | 95.69 138 | 95.33 136 | 96.76 179 | 96.16 276 | 94.63 201 | 98.43 164 | 98.39 154 | 96.64 50 | 95.02 168 | 98.78 85 | 85.15 245 | 99.05 167 | 95.21 131 | 94.20 205 | 96.60 260 |
|
BH-RMVSNet | | | 95.92 127 | 95.32 137 | 97.69 123 | 98.32 137 | 94.64 200 | 98.19 192 | 97.45 256 | 94.56 128 | 96.03 156 | 98.61 100 | 85.02 246 | 99.12 156 | 90.68 238 | 99.06 89 | 99.30 95 |
|
DSMNet-mixed | | | 92.52 262 | 92.58 253 | 92.33 306 | 94.15 315 | 82.65 325 | 98.30 180 | 94.26 336 | 89.08 295 | 92.65 252 | 95.73 289 | 85.01 247 | 95.76 323 | 86.24 300 | 97.76 141 | 98.59 150 |
|
tfpnnormal | | | 93.66 246 | 92.70 252 | 96.55 208 | 96.94 220 | 95.94 120 | 98.97 65 | 99.19 15 | 91.04 260 | 91.38 272 | 97.34 202 | 84.94 248 | 98.61 210 | 85.45 307 | 89.02 272 | 95.11 303 |
|
LTVRE_ROB | | 92.95 15 | 94.60 208 | 93.90 212 | 96.68 187 | 97.41 195 | 94.42 211 | 98.52 152 | 98.59 115 | 91.69 238 | 91.21 273 | 98.35 123 | 84.87 249 | 99.04 171 | 91.06 232 | 93.44 226 | 96.60 260 |
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 |
XXY-MVS | | | 95.20 174 | 94.45 182 | 97.46 143 | 96.75 232 | 96.56 94 | 98.86 83 | 98.65 111 | 93.30 187 | 93.27 236 | 98.27 134 | 84.85 250 | 98.87 192 | 94.82 136 | 91.26 252 | 96.96 209 |
|
AllTest | | | 95.24 171 | 94.65 172 | 96.99 166 | 99.25 65 | 93.21 244 | 98.59 140 | 98.18 185 | 91.36 248 | 93.52 230 | 98.77 87 | 84.67 251 | 99.72 86 | 89.70 261 | 97.87 135 | 98.02 173 |
|
TestCases | | | | | 96.99 166 | 99.25 65 | 93.21 244 | | 98.18 185 | 91.36 248 | 93.52 230 | 98.77 87 | 84.67 251 | 99.72 86 | 89.70 261 | 97.87 135 | 98.02 173 |
|
thres200 | | | 95.25 170 | 94.57 175 | 97.28 152 | 98.81 109 | 94.92 170 | 98.20 188 | 97.11 277 | 95.24 103 | 96.54 135 | 96.22 278 | 84.58 253 | 99.53 122 | 87.93 291 | 96.50 164 | 97.39 191 |
|
view600 | | | 95.60 143 | 94.93 155 | 97.62 128 | 99.05 82 | 94.85 175 | 99.09 50 | 97.01 285 | 95.36 92 | 96.52 137 | 97.37 198 | 84.55 254 | 99.59 107 | 89.07 272 | 96.39 167 | 98.40 158 |
|
view800 | | | 95.60 143 | 94.93 155 | 97.62 128 | 99.05 82 | 94.85 175 | 99.09 50 | 97.01 285 | 95.36 92 | 96.52 137 | 97.37 198 | 84.55 254 | 99.59 107 | 89.07 272 | 96.39 167 | 98.40 158 |
|
conf0.05thres1000 | | | 95.60 143 | 94.93 155 | 97.62 128 | 99.05 82 | 94.85 175 | 99.09 50 | 97.01 285 | 95.36 92 | 96.52 137 | 97.37 198 | 84.55 254 | 99.59 107 | 89.07 272 | 96.39 167 | 98.40 158 |
|
tfpn | | | 95.60 143 | 94.93 155 | 97.62 128 | 99.05 82 | 94.85 175 | 99.09 50 | 97.01 285 | 95.36 92 | 96.52 137 | 97.37 198 | 84.55 254 | 99.59 107 | 89.07 272 | 96.39 167 | 98.40 158 |
|
pm-mvs1 | | | 93.94 242 | 93.06 245 | 96.59 200 | 96.49 244 | 95.16 157 | 98.95 67 | 98.03 219 | 92.32 225 | 91.08 275 | 97.84 165 | 84.54 258 | 98.41 248 | 92.16 205 | 86.13 308 | 96.19 284 |
|
ACMP | | 93.49 10 | 95.34 166 | 94.98 151 | 96.43 218 | 97.67 173 | 93.48 237 | 98.73 118 | 98.44 146 | 94.94 118 | 92.53 256 | 98.53 107 | 84.50 259 | 99.14 154 | 95.48 121 | 94.00 213 | 96.66 251 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LP | | | 91.12 283 | 89.99 285 | 94.53 286 | 96.35 255 | 88.70 301 | 93.86 332 | 97.35 264 | 84.88 317 | 90.98 276 | 94.77 300 | 84.40 260 | 97.43 295 | 75.41 330 | 91.89 245 | 97.47 187 |
|
conf200view11 | | | 95.40 160 | 94.70 170 | 97.50 142 | 98.98 90 | 94.92 170 | 98.87 78 | 96.90 293 | 95.38 89 | 96.61 128 | 96.88 251 | 84.29 261 | 99.56 115 | 88.11 287 | 96.29 174 | 98.02 173 |
|
thres100view900 | | | 95.38 161 | 94.70 170 | 97.41 146 | 98.98 90 | 94.92 170 | 98.87 78 | 96.90 293 | 95.38 89 | 96.61 128 | 96.88 251 | 84.29 261 | 99.56 115 | 88.11 287 | 96.29 174 | 97.76 178 |
|
thres600view7 | | | 95.49 152 | 94.77 167 | 97.67 125 | 98.98 90 | 95.02 162 | 98.85 84 | 96.90 293 | 95.38 89 | 96.63 127 | 96.90 248 | 84.29 261 | 99.59 107 | 88.65 281 | 96.33 172 | 98.40 158 |
|
FMVSNet3 | | | 94.97 183 | 94.26 188 | 97.11 161 | 98.18 147 | 96.62 90 | 98.56 147 | 98.26 172 | 93.67 170 | 94.09 212 | 97.10 219 | 84.25 264 | 98.01 276 | 92.08 207 | 92.14 239 | 96.70 242 |
|
tfpn200view9 | | | 95.32 168 | 94.62 173 | 97.43 145 | 98.94 94 | 94.98 166 | 98.68 129 | 96.93 291 | 95.33 96 | 96.55 133 | 96.53 265 | 84.23 265 | 99.56 115 | 88.11 287 | 96.29 174 | 97.76 178 |
|
thres400 | | | 95.38 161 | 94.62 173 | 97.65 127 | 98.94 94 | 94.98 166 | 98.68 129 | 96.93 291 | 95.33 96 | 96.55 133 | 96.53 265 | 84.23 265 | 99.56 115 | 88.11 287 | 96.29 174 | 98.40 158 |
|
cascas | | | 94.63 207 | 93.86 214 | 96.93 172 | 96.91 223 | 94.27 218 | 96.00 311 | 98.51 132 | 85.55 314 | 94.54 179 | 96.23 276 | 84.20 267 | 98.87 192 | 95.80 109 | 96.98 153 | 97.66 185 |
|
tpm | | | 94.13 235 | 93.80 217 | 95.12 271 | 96.50 243 | 87.91 311 | 97.44 257 | 95.89 317 | 92.62 206 | 96.37 150 | 96.30 273 | 84.13 268 | 98.30 261 | 93.24 175 | 91.66 248 | 99.14 113 |
|
IterMVS | | | 94.09 236 | 93.85 215 | 94.80 281 | 97.99 158 | 90.35 282 | 97.18 277 | 98.12 198 | 93.68 168 | 92.46 260 | 97.34 202 | 84.05 269 | 97.41 296 | 92.51 201 | 91.33 249 | 96.62 257 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Patchmatch-test1 | | | 95.32 168 | 94.97 153 | 96.35 223 | 97.67 173 | 91.29 269 | 97.33 269 | 97.60 233 | 94.68 122 | 96.92 114 | 96.95 240 | 83.97 270 | 98.50 227 | 91.33 229 | 98.32 122 | 99.25 101 |
|
semantic-postprocess | | | | | 94.85 278 | 97.98 160 | 90.56 280 | | 98.11 203 | 93.75 158 | 92.58 254 | 97.48 192 | 83.91 271 | 97.41 296 | 92.48 202 | 91.30 250 | 96.58 262 |
|
TR-MVS | | | 94.94 186 | 94.20 192 | 97.17 157 | 97.75 170 | 94.14 221 | 97.59 251 | 97.02 283 | 92.28 227 | 95.75 160 | 97.64 184 | 83.88 272 | 98.96 179 | 89.77 257 | 96.15 184 | 98.40 158 |
|
jajsoiax | | | 95.45 155 | 95.03 148 | 96.73 180 | 95.42 302 | 94.63 201 | 99.14 42 | 98.52 130 | 95.74 73 | 93.22 237 | 98.36 122 | 83.87 273 | 98.65 208 | 96.95 68 | 94.04 211 | 96.91 217 |
|
Anonymous20231206 | | | 91.66 278 | 91.10 266 | 93.33 300 | 94.02 317 | 87.35 314 | 98.58 142 | 97.26 273 | 90.48 263 | 90.16 283 | 96.31 272 | 83.83 274 | 96.53 318 | 79.36 320 | 89.90 259 | 96.12 285 |
|
tpm2 | | | 94.19 229 | 93.76 222 | 95.46 256 | 97.23 203 | 89.04 297 | 97.31 271 | 96.85 299 | 87.08 305 | 96.21 152 | 96.79 256 | 83.75 275 | 98.74 203 | 92.43 203 | 96.23 181 | 98.59 150 |
|
mvs_tets | | | 95.41 159 | 95.00 149 | 96.65 192 | 95.58 297 | 94.42 211 | 99.00 60 | 98.55 124 | 95.73 74 | 93.21 238 | 98.38 120 | 83.45 276 | 98.63 209 | 97.09 63 | 94.00 213 | 96.91 217 |
|
tpmp4_e23 | | | 93.91 243 | 93.42 242 | 95.38 264 | 97.62 176 | 88.59 304 | 97.52 255 | 97.34 265 | 87.94 301 | 94.17 209 | 96.79 256 | 82.91 277 | 99.05 167 | 90.62 240 | 95.91 192 | 98.50 153 |
|
OurMVSNet-221017-0 | | | 94.21 227 | 94.00 205 | 94.85 278 | 95.60 296 | 89.22 294 | 98.89 75 | 97.43 258 | 95.29 99 | 92.18 266 | 98.52 110 | 82.86 278 | 98.59 213 | 93.46 170 | 91.76 246 | 96.74 235 |
|
UGNet | | | 96.78 99 | 96.30 103 | 98.19 94 | 98.24 140 | 95.89 132 | 98.88 77 | 98.93 36 | 97.39 16 | 96.81 122 | 97.84 165 | 82.60 279 | 99.90 25 | 96.53 88 | 99.49 68 | 98.79 138 |
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 |
pmmvs5 | | | 93.65 248 | 92.97 247 | 95.68 249 | 95.49 300 | 92.37 253 | 98.20 188 | 97.28 271 | 89.66 286 | 92.58 254 | 97.26 207 | 82.14 280 | 98.09 272 | 93.18 178 | 90.95 253 | 96.58 262 |
|
DWT-MVSNet_test | | | 94.82 192 | 94.36 185 | 96.20 231 | 97.35 197 | 90.79 274 | 98.34 172 | 96.57 307 | 92.91 199 | 95.33 164 | 96.44 270 | 82.00 281 | 99.12 156 | 94.52 144 | 95.78 195 | 98.70 142 |
|
ACMH | | 92.88 16 | 94.55 212 | 93.95 209 | 96.34 225 | 97.63 175 | 93.26 242 | 98.81 96 | 98.49 141 | 93.43 177 | 89.74 286 | 98.53 107 | 81.91 282 | 99.08 165 | 93.69 164 | 93.30 229 | 96.70 242 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ITE_SJBPF | | | | | 95.44 258 | 97.42 192 | 91.32 268 | | 97.50 247 | 95.09 110 | 93.59 226 | 98.35 123 | 81.70 283 | 98.88 191 | 89.71 260 | 93.39 227 | 96.12 285 |
|
GBi-Net | | | 94.49 214 | 93.80 217 | 96.56 205 | 98.21 142 | 95.00 163 | 98.82 90 | 98.18 185 | 92.46 210 | 94.09 212 | 97.07 223 | 81.16 284 | 97.95 279 | 92.08 207 | 92.14 239 | 96.72 238 |
|
test1 | | | 94.49 214 | 93.80 217 | 96.56 205 | 98.21 142 | 95.00 163 | 98.82 90 | 98.18 185 | 92.46 210 | 94.09 212 | 97.07 223 | 81.16 284 | 97.95 279 | 92.08 207 | 92.14 239 | 96.72 238 |
|
FMVSNet2 | | | 94.47 216 | 93.61 230 | 97.04 164 | 98.21 142 | 96.43 99 | 98.79 104 | 98.27 168 | 92.46 210 | 93.50 232 | 97.09 221 | 81.16 284 | 98.00 277 | 91.09 230 | 91.93 243 | 96.70 242 |
|
GA-MVS | | | 94.81 193 | 94.03 203 | 97.14 158 | 97.15 211 | 93.86 227 | 96.76 295 | 97.58 234 | 94.00 145 | 94.76 176 | 97.04 231 | 80.91 287 | 98.48 228 | 91.79 217 | 96.25 180 | 99.09 116 |
|
SixPastTwentyTwo | | | 93.34 251 | 92.86 248 | 94.75 282 | 95.67 294 | 89.41 292 | 98.75 111 | 96.67 304 | 93.89 151 | 90.15 284 | 98.25 136 | 80.87 288 | 98.27 264 | 90.90 235 | 90.64 254 | 96.57 264 |
|
ACMH+ | | 92.99 14 | 94.30 223 | 93.77 220 | 95.88 242 | 97.81 168 | 92.04 258 | 98.71 121 | 98.37 157 | 93.99 146 | 90.60 281 | 98.47 113 | 80.86 289 | 99.05 167 | 92.75 193 | 92.40 238 | 96.55 267 |
|
gg-mvs-nofinetune | | | 92.21 265 | 90.58 279 | 97.13 159 | 96.75 232 | 95.09 160 | 95.85 313 | 89.40 346 | 85.43 315 | 94.50 181 | 81.98 337 | 80.80 290 | 98.40 254 | 92.16 205 | 98.33 121 | 97.88 176 |
|
test20.03 | | | 90.89 286 | 90.38 280 | 92.43 305 | 93.48 318 | 88.14 309 | 98.33 173 | 97.56 235 | 93.40 182 | 87.96 297 | 96.71 259 | 80.69 291 | 94.13 329 | 79.15 321 | 86.17 306 | 95.01 307 |
|
test_normal | | | 94.72 200 | 93.59 231 | 98.11 99 | 95.30 304 | 95.95 119 | 97.91 223 | 97.39 263 | 94.64 126 | 85.70 308 | 95.88 286 | 80.52 292 | 99.36 135 | 96.69 82 | 98.30 123 | 99.01 125 |
|
DI_MVS_plusplus_test | | | 94.74 199 | 93.62 229 | 98.09 100 | 95.34 303 | 95.92 128 | 98.09 206 | 97.34 265 | 94.66 125 | 85.89 305 | 95.91 285 | 80.49 293 | 99.38 134 | 96.66 83 | 98.22 124 | 98.97 127 |
|
VPNet | | | 94.99 180 | 94.19 193 | 97.40 148 | 97.16 210 | 96.57 93 | 98.71 121 | 98.97 29 | 95.67 76 | 94.84 171 | 98.24 137 | 80.36 294 | 98.67 207 | 96.46 90 | 87.32 295 | 96.96 209 |
|
GG-mvs-BLEND | | | | | 96.59 200 | 96.34 256 | 94.98 166 | 96.51 305 | 88.58 347 | | 93.10 244 | 94.34 305 | 80.34 295 | 98.05 274 | 89.53 264 | 96.99 152 | 96.74 235 |
|
PVSNet_0 | | 88.72 19 | 91.28 281 | 90.03 284 | 95.00 274 | 97.99 158 | 87.29 315 | 94.84 324 | 98.50 137 | 92.06 230 | 89.86 285 | 95.19 295 | 79.81 296 | 99.39 133 | 92.27 204 | 69.79 338 | 98.33 166 |
|
MS-PatchMatch | | | 93.84 244 | 93.63 228 | 94.46 290 | 96.18 272 | 89.45 290 | 97.76 239 | 98.27 168 | 92.23 228 | 92.13 267 | 97.49 191 | 79.50 297 | 98.69 204 | 89.75 259 | 99.38 80 | 95.25 301 |
|
MVS-HIRNet | | | 89.46 295 | 88.40 297 | 92.64 304 | 97.58 180 | 82.15 326 | 94.16 331 | 93.05 342 | 75.73 334 | 90.90 277 | 82.52 336 | 79.42 298 | 98.33 256 | 83.53 311 | 98.68 103 | 97.43 188 |
|
MDA-MVSNet-bldmvs | | | 89.97 292 | 88.35 298 | 94.83 280 | 95.21 305 | 91.34 267 | 97.64 248 | 97.51 244 | 88.36 299 | 71.17 337 | 96.13 281 | 79.22 299 | 96.63 317 | 83.65 310 | 86.27 305 | 96.52 270 |
|
XVG-ACMP-BASELINE | | | 94.54 213 | 94.14 196 | 95.75 248 | 96.55 240 | 91.65 265 | 98.11 203 | 98.44 146 | 94.96 115 | 94.22 205 | 97.90 159 | 79.18 300 | 99.11 160 | 94.05 157 | 93.85 216 | 96.48 275 |
|
TESTMET0.1,1 | | | 94.18 231 | 93.69 226 | 95.63 250 | 96.92 221 | 89.12 295 | 96.91 286 | 94.78 331 | 93.17 189 | 94.88 170 | 96.45 269 | 78.52 301 | 98.92 185 | 93.09 179 | 98.50 113 | 98.85 134 |
|
pmmvs-eth3d | | | 90.36 290 | 89.05 293 | 94.32 292 | 91.10 326 | 92.12 255 | 97.63 250 | 96.95 290 | 88.86 296 | 84.91 320 | 93.13 312 | 78.32 302 | 96.74 312 | 88.70 279 | 81.81 318 | 94.09 323 |
|
IB-MVS | | 91.98 17 | 93.27 253 | 91.97 260 | 97.19 155 | 97.47 187 | 93.41 240 | 97.09 280 | 95.99 313 | 93.32 185 | 92.47 259 | 95.73 289 | 78.06 303 | 99.53 122 | 94.59 142 | 82.98 314 | 98.62 149 |
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 |
LF4IMVS | | | 93.14 257 | 92.79 250 | 94.20 293 | 95.88 287 | 88.67 302 | 97.66 247 | 97.07 279 | 93.81 156 | 91.71 270 | 97.65 182 | 77.96 304 | 98.81 199 | 91.47 227 | 91.92 244 | 95.12 302 |
|
test-mter | | | 94.08 237 | 93.51 237 | 95.80 245 | 96.77 229 | 89.70 287 | 96.91 286 | 95.21 326 | 92.89 200 | 94.83 173 | 95.72 291 | 77.69 305 | 98.97 176 | 93.06 180 | 98.50 113 | 98.72 140 |
|
USDC | | | 93.33 252 | 92.71 251 | 95.21 268 | 96.83 228 | 90.83 273 | 96.91 286 | 97.50 247 | 93.84 154 | 90.72 279 | 98.14 142 | 77.69 305 | 98.82 198 | 89.51 265 | 93.21 232 | 95.97 289 |
|
test_0402 | | | 91.32 280 | 90.27 282 | 94.48 288 | 96.60 238 | 91.12 271 | 98.50 157 | 97.22 275 | 86.10 310 | 88.30 296 | 96.98 237 | 77.65 307 | 97.99 278 | 78.13 324 | 92.94 234 | 94.34 319 |
|
K. test v3 | | | 92.55 261 | 91.91 262 | 94.48 288 | 95.64 295 | 89.24 293 | 99.07 54 | 94.88 330 | 94.04 143 | 86.78 301 | 97.59 187 | 77.64 308 | 97.64 290 | 92.08 207 | 89.43 266 | 96.57 264 |
|
TDRefinement | | | 91.06 284 | 89.68 287 | 95.21 268 | 85.35 337 | 91.49 266 | 98.51 156 | 97.07 279 | 91.47 242 | 88.83 294 | 97.84 165 | 77.31 309 | 99.09 164 | 92.79 192 | 77.98 331 | 95.04 305 |
|
new_pmnet | | | 90.06 291 | 89.00 294 | 93.22 303 | 94.18 314 | 88.32 308 | 96.42 306 | 96.89 296 | 86.19 308 | 85.67 309 | 93.62 307 | 77.18 310 | 97.10 300 | 81.61 315 | 89.29 268 | 94.23 320 |
|
new-patchmatchnet | | | 88.50 300 | 87.45 301 | 91.67 309 | 90.31 328 | 85.89 318 | 97.16 278 | 97.33 268 | 89.47 289 | 83.63 322 | 92.77 322 | 76.38 311 | 95.06 327 | 82.70 312 | 77.29 332 | 94.06 324 |
|
lessismore_v0 | | | | | 94.45 291 | 94.93 309 | 88.44 306 | | 91.03 344 | | 86.77 302 | 97.64 184 | 76.23 312 | 98.42 241 | 90.31 248 | 85.64 310 | 96.51 272 |
|
TinyColmap | | | 92.31 264 | 91.53 263 | 94.65 284 | 96.92 221 | 89.75 286 | 96.92 284 | 96.68 303 | 90.45 265 | 89.62 287 | 97.85 164 | 76.06 313 | 98.81 199 | 86.74 297 | 92.51 237 | 95.41 300 |
|
pmmvs6 | | | 91.77 277 | 90.63 278 | 95.17 270 | 94.69 313 | 91.24 270 | 98.67 132 | 97.92 222 | 86.14 309 | 89.62 287 | 97.56 190 | 75.79 314 | 98.34 255 | 90.75 237 | 84.56 313 | 95.94 290 |
|
MIMVSNet | | | 93.26 254 | 92.21 258 | 96.41 219 | 97.73 172 | 93.13 246 | 95.65 316 | 97.03 282 | 91.27 256 | 94.04 215 | 96.06 282 | 75.33 315 | 97.19 299 | 86.56 298 | 96.23 181 | 98.92 132 |
|
UnsupCasMVSNet_eth | | | 90.99 285 | 89.92 286 | 94.19 294 | 94.08 316 | 89.83 285 | 97.13 279 | 98.67 104 | 93.69 166 | 85.83 307 | 96.19 279 | 75.15 316 | 96.74 312 | 89.14 270 | 79.41 325 | 96.00 288 |
|
LFMVS | | | 95.86 129 | 94.98 151 | 98.47 78 | 98.87 104 | 96.32 104 | 98.84 87 | 96.02 312 | 93.40 182 | 98.62 40 | 99.20 35 | 74.99 317 | 99.63 103 | 97.72 42 | 97.20 149 | 99.46 82 |
|
testpf | | | 88.74 298 | 89.09 291 | 87.69 316 | 95.78 290 | 83.16 324 | 84.05 343 | 94.13 339 | 85.22 316 | 90.30 282 | 94.39 304 | 74.92 318 | 95.80 322 | 89.77 257 | 93.28 231 | 84.10 338 |
|
CMPMVS | | 66.06 21 | 89.70 293 | 89.67 288 | 89.78 312 | 93.19 319 | 76.56 332 | 97.00 281 | 98.35 159 | 80.97 328 | 81.57 326 | 97.75 173 | 74.75 319 | 98.61 210 | 89.85 256 | 93.63 220 | 94.17 321 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
FMVSNet5 | | | 91.81 276 | 90.92 270 | 94.49 287 | 97.21 205 | 92.09 256 | 98.00 214 | 97.55 239 | 89.31 293 | 90.86 278 | 95.61 294 | 74.48 320 | 95.32 325 | 85.57 305 | 89.70 260 | 96.07 287 |
|
testgi | | | 93.06 258 | 92.45 255 | 94.88 277 | 96.43 247 | 89.90 284 | 98.75 111 | 97.54 240 | 95.60 79 | 91.63 271 | 97.91 158 | 74.46 321 | 97.02 301 | 86.10 301 | 93.67 218 | 97.72 182 |
|
VDD-MVS | | | 95.82 131 | 95.23 141 | 97.61 133 | 98.84 108 | 93.98 224 | 98.68 129 | 97.40 261 | 95.02 112 | 97.95 71 | 99.34 19 | 74.37 322 | 99.78 74 | 98.64 4 | 96.80 155 | 99.08 119 |
|
FMVSNet1 | | | 93.19 256 | 92.07 259 | 96.56 205 | 97.54 183 | 95.00 163 | 98.82 90 | 98.18 185 | 90.38 267 | 92.27 263 | 97.07 223 | 73.68 323 | 97.95 279 | 89.36 268 | 91.30 250 | 96.72 238 |
|
VDDNet | | | 95.36 164 | 94.53 177 | 97.86 111 | 98.10 151 | 95.13 159 | 98.85 84 | 97.75 228 | 90.46 264 | 98.36 52 | 99.39 7 | 73.27 324 | 99.64 100 | 97.98 27 | 96.58 160 | 98.81 137 |
|
test2356 | | | 88.68 299 | 88.61 295 | 88.87 314 | 89.90 330 | 78.23 330 | 95.11 319 | 96.66 306 | 88.66 298 | 89.06 292 | 94.33 306 | 73.14 325 | 92.56 336 | 75.56 329 | 95.11 199 | 95.81 293 |
|
test1235678 | | | 86.26 305 | 85.81 304 | 87.62 317 | 86.97 335 | 75.00 337 | 96.55 303 | 96.32 311 | 86.08 311 | 81.32 327 | 92.98 318 | 73.10 326 | 92.05 337 | 71.64 333 | 87.32 295 | 95.81 293 |
|
testus | | | 88.91 297 | 89.08 292 | 88.40 315 | 91.39 324 | 76.05 333 | 96.56 301 | 96.48 308 | 89.38 292 | 89.39 290 | 95.17 297 | 70.94 327 | 93.56 332 | 77.04 326 | 95.41 197 | 95.61 297 |
|
DeepMVS_CX | | | | | 86.78 319 | 97.09 214 | 72.30 339 | | 95.17 329 | 75.92 333 | 84.34 321 | 95.19 295 | 70.58 328 | 95.35 324 | 79.98 319 | 89.04 271 | 92.68 329 |
|
OpenMVS_ROB | | 86.42 20 | 89.00 296 | 87.43 302 | 93.69 297 | 93.08 320 | 89.42 291 | 97.91 223 | 96.89 296 | 78.58 331 | 85.86 306 | 94.69 301 | 69.48 329 | 98.29 263 | 77.13 325 | 93.29 230 | 93.36 328 |
|
1111 | | | 84.94 306 | 84.30 307 | 86.86 318 | 87.59 333 | 75.10 335 | 96.63 298 | 96.43 309 | 82.53 323 | 80.75 328 | 92.91 320 | 68.94 330 | 93.79 330 | 68.24 336 | 84.66 312 | 91.70 330 |
|
.test1245 | | | 73.05 315 | 76.31 313 | 63.27 336 | 87.59 333 | 75.10 335 | 96.63 298 | 96.43 309 | 82.53 323 | 80.75 328 | 92.91 320 | 68.94 330 | 93.79 330 | 68.24 336 | 12.72 349 | 20.91 347 |
|
EG-PatchMatch MVS | | | 91.13 282 | 90.12 283 | 94.17 295 | 94.73 312 | 89.00 298 | 98.13 200 | 97.81 225 | 89.22 294 | 85.32 310 | 96.46 268 | 67.71 332 | 98.42 241 | 87.89 292 | 93.82 217 | 95.08 304 |
|
MIMVSNet1 | | | 89.67 294 | 88.28 299 | 93.82 296 | 92.81 322 | 91.08 272 | 98.01 212 | 97.45 256 | 87.95 300 | 87.90 298 | 95.87 287 | 67.63 333 | 94.56 328 | 78.73 323 | 88.18 285 | 95.83 292 |
|
pmmvs3 | | | 86.67 304 | 84.86 306 | 92.11 308 | 88.16 332 | 87.19 316 | 96.63 298 | 94.75 332 | 79.88 330 | 87.22 300 | 92.75 323 | 66.56 334 | 95.20 326 | 81.24 316 | 76.56 334 | 93.96 325 |
|
test12356 | | | 83.47 308 | 83.37 308 | 83.78 324 | 84.43 338 | 70.09 342 | 95.12 318 | 95.60 323 | 82.98 321 | 78.89 330 | 92.43 327 | 64.99 335 | 91.41 339 | 70.36 334 | 85.55 311 | 89.82 332 |
|
tmp_tt | | | 68.90 317 | 66.97 317 | 74.68 332 | 50.78 352 | 59.95 348 | 87.13 339 | 83.47 352 | 38.80 346 | 62.21 341 | 96.23 276 | 64.70 336 | 76.91 349 | 88.91 276 | 30.49 347 | 87.19 335 |
|
UnsupCasMVSNet_bld | | | 87.17 302 | 85.12 305 | 93.31 301 | 91.94 323 | 88.77 299 | 94.92 323 | 98.30 165 | 84.30 320 | 82.30 323 | 90.04 330 | 63.96 337 | 97.25 298 | 85.85 304 | 74.47 337 | 93.93 326 |
|
testing_2 | | | 90.61 289 | 88.50 296 | 96.95 170 | 90.08 329 | 95.57 142 | 97.69 244 | 98.06 215 | 93.02 194 | 76.55 331 | 92.48 326 | 61.18 338 | 98.44 238 | 95.45 122 | 91.98 242 | 96.84 226 |
|
Test4 | | | 92.21 265 | 90.34 281 | 97.82 115 | 92.83 321 | 95.87 134 | 97.94 219 | 98.05 218 | 94.50 131 | 82.12 324 | 94.48 302 | 59.54 339 | 98.54 217 | 95.39 123 | 98.22 124 | 99.06 121 |
|
PM-MVS | | | 87.77 301 | 86.55 303 | 91.40 310 | 91.03 327 | 83.36 323 | 96.92 284 | 95.18 328 | 91.28 255 | 86.48 304 | 93.42 308 | 53.27 340 | 96.74 312 | 89.43 267 | 81.97 317 | 94.11 322 |
|
Anonymous20231211 | | | 83.69 307 | 81.50 309 | 90.26 311 | 89.23 331 | 80.10 329 | 97.97 216 | 97.06 281 | 72.79 336 | 82.05 325 | 92.57 324 | 50.28 341 | 96.32 321 | 76.15 328 | 75.38 335 | 94.37 318 |
|
testmv | | | 78.74 309 | 77.35 310 | 82.89 326 | 78.16 346 | 69.30 343 | 95.87 312 | 94.65 333 | 81.11 327 | 70.98 338 | 87.11 334 | 46.31 342 | 90.42 340 | 65.28 339 | 76.72 333 | 88.95 333 |
|
ambc | | | | | 89.49 313 | 86.66 336 | 75.78 334 | 92.66 334 | 96.72 301 | | 86.55 303 | 92.50 325 | 46.01 343 | 97.90 282 | 90.32 247 | 82.09 315 | 94.80 308 |
|
Gipuma | | | 78.40 311 | 76.75 312 | 83.38 325 | 95.54 298 | 80.43 328 | 79.42 344 | 97.40 261 | 64.67 338 | 73.46 334 | 80.82 339 | 45.65 344 | 93.14 334 | 66.32 338 | 87.43 293 | 76.56 343 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
EMVS | | | 64.07 321 | 63.26 322 | 66.53 335 | 81.73 341 | 58.81 351 | 91.85 335 | 84.75 351 | 51.93 345 | 59.09 343 | 75.13 343 | 43.32 345 | 79.09 348 | 42.03 346 | 39.47 344 | 61.69 344 |
|
E-PMN | | | 64.94 320 | 64.25 320 | 67.02 334 | 82.28 340 | 59.36 350 | 91.83 336 | 85.63 350 | 52.69 343 | 60.22 342 | 77.28 342 | 41.06 346 | 80.12 347 | 46.15 345 | 41.14 343 | 61.57 345 |
|
FPMVS | | | 77.62 313 | 77.14 311 | 79.05 328 | 79.25 343 | 60.97 347 | 95.79 314 | 95.94 315 | 65.96 337 | 67.93 339 | 94.40 303 | 37.73 347 | 88.88 342 | 68.83 335 | 88.46 282 | 87.29 334 |
|
PMMVS2 | | | 77.95 312 | 75.44 315 | 85.46 321 | 82.54 339 | 74.95 338 | 94.23 330 | 93.08 341 | 72.80 335 | 74.68 333 | 87.38 332 | 36.36 348 | 91.56 338 | 73.95 331 | 63.94 339 | 89.87 331 |
|
no-one | | | 74.41 314 | 70.76 316 | 85.35 322 | 79.88 342 | 76.83 331 | 94.68 326 | 94.22 337 | 80.33 329 | 63.81 340 | 79.73 340 | 35.45 349 | 93.36 333 | 71.78 332 | 36.99 346 | 85.86 337 |
|
LCM-MVSNet | | | 78.70 310 | 76.24 314 | 86.08 320 | 77.26 347 | 71.99 340 | 94.34 329 | 96.72 301 | 61.62 340 | 76.53 332 | 89.33 331 | 33.91 350 | 92.78 335 | 81.85 314 | 74.60 336 | 93.46 327 |
|
ANet_high | | | 69.08 316 | 65.37 318 | 80.22 327 | 65.99 350 | 71.96 341 | 90.91 337 | 90.09 345 | 82.62 322 | 49.93 346 | 78.39 341 | 29.36 351 | 81.75 345 | 62.49 342 | 38.52 345 | 86.95 336 |
|
PNet_i23d | | | 67.70 318 | 65.07 319 | 75.60 330 | 78.61 344 | 59.61 349 | 89.14 338 | 88.24 348 | 61.83 339 | 52.37 344 | 80.89 338 | 18.91 352 | 84.91 344 | 62.70 341 | 52.93 341 | 82.28 339 |
|
PMVS | | 61.03 23 | 65.95 319 | 63.57 321 | 73.09 333 | 57.90 351 | 51.22 352 | 85.05 342 | 93.93 340 | 54.45 342 | 44.32 347 | 83.57 335 | 13.22 353 | 89.15 341 | 58.68 343 | 81.00 320 | 78.91 342 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test123 | | | 20.95 328 | 23.72 329 | 12.64 339 | 13.54 354 | 8.19 354 | 96.55 303 | 6.13 357 | 7.48 349 | 16.74 350 | 37.98 348 | 12.97 354 | 6.05 351 | 16.69 348 | 5.43 351 | 23.68 346 |
|
wuyk23d | | | 30.17 325 | 30.18 327 | 30.16 338 | 78.61 344 | 43.29 353 | 66.79 345 | 14.21 355 | 17.31 347 | 14.82 351 | 11.93 351 | 11.55 355 | 41.43 350 | 37.08 347 | 19.30 348 | 5.76 349 |
|
wuykxyi23d | | | 63.73 322 | 58.86 324 | 78.35 329 | 67.62 349 | 67.90 344 | 86.56 340 | 87.81 349 | 58.26 341 | 42.49 348 | 70.28 345 | 11.55 355 | 85.05 343 | 63.66 340 | 41.50 342 | 82.11 340 |
|
MVE | | 62.14 22 | 63.28 323 | 59.38 323 | 74.99 331 | 74.33 348 | 65.47 345 | 85.55 341 | 80.50 353 | 52.02 344 | 51.10 345 | 75.00 344 | 10.91 357 | 80.50 346 | 51.60 344 | 53.40 340 | 78.99 341 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 21.48 327 | 24.95 328 | 11.09 340 | 14.89 353 | 6.47 355 | 96.56 301 | 9.87 356 | 7.55 348 | 17.93 349 | 39.02 347 | 9.43 358 | 5.90 352 | 16.56 349 | 12.72 349 | 20.91 347 |
|
sosnet-low-res | | | 0.00 331 | 0.00 332 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
sosnet | | | 0.00 331 | 0.00 332 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
uncertanet | | | 0.00 331 | 0.00 332 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
Regformer | | | 0.00 331 | 0.00 332 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
ab-mvs-re | | | 8.20 329 | 10.94 330 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 98.43 115 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
uanet | | | 0.00 331 | 0.00 332 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 0.00 359 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
test_part2 | | | | | | 99.63 21 | 99.18 1 | | | | 99.27 6 | | | | | | |
|
test_all | | | | | | | | | 98.84 54 | | | | | | | | |
|
MTGPA | | | | | | | | | 98.74 79 | | | | | | | | |
|
MTMP | | | | | | | | | 94.14 338 | | | | | | | | |
|
gm-plane-assit | | | | | | 95.88 287 | 87.47 313 | | | 89.74 284 | | 96.94 242 | | 99.19 150 | 93.32 174 | | |
|
test9_res | | | | | | | | | | | | | | | 96.39 94 | 99.57 56 | 99.69 36 |
|
agg_prior2 | | | | | | | | | | | | | | | 95.87 106 | 99.57 56 | 99.68 42 |
|
agg_prior | | | | | | 99.30 53 | 98.38 18 | | 98.72 86 | | 97.57 94 | | | 99.81 50 | | | |
|
test_prior4 | | | | | | | 98.01 42 | 97.86 231 | | | | | | | | | |
|
test_prior | | | | | 99.19 28 | 99.31 48 | 98.22 31 | | 98.84 54 | | | | | 99.70 91 | | | 99.65 51 |
|
旧先验2 | | | | | | | | 97.57 253 | | 91.30 253 | 98.67 37 | | | 99.80 57 | 95.70 115 | | |
|
新几何2 | | | | | | | | 97.64 248 | | | | | | | | | |
|
无先验 | | | | | | | | 97.58 252 | 98.72 86 | 91.38 247 | | | | 99.87 35 | 93.36 172 | | 99.60 60 |
|
原ACMM2 | | | | | | | | 97.67 246 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 99.89 27 | 91.65 222 | | |
|
testdata1 | | | | | | | | 97.32 270 | | 96.34 57 | | | | | | | |
|
plane_prior7 | | | | | | 97.42 192 | 94.63 201 | | | | | | | | | | |
|
plane_prior5 | | | | | | | | | 98.56 122 | | | | | 99.03 172 | 96.07 97 | 94.27 202 | 96.92 212 |
|
plane_prior4 | | | | | | | | | | | | 98.28 131 | | | | | |
|
plane_prior3 | | | | | | | 94.61 204 | | | 97.02 39 | 95.34 162 | | | | | | |
|
plane_prior2 | | | | | | | | 98.80 99 | | 97.28 21 | | | | | | | |
|
plane_prior1 | | | | | | 97.37 196 | | | | | | | | | | | |
|
plane_prior | | | | | | | 94.60 206 | 98.44 162 | | 96.74 46 | | | | | | 94.22 204 | |
|
n2 | | | | | | | | | 0.00 358 | | | | | | | | |
|
nn | | | | | | | | | 0.00 358 | | | | | | | | |
|
door-mid | | | | | | | | | 94.37 335 | | | | | | | | |
|
test11 | | | | | | | | | 98.66 107 | | | | | | | | |
|
door | | | | | | | | | 94.64 334 | | | | | | | | |
|
HQP5-MVS | | | | | | | 94.25 219 | | | | | | | | | | |
|
HQP-NCC | | | | | | 97.20 206 | | 98.05 208 | | 96.43 54 | 94.45 183 | | | | | | |
|
ACMP_Plane | | | | | | 97.20 206 | | 98.05 208 | | 96.43 54 | 94.45 183 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 95.30 125 | | |
|
HQP4-MVS | | | | | | | | | | | 94.45 183 | | | 98.96 179 | | | 96.87 223 |
|
HQP3-MVS | | | | | | | | | 98.46 142 | | | | | | | 94.18 206 | |
|
NP-MVS | | | | | | 97.28 200 | 94.51 209 | | | | | 97.73 174 | | | | | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 92.97 233 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 93.61 221 | |
|