3Dnovator+ | | 77.84 4 | 85.48 44 | 84.47 52 | 88.51 2 | 91.08 64 | 73.49 13 | 93.18 4 | 93.78 7 | 80.79 11 | 76.66 143 | 93.37 34 | 60.40 161 | 96.75 11 | 77.20 78 | 93.73 46 | 95.29 1 |
|
TSAR-MVS + MP. | | | 88.02 10 | 88.11 8 | 87.72 22 | 93.68 26 | 72.13 38 | 91.41 26 | 92.35 50 | 74.62 89 | 88.90 6 | 93.85 27 | 75.75 9 | 96.00 33 | 87.80 5 | 94.63 32 | 95.04 2 |
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IS-MVSNet | | | 83.15 65 | 82.81 64 | 84.18 90 | 89.94 82 | 63.30 200 | 91.59 24 | 88.46 184 | 79.04 25 | 79.49 89 | 92.16 52 | 65.10 83 | 94.28 84 | 67.71 160 | 91.86 57 | 94.95 3 |
|
SteuartSystems-ACMMP | | | 88.72 5 | 88.86 5 | 88.32 4 | 92.14 53 | 72.96 19 | 93.73 3 | 93.67 8 | 80.19 15 | 88.10 9 | 94.80 4 | 73.76 21 | 97.11 3 | 87.51 8 | 95.82 9 | 94.90 4 |
Skip Steuart: Steuart Systems R&D Blog. |
canonicalmvs | | | 85.91 39 | 85.87 37 | 86.04 53 | 89.84 84 | 69.44 78 | 90.45 43 | 93.00 26 | 76.70 56 | 88.01 11 | 91.23 73 | 73.28 23 | 93.91 103 | 81.50 47 | 88.80 87 | 94.77 5 |
|
alignmvs | | | 85.48 44 | 85.32 44 | 85.96 54 | 89.51 95 | 69.47 76 | 89.74 57 | 92.47 44 | 76.17 67 | 87.73 13 | 91.46 70 | 70.32 43 | 93.78 112 | 81.51 46 | 88.95 84 | 94.63 6 |
|
MP-MVS-pluss | | | 87.67 12 | 87.72 11 | 87.54 26 | 93.64 27 | 72.04 39 | 89.80 55 | 93.50 11 | 75.17 83 | 86.34 17 | 95.29 2 | 70.86 38 | 96.00 33 | 88.78 3 | 96.04 4 | 94.58 7 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
DeepPCF-MVS | | 80.84 1 | 88.10 6 | 88.56 6 | 86.73 39 | 92.24 51 | 69.03 80 | 89.57 62 | 93.39 15 | 77.53 39 | 89.79 5 | 94.12 22 | 78.98 2 | 96.58 20 | 85.66 12 | 95.72 10 | 94.58 7 |
|
VDD-MVS | | | 83.01 69 | 82.36 69 | 84.96 69 | 91.02 65 | 66.40 130 | 88.91 76 | 88.11 187 | 77.57 35 | 84.39 42 | 93.29 36 | 52.19 217 | 93.91 103 | 77.05 81 | 88.70 89 | 94.57 9 |
|
VDDNet | | | 81.52 88 | 80.67 89 | 84.05 94 | 90.44 72 | 64.13 184 | 89.73 58 | 85.91 220 | 71.11 155 | 83.18 55 | 93.48 31 | 50.54 249 | 93.49 127 | 73.40 117 | 88.25 98 | 94.54 10 |
|
APDe-MVS | | | 89.15 2 | 89.63 2 | 87.73 20 | 94.49 9 | 71.69 42 | 93.83 2 | 93.96 4 | 75.70 72 | 91.06 3 | 96.03 1 | 76.84 4 | 97.03 5 | 89.09 2 | 95.65 14 | 94.47 11 |
|
MCST-MVS | | | 87.37 18 | 87.25 16 | 87.73 20 | 94.53 8 | 72.46 32 | 89.82 53 | 93.82 6 | 73.07 124 | 84.86 35 | 92.89 45 | 76.22 6 | 96.33 23 | 84.89 19 | 95.13 23 | 94.40 12 |
|
CANet | | | 86.45 30 | 86.10 34 | 87.51 27 | 90.09 78 | 70.94 50 | 89.70 59 | 92.59 43 | 81.78 4 | 81.32 74 | 91.43 71 | 70.34 42 | 97.23 2 | 84.26 27 | 93.36 47 | 94.37 13 |
|
PHI-MVS | | | 86.43 31 | 86.17 33 | 87.24 30 | 90.88 68 | 70.96 48 | 92.27 17 | 94.07 3 | 72.45 136 | 85.22 26 | 91.90 58 | 69.47 51 | 96.42 22 | 83.28 34 | 95.94 6 | 94.35 14 |
|
CNVR-MVS | | | 88.93 4 | 89.13 4 | 88.33 3 | 94.77 3 | 73.82 6 | 90.51 39 | 93.00 26 | 80.90 10 | 88.06 10 | 94.06 24 | 76.43 5 | 96.84 7 | 88.48 4 | 95.99 5 | 94.34 15 |
|
MVS_0304 | | | 86.37 35 | 85.81 39 | 88.02 8 | 90.13 76 | 72.39 33 | 89.66 60 | 92.75 38 | 81.64 6 | 82.66 64 | 92.04 54 | 64.44 87 | 97.35 1 | 84.76 21 | 94.25 42 | 94.33 16 |
|
HPM-MVS | | | 87.11 22 | 86.98 21 | 87.50 28 | 93.88 23 | 72.16 37 | 92.19 18 | 93.33 16 | 76.07 69 | 83.81 49 | 93.95 26 | 69.77 49 | 96.01 32 | 85.15 14 | 94.66 31 | 94.32 17 |
|
CDPH-MVS | | | 85.76 41 | 85.29 46 | 87.17 32 | 93.49 30 | 71.08 46 | 88.58 90 | 92.42 48 | 68.32 205 | 84.61 37 | 93.48 31 | 72.32 30 | 96.15 30 | 79.00 60 | 95.43 16 | 94.28 18 |
|
DeepC-MVS_fast | | 79.65 3 | 86.91 25 | 86.62 26 | 87.76 19 | 93.52 29 | 72.37 35 | 91.26 27 | 93.04 23 | 76.62 57 | 84.22 44 | 93.36 35 | 71.44 35 | 96.76 10 | 80.82 51 | 95.33 20 | 94.16 19 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
EPP-MVSNet | | | 83.40 63 | 83.02 61 | 84.57 77 | 90.13 76 | 64.47 178 | 92.32 16 | 90.73 102 | 74.45 91 | 79.35 91 | 91.10 74 | 69.05 55 | 95.12 57 | 72.78 122 | 87.22 109 | 94.13 20 |
|
NCCC | | | 88.06 7 | 88.01 10 | 88.24 5 | 94.41 13 | 73.62 7 | 91.22 30 | 92.83 35 | 81.50 7 | 85.79 22 | 93.47 33 | 73.02 25 | 97.00 6 | 84.90 17 | 94.94 25 | 94.10 21 |
|
ACMMP_Plus | | | 88.05 9 | 88.08 9 | 87.94 11 | 93.70 24 | 73.05 18 | 90.86 33 | 93.59 9 | 76.27 66 | 88.14 8 | 95.09 3 | 71.06 37 | 96.67 13 | 87.67 6 | 96.37 3 | 94.09 22 |
|
XVS | | | 87.18 21 | 86.91 23 | 88.00 9 | 94.42 11 | 73.33 16 | 92.78 9 | 92.99 28 | 79.14 21 | 83.67 51 | 94.17 19 | 67.45 65 | 96.60 18 | 83.06 36 | 94.50 34 | 94.07 23 |
|
X-MVStestdata | | | 80.37 117 | 77.83 151 | 88.00 9 | 94.42 11 | 73.33 16 | 92.78 9 | 92.99 28 | 79.14 21 | 83.67 51 | 12.47 346 | 67.45 65 | 96.60 18 | 83.06 36 | 94.50 34 | 94.07 23 |
|
region2R | | | 87.42 17 | 87.20 18 | 88.09 6 | 94.63 5 | 73.55 9 | 93.03 7 | 93.12 22 | 76.73 55 | 84.45 39 | 94.52 7 | 69.09 54 | 96.70 12 | 84.37 26 | 94.83 29 | 94.03 25 |
|
Regformer-4 | | | 85.68 43 | 85.45 41 | 86.35 45 | 88.95 115 | 69.67 71 | 88.29 101 | 91.29 91 | 81.73 5 | 85.36 24 | 90.01 96 | 72.62 28 | 95.35 53 | 83.28 34 | 87.57 102 | 94.03 25 |
|
Regformer-2 | | | 86.63 29 | 86.53 27 | 86.95 36 | 89.33 100 | 71.24 45 | 88.43 92 | 92.05 59 | 82.50 1 | 86.88 15 | 90.09 94 | 74.45 13 | 95.61 39 | 84.38 25 | 90.63 69 | 94.01 27 |
|
ACMMPR | | | 87.44 15 | 87.23 17 | 88.08 7 | 94.64 4 | 73.59 8 | 93.04 5 | 93.20 19 | 76.78 52 | 84.66 36 | 94.52 7 | 68.81 56 | 96.65 14 | 84.53 23 | 94.90 26 | 94.00 28 |
|
Regformer-1 | | | 86.41 33 | 86.33 28 | 86.64 41 | 89.33 100 | 70.93 51 | 88.43 92 | 91.39 89 | 82.14 3 | 86.65 16 | 90.09 94 | 74.39 16 | 95.01 64 | 83.97 30 | 90.63 69 | 93.97 29 |
|
test_prior3 | | | 86.73 26 | 86.86 25 | 86.33 46 | 92.61 47 | 69.59 72 | 88.85 79 | 92.97 31 | 75.41 76 | 84.91 30 | 93.54 29 | 74.28 18 | 95.48 43 | 83.31 32 | 95.86 7 | 93.91 30 |
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test_prior | | | | | 86.33 46 | 92.61 47 | 69.59 72 | | 92.97 31 | | | | | 95.48 43 | | | 93.91 30 |
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Regformer-3 | | | 85.23 49 | 85.07 47 | 85.70 56 | 88.95 115 | 69.01 82 | 88.29 101 | 89.91 136 | 80.95 9 | 85.01 27 | 90.01 96 | 72.45 29 | 94.19 90 | 82.50 43 | 87.57 102 | 93.90 32 |
|
LFMVS | | | 81.82 83 | 81.23 82 | 83.57 109 | 91.89 57 | 63.43 198 | 89.84 52 | 81.85 266 | 77.04 47 | 83.21 54 | 93.10 39 | 52.26 216 | 93.43 132 | 71.98 134 | 89.95 77 | 93.85 33 |
|
Effi-MVS+ | | | 83.62 59 | 83.08 59 | 85.24 62 | 88.38 136 | 67.45 114 | 88.89 77 | 89.15 157 | 75.50 75 | 82.27 65 | 88.28 135 | 69.61 50 | 94.45 81 | 77.81 72 | 87.84 100 | 93.84 34 |
|
MVS_Test | | | 83.15 65 | 83.06 60 | 83.41 114 | 86.86 177 | 63.21 203 | 86.11 173 | 92.00 63 | 74.31 92 | 82.87 59 | 89.44 111 | 70.03 45 | 93.21 137 | 77.39 77 | 88.50 96 | 93.81 35 |
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test_part1 | | | | | | | | | 94.09 2 | | | | 81.79 1 | | | 96.38 2 | 93.74 36 |
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HFP-MVS | | | 87.58 13 | 87.47 14 | 87.94 11 | 94.58 6 | 73.54 11 | 93.04 5 | 93.24 17 | 76.78 52 | 84.91 30 | 94.44 12 | 70.78 39 | 96.61 16 | 84.53 23 | 94.89 27 | 93.66 37 |
|
#test# | | | 87.33 19 | 87.13 19 | 87.94 11 | 94.58 6 | 73.54 11 | 92.34 15 | 93.24 17 | 75.23 80 | 84.91 30 | 94.44 12 | 70.78 39 | 96.61 16 | 83.75 31 | 94.89 27 | 93.66 37 |
|
VNet | | | 82.21 76 | 82.41 67 | 81.62 177 | 90.82 69 | 60.93 224 | 84.47 214 | 89.78 138 | 76.36 64 | 84.07 46 | 91.88 59 | 64.71 86 | 90.26 224 | 70.68 141 | 88.89 85 | 93.66 37 |
|
PGM-MVS | | | 86.68 27 | 86.27 30 | 87.90 15 | 94.22 18 | 73.38 15 | 90.22 48 | 93.04 23 | 75.53 74 | 83.86 47 | 94.42 14 | 67.87 62 | 96.64 15 | 82.70 41 | 94.57 33 | 93.66 37 |
|
DELS-MVS | | | 85.41 47 | 85.30 45 | 85.77 55 | 88.49 131 | 67.93 108 | 85.52 196 | 93.44 13 | 78.70 28 | 83.63 53 | 89.03 117 | 74.57 12 | 95.71 38 | 80.26 56 | 94.04 44 | 93.66 37 |
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 |
SD-MVS | | | 88.06 7 | 88.50 7 | 86.71 40 | 92.60 49 | 72.71 24 | 91.81 23 | 93.19 20 | 77.87 32 | 90.32 4 | 94.00 25 | 74.83 11 | 93.78 112 | 87.63 7 | 94.27 41 | 93.65 42 |
|
DeepC-MVS | | 79.81 2 | 87.08 24 | 86.88 24 | 87.69 24 | 91.16 63 | 72.32 36 | 90.31 45 | 93.94 5 | 77.12 44 | 82.82 60 | 94.23 18 | 72.13 32 | 97.09 4 | 84.83 20 | 95.37 17 | 93.65 42 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MP-MVS | | | 87.71 11 | 87.64 12 | 87.93 14 | 94.36 15 | 73.88 4 | 92.71 13 | 92.65 42 | 77.57 35 | 83.84 48 | 94.40 15 | 72.24 31 | 96.28 25 | 85.65 13 | 95.30 22 | 93.62 44 |
|
HPM-MVS_fast | | | 85.35 48 | 84.95 49 | 86.57 44 | 93.69 25 | 70.58 57 | 92.15 19 | 91.62 80 | 73.89 100 | 82.67 63 | 94.09 23 | 62.60 121 | 95.54 42 | 80.93 49 | 92.93 49 | 93.57 45 |
|
CSCG | | | 86.41 33 | 86.19 32 | 87.07 35 | 92.91 41 | 72.48 31 | 90.81 34 | 93.56 10 | 73.95 97 | 83.16 56 | 91.07 76 | 75.94 7 | 95.19 55 | 79.94 58 | 94.38 38 | 93.55 46 |
|
test12 | | | | | 86.80 38 | 92.63 46 | 70.70 56 | | 91.79 74 | | 82.71 62 | | 71.67 33 | 96.16 29 | | 94.50 34 | 93.54 47 |
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APD-MVS_3200maxsize | | | 85.97 38 | 85.88 36 | 86.22 49 | 92.69 45 | 69.53 74 | 91.93 21 | 92.99 28 | 73.54 111 | 85.94 18 | 94.51 10 | 65.80 79 | 95.61 39 | 83.04 38 | 92.51 54 | 93.53 48 |
|
mvs_anonymous | | | 79.42 140 | 79.11 125 | 80.34 199 | 84.45 208 | 57.97 249 | 82.59 244 | 87.62 198 | 67.40 216 | 76.17 158 | 88.56 128 | 68.47 57 | 89.59 234 | 70.65 142 | 86.05 123 | 93.47 49 |
|
mPP-MVS | | | 86.67 28 | 86.32 29 | 87.72 22 | 94.41 13 | 73.55 9 | 92.74 11 | 92.22 53 | 76.87 50 | 82.81 61 | 94.25 17 | 66.44 72 | 96.24 26 | 82.88 40 | 94.28 40 | 93.38 50 |
|
EPNet | | | 83.72 57 | 82.92 63 | 86.14 51 | 84.22 212 | 69.48 75 | 91.05 32 | 85.27 224 | 81.30 8 | 76.83 140 | 91.65 62 | 66.09 75 | 95.56 41 | 76.00 89 | 93.85 45 | 93.38 50 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
Vis-MVSNet | | | 83.46 61 | 82.80 65 | 85.43 58 | 90.25 75 | 68.74 90 | 90.30 46 | 90.13 127 | 76.33 65 | 80.87 82 | 92.89 45 | 61.00 150 | 94.20 89 | 72.45 128 | 90.97 65 | 93.35 52 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
HSP-MVS | | | 89.28 1 | 89.76 1 | 87.85 18 | 94.28 16 | 73.46 14 | 92.90 8 | 92.73 39 | 80.27 13 | 91.35 2 | 94.16 20 | 78.35 3 | 96.77 9 | 89.59 1 | 94.22 43 | 93.33 53 |
|
EI-MVSNet-Vis-set | | | 84.19 53 | 83.81 53 | 85.31 59 | 88.18 140 | 67.85 109 | 87.66 116 | 89.73 140 | 80.05 17 | 82.95 57 | 89.59 103 | 70.74 41 | 94.82 72 | 80.66 53 | 84.72 133 | 93.28 54 |
|
MPTG | | | 87.53 14 | 87.41 15 | 87.90 15 | 94.18 20 | 74.25 2 | 90.23 47 | 92.02 60 | 79.45 19 | 85.88 19 | 94.80 4 | 68.07 58 | 96.21 27 | 86.69 10 | 95.34 18 | 93.23 55 |
|
MTAPA | | | 87.23 20 | 87.00 20 | 87.90 15 | 94.18 20 | 74.25 2 | 86.58 160 | 92.02 60 | 79.45 19 | 85.88 19 | 94.80 4 | 68.07 58 | 96.21 27 | 86.69 10 | 95.34 18 | 93.23 55 |
|
CP-MVS | | | 87.11 22 | 86.92 22 | 87.68 25 | 94.20 19 | 73.86 5 | 93.98 1 | 92.82 37 | 76.62 57 | 83.68 50 | 94.46 11 | 67.93 60 | 95.95 35 | 84.20 29 | 94.39 37 | 93.23 55 |
|
ACMMP | | | 85.89 40 | 85.39 42 | 87.38 29 | 93.59 28 | 72.63 27 | 92.74 11 | 93.18 21 | 76.78 52 | 80.73 83 | 93.82 28 | 64.33 88 | 96.29 24 | 82.67 42 | 90.69 68 | 93.23 55 |
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 |
PAPM_NR | | | 83.02 68 | 82.41 67 | 84.82 74 | 92.47 50 | 66.37 131 | 87.93 112 | 91.80 73 | 73.82 105 | 77.32 132 | 90.66 85 | 67.90 61 | 94.90 69 | 70.37 144 | 89.48 81 | 93.19 59 |
|
OMC-MVS | | | 82.69 71 | 81.97 76 | 84.85 73 | 88.75 125 | 67.42 115 | 87.98 108 | 90.87 100 | 74.92 86 | 79.72 87 | 91.65 62 | 62.19 132 | 93.96 98 | 75.26 101 | 86.42 119 | 93.16 60 |
|
PAPR | | | 81.66 86 | 80.89 87 | 83.99 99 | 90.27 74 | 64.00 188 | 86.76 156 | 91.77 77 | 68.84 194 | 77.13 139 | 89.50 104 | 67.63 63 | 94.88 70 | 67.55 162 | 88.52 95 | 93.09 61 |
|
UA-Net | | | 85.08 52 | 84.96 48 | 85.45 57 | 92.07 54 | 68.07 106 | 89.78 56 | 90.86 101 | 82.48 2 | 84.60 38 | 93.20 37 | 69.35 52 | 95.22 54 | 71.39 140 | 90.88 67 | 93.07 62 |
|
HPM-MVS++ | | | 89.02 3 | 89.15 3 | 88.63 1 | 95.01 2 | 76.03 1 | 92.38 14 | 92.85 34 | 80.26 14 | 87.78 12 | 94.27 16 | 75.89 8 | 96.81 8 | 87.45 9 | 96.44 1 | 93.05 63 |
|
train_agg | | | 86.43 31 | 86.20 31 | 87.13 33 | 93.26 34 | 72.96 19 | 88.75 84 | 91.89 69 | 68.69 196 | 85.00 28 | 93.10 39 | 74.43 14 | 95.41 48 | 84.97 15 | 95.71 11 | 93.02 64 |
|
agg_prior3 | | | 86.16 37 | 85.85 38 | 87.10 34 | 93.31 31 | 72.86 23 | 88.77 82 | 91.68 79 | 68.29 206 | 84.26 43 | 92.83 47 | 72.83 26 | 95.42 47 | 84.97 15 | 95.71 11 | 93.02 64 |
|
agg_prior1 | | | 86.22 36 | 86.09 35 | 86.62 42 | 92.85 42 | 71.94 40 | 88.59 89 | 91.78 75 | 68.96 193 | 84.41 40 | 93.18 38 | 74.94 10 | 94.93 65 | 84.75 22 | 95.33 20 | 93.01 66 |
|
EI-MVSNet-UG-set | | | 83.81 55 | 83.38 56 | 85.09 66 | 87.87 147 | 67.53 113 | 87.44 127 | 89.66 141 | 79.74 18 | 82.23 66 | 89.41 112 | 70.24 44 | 94.74 74 | 79.95 57 | 83.92 139 | 92.99 67 |
|
diffmvs | | | 79.51 135 | 78.59 133 | 82.25 159 | 83.31 244 | 62.66 212 | 84.17 224 | 88.11 187 | 67.64 209 | 76.09 159 | 87.47 156 | 64.01 91 | 91.15 208 | 71.71 137 | 84.82 132 | 92.94 68 |
|
test9_res | | | | | | | | | | | | | | | 84.90 17 | 95.70 13 | 92.87 69 |
|
agg_prior2 | | | | | | | | | | | | | | | 82.91 39 | 95.45 15 | 92.70 70 |
|
APD-MVS | | | 87.44 15 | 87.52 13 | 87.19 31 | 94.24 17 | 72.39 33 | 91.86 22 | 92.83 35 | 73.01 125 | 88.58 7 | 94.52 7 | 73.36 22 | 96.49 21 | 84.26 27 | 95.01 24 | 92.70 70 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
Vis-MVSNet (Re-imp) | | | 78.36 157 | 78.45 137 | 78.07 242 | 88.64 127 | 51.78 307 | 86.70 157 | 79.63 287 | 74.14 95 | 75.11 182 | 90.83 83 | 61.29 144 | 89.75 231 | 58.10 239 | 91.60 58 | 92.69 72 |
|
TSAR-MVS + GP. | | | 85.71 42 | 85.33 43 | 86.84 37 | 91.34 61 | 72.50 30 | 89.07 74 | 87.28 204 | 76.41 59 | 85.80 21 | 90.22 92 | 74.15 20 | 95.37 52 | 81.82 45 | 91.88 56 | 92.65 73 |
|
Test4 | | | 77.83 172 | 75.90 189 | 83.62 106 | 80.24 291 | 65.25 153 | 85.27 198 | 90.67 103 | 69.03 191 | 66.48 281 | 83.75 245 | 43.07 290 | 93.00 151 | 75.93 90 | 88.66 90 | 92.62 74 |
|
test_normal | | | 79.81 130 | 78.45 137 | 83.89 103 | 82.70 261 | 65.40 147 | 85.82 182 | 89.48 146 | 69.39 179 | 70.12 240 | 85.66 217 | 57.15 181 | 93.71 121 | 77.08 80 | 88.62 91 | 92.56 75 |
|
nrg030 | | | 83.88 54 | 83.53 54 | 84.96 69 | 86.77 180 | 69.28 79 | 90.46 42 | 92.67 40 | 74.79 87 | 82.95 57 | 91.33 72 | 72.70 27 | 93.09 146 | 80.79 52 | 79.28 203 | 92.50 76 |
|
MG-MVS | | | 83.41 62 | 83.45 55 | 83.28 117 | 92.74 44 | 62.28 217 | 88.17 105 | 89.50 145 | 75.22 81 | 81.49 73 | 92.74 51 | 66.75 69 | 95.11 58 | 72.85 121 | 91.58 59 | 92.45 77 |
|
FIs | | | 82.07 78 | 82.42 66 | 81.04 190 | 88.80 122 | 58.34 243 | 88.26 103 | 93.49 12 | 76.93 49 | 78.47 103 | 91.04 77 | 69.92 47 | 92.34 170 | 69.87 148 | 84.97 129 | 92.44 78 |
|
DI_MVS_plusplus_test | | | 79.89 129 | 78.58 134 | 83.85 104 | 82.89 257 | 65.32 151 | 86.12 172 | 89.55 143 | 69.64 178 | 70.55 231 | 85.82 214 | 57.24 179 | 93.81 110 | 76.85 83 | 88.55 93 | 92.41 79 |
|
FC-MVSNet-test | | | 81.52 88 | 82.02 74 | 80.03 205 | 88.42 135 | 55.97 280 | 87.95 110 | 93.42 14 | 77.10 45 | 77.38 130 | 90.98 82 | 69.96 46 | 91.79 183 | 68.46 158 | 84.50 134 | 92.33 80 |
|
Fast-Effi-MVS+ | | | 80.81 100 | 79.92 99 | 83.47 110 | 88.85 117 | 64.51 172 | 85.53 194 | 89.39 148 | 70.79 159 | 78.49 102 | 85.06 230 | 67.54 64 | 93.58 123 | 67.03 170 | 86.58 116 | 92.32 81 |
|
TranMVSNet+NR-MVSNet | | | 80.84 97 | 80.31 94 | 82.42 156 | 87.85 148 | 62.33 215 | 87.74 115 | 91.33 90 | 80.55 12 | 77.99 121 | 89.86 98 | 65.23 82 | 92.62 160 | 67.05 169 | 75.24 254 | 92.30 82 |
|
ab-mvs | | | 79.51 135 | 78.97 128 | 81.14 188 | 88.46 133 | 60.91 225 | 83.84 230 | 89.24 155 | 70.36 166 | 79.03 93 | 88.87 119 | 63.23 100 | 90.21 226 | 65.12 182 | 82.57 164 | 92.28 83 |
|
CANet_DTU | | | 80.61 107 | 79.87 100 | 82.83 144 | 85.60 192 | 63.17 206 | 87.36 128 | 88.65 180 | 76.37 63 | 75.88 160 | 88.44 131 | 53.51 207 | 93.07 147 | 73.30 118 | 89.74 79 | 92.25 84 |
|
UniMVSNet_NR-MVSNet | | | 81.88 81 | 81.54 79 | 82.92 137 | 88.46 133 | 63.46 196 | 87.13 140 | 92.37 49 | 80.19 15 | 78.38 107 | 89.14 114 | 71.66 34 | 93.05 148 | 70.05 145 | 76.46 236 | 92.25 84 |
|
DU-MVS | | | 81.12 93 | 80.52 92 | 82.90 138 | 87.80 155 | 63.46 196 | 87.02 145 | 91.87 71 | 79.01 26 | 78.38 107 | 89.07 115 | 65.02 84 | 93.05 148 | 70.05 145 | 76.46 236 | 92.20 86 |
|
NR-MVSNet | | | 80.23 120 | 79.38 115 | 82.78 149 | 87.80 155 | 63.34 199 | 86.31 168 | 91.09 97 | 79.01 26 | 72.17 214 | 89.07 115 | 67.20 67 | 92.81 158 | 66.08 176 | 75.65 245 | 92.20 86 |
|
TAPA-MVS | | 73.13 9 | 79.15 144 | 77.94 149 | 82.79 148 | 89.59 91 | 62.99 210 | 88.16 106 | 91.51 85 | 65.77 229 | 77.14 138 | 91.09 75 | 60.91 151 | 93.21 137 | 50.26 275 | 87.05 111 | 92.17 88 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
3Dnovator | | 76.31 5 | 83.38 64 | 82.31 70 | 86.59 43 | 87.94 146 | 72.94 22 | 90.64 37 | 92.14 57 | 77.21 42 | 75.47 168 | 92.83 47 | 58.56 168 | 94.72 75 | 73.24 119 | 92.71 52 | 92.13 89 |
|
MVS_111021_HR | | | 85.14 51 | 84.75 51 | 86.32 48 | 91.65 59 | 72.70 25 | 85.98 175 | 90.33 118 | 76.11 68 | 82.08 67 | 91.61 65 | 71.36 36 | 94.17 92 | 81.02 48 | 92.58 53 | 92.08 90 |
|
MVSFormer | | | 82.85 70 | 82.05 73 | 85.24 62 | 87.35 167 | 70.21 60 | 90.50 40 | 90.38 113 | 68.55 198 | 81.32 74 | 89.47 106 | 61.68 135 | 93.46 128 | 78.98 61 | 90.26 72 | 92.05 91 |
|
jason | | | 81.39 91 | 80.29 95 | 84.70 76 | 86.63 181 | 69.90 67 | 85.95 176 | 86.77 208 | 63.24 250 | 81.07 80 | 89.47 106 | 61.08 149 | 92.15 174 | 78.33 68 | 90.07 76 | 92.05 91 |
jason: jason. |
HyFIR lowres test | | | 77.53 179 | 75.40 197 | 83.94 102 | 89.59 91 | 66.62 127 | 80.36 262 | 88.64 181 | 56.29 304 | 76.45 146 | 85.17 227 | 57.64 174 | 93.28 135 | 61.34 213 | 83.10 157 | 91.91 93 |
|
mvs-test1 | | | 80.88 95 | 79.40 114 | 85.29 60 | 85.13 199 | 69.75 70 | 89.28 65 | 88.10 189 | 74.99 84 | 76.44 149 | 86.72 176 | 57.27 177 | 94.26 88 | 73.53 115 | 83.18 156 | 91.87 94 |
|
XVG-OURS-SEG-HR | | | 80.81 100 | 79.76 103 | 83.96 101 | 85.60 192 | 68.78 87 | 83.54 235 | 90.50 110 | 70.66 163 | 76.71 142 | 91.66 61 | 60.69 154 | 91.26 205 | 76.94 82 | 81.58 173 | 91.83 95 |
|
lupinMVS | | | 81.39 91 | 80.27 96 | 84.76 75 | 87.35 167 | 70.21 60 | 85.55 192 | 86.41 212 | 62.85 256 | 81.32 74 | 88.61 125 | 61.68 135 | 92.24 173 | 78.41 67 | 90.26 72 | 91.83 95 |
|
abl_6 | | | 85.23 49 | 84.95 49 | 86.07 52 | 92.23 52 | 70.48 58 | 90.80 35 | 92.08 58 | 73.51 112 | 85.26 25 | 94.16 20 | 62.75 114 | 95.92 36 | 82.46 44 | 91.30 63 | 91.81 97 |
|
WR-MVS | | | 79.49 137 | 79.22 124 | 80.27 202 | 88.79 123 | 58.35 242 | 85.06 202 | 88.61 182 | 78.56 29 | 77.65 126 | 88.34 133 | 63.81 94 | 90.66 221 | 64.98 185 | 77.22 219 | 91.80 98 |
|
UniMVSNet (Re) | | | 81.60 87 | 81.11 84 | 83.09 125 | 88.38 136 | 64.41 180 | 87.60 117 | 93.02 25 | 78.42 31 | 78.56 100 | 88.16 137 | 69.78 48 | 93.26 136 | 69.58 150 | 76.49 235 | 91.60 99 |
|
UGNet | | | 80.83 99 | 79.59 107 | 84.54 78 | 88.04 143 | 68.09 105 | 89.42 63 | 88.16 186 | 76.95 48 | 76.22 154 | 89.46 108 | 49.30 259 | 93.94 100 | 68.48 157 | 90.31 71 | 91.60 99 |
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 |
XVG-OURS | | | 80.41 112 | 79.23 123 | 83.97 100 | 85.64 191 | 69.02 81 | 83.03 243 | 90.39 112 | 71.09 156 | 77.63 127 | 91.49 69 | 54.62 199 | 91.35 203 | 75.71 94 | 83.47 148 | 91.54 101 |
|
LCM-MVSNet-Re | | | 77.05 190 | 76.94 166 | 77.36 251 | 87.20 173 | 51.60 308 | 80.06 264 | 80.46 278 | 75.20 82 | 67.69 269 | 86.72 176 | 62.48 126 | 88.98 251 | 63.44 192 | 89.25 83 | 91.51 102 |
|
DP-MVS Recon | | | 83.11 67 | 82.09 72 | 86.15 50 | 94.44 10 | 70.92 52 | 88.79 81 | 92.20 54 | 70.53 164 | 79.17 92 | 91.03 79 | 64.12 90 | 96.03 31 | 68.39 159 | 90.14 74 | 91.50 103 |
|
pcd1.5k->3k | | | 34.07 320 | 35.26 320 | 30.50 335 | 86.92 176 | 0.00 356 | 0.00 346 | 91.58 82 | 0.00 350 | 0.00 352 | 0.00 352 | 56.23 185 | 0.00 353 | 0.00 350 | 82.60 163 | 91.49 104 |
|
PS-MVSNAJss | | | 82.07 78 | 81.31 80 | 84.34 86 | 86.51 182 | 67.27 119 | 89.27 66 | 91.51 85 | 71.75 146 | 79.37 90 | 90.22 92 | 63.15 102 | 94.27 85 | 77.69 73 | 82.36 166 | 91.49 104 |
|
HQP_MVS | | | 83.64 58 | 83.14 58 | 85.14 64 | 90.08 79 | 68.71 92 | 91.25 28 | 92.44 45 | 79.12 23 | 78.92 95 | 91.00 80 | 60.42 159 | 95.38 50 | 78.71 63 | 86.32 120 | 91.33 106 |
|
plane_prior5 | | | | | | | | | 92.44 45 | | | | | 95.38 50 | 78.71 63 | 86.32 120 | 91.33 106 |
|
GA-MVS | | | 76.87 193 | 75.17 205 | 81.97 164 | 82.75 259 | 62.58 213 | 81.44 256 | 86.35 215 | 72.16 144 | 74.74 187 | 82.89 252 | 46.20 274 | 92.02 176 | 68.85 155 | 81.09 177 | 91.30 108 |
|
VPA-MVSNet | | | 80.60 108 | 80.55 91 | 80.76 194 | 88.07 142 | 60.80 227 | 86.86 150 | 91.58 82 | 75.67 73 | 80.24 85 | 89.45 110 | 63.34 96 | 90.25 225 | 70.51 143 | 79.22 204 | 91.23 109 |
|
Effi-MVS+-dtu | | | 80.03 126 | 78.57 135 | 84.42 82 | 85.13 199 | 68.74 90 | 88.77 82 | 88.10 189 | 74.99 84 | 74.97 185 | 83.49 249 | 57.27 177 | 93.36 133 | 73.53 115 | 80.88 179 | 91.18 110 |
|
v2v482 | | | 80.23 120 | 79.29 121 | 83.05 128 | 83.62 237 | 64.14 183 | 87.04 144 | 89.97 133 | 73.61 108 | 78.18 117 | 87.22 163 | 61.10 148 | 93.82 109 | 76.11 87 | 76.78 233 | 91.18 110 |
|
HQP4-MVS | | | | | | | | | | | 77.24 134 | | | 95.11 58 | | | 91.03 112 |
|
HQP-MVS | | | 82.61 73 | 82.02 74 | 84.37 83 | 89.33 100 | 66.98 123 | 89.17 68 | 92.19 55 | 76.41 59 | 77.23 135 | 90.23 91 | 60.17 162 | 95.11 58 | 77.47 75 | 85.99 124 | 91.03 112 |
|
RPSCF | | | 73.23 243 | 71.46 242 | 78.54 235 | 82.50 265 | 59.85 232 | 82.18 247 | 82.84 252 | 58.96 285 | 71.15 228 | 89.41 112 | 45.48 281 | 84.77 286 | 58.82 232 | 71.83 280 | 91.02 114 |
|
test_djsdf | | | 80.30 118 | 79.32 117 | 83.27 118 | 83.98 229 | 65.37 150 | 90.50 40 | 90.38 113 | 68.55 198 | 76.19 155 | 88.70 121 | 56.44 184 | 93.46 128 | 78.98 61 | 80.14 192 | 90.97 115 |
|
PCF-MVS | | 73.52 7 | 80.38 116 | 78.84 129 | 85.01 68 | 87.71 160 | 68.99 83 | 83.65 232 | 91.46 88 | 63.00 253 | 77.77 125 | 90.28 89 | 66.10 74 | 95.09 62 | 61.40 211 | 88.22 99 | 90.94 116 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
VPNet | | | 78.69 152 | 78.66 131 | 78.76 231 | 88.31 138 | 55.72 286 | 84.45 217 | 86.63 210 | 76.79 51 | 78.26 114 | 90.55 87 | 59.30 164 | 89.70 233 | 66.63 171 | 77.05 221 | 90.88 117 |
|
CPTT-MVS | | | 83.73 56 | 83.33 57 | 84.92 72 | 93.28 33 | 70.86 53 | 92.09 20 | 90.38 113 | 68.75 195 | 79.57 88 | 92.83 47 | 60.60 157 | 93.04 150 | 80.92 50 | 91.56 60 | 90.86 118 |
|
CLD-MVS | | | 82.31 75 | 81.65 78 | 84.29 87 | 88.47 132 | 67.73 112 | 85.81 183 | 92.35 50 | 75.78 70 | 78.33 109 | 86.58 189 | 64.01 91 | 94.35 82 | 76.05 88 | 87.48 107 | 90.79 119 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
v1192 | | | 79.59 134 | 78.43 140 | 83.07 127 | 83.55 239 | 64.52 170 | 86.93 148 | 90.58 107 | 70.83 158 | 77.78 124 | 85.90 210 | 59.15 165 | 93.94 100 | 73.96 110 | 77.19 220 | 90.76 120 |
|
IterMVS-LS | | | 80.06 125 | 79.38 115 | 82.11 161 | 85.89 187 | 63.20 204 | 86.79 153 | 89.34 149 | 74.19 93 | 75.45 170 | 86.72 176 | 66.62 70 | 92.39 167 | 72.58 126 | 76.86 227 | 90.75 121 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
EI-MVSNet | | | 80.52 111 | 79.98 98 | 82.12 160 | 84.28 209 | 63.19 205 | 86.41 165 | 88.95 169 | 74.18 94 | 78.69 97 | 87.54 154 | 66.62 70 | 92.43 165 | 72.57 127 | 80.57 185 | 90.74 122 |
|
v1921920 | | | 79.22 143 | 78.03 147 | 82.80 146 | 83.30 245 | 63.94 190 | 86.80 152 | 90.33 118 | 69.91 172 | 77.48 129 | 85.53 221 | 58.44 169 | 93.75 116 | 73.60 114 | 76.85 228 | 90.71 123 |
|
v1141 | | | 80.19 122 | 79.31 118 | 82.85 141 | 83.84 232 | 64.12 185 | 87.14 137 | 90.08 129 | 73.13 120 | 78.27 111 | 86.39 195 | 62.67 119 | 93.75 116 | 75.40 99 | 76.83 230 | 90.68 124 |
|
divwei89l23v2f112 | | | 80.19 122 | 79.31 118 | 82.85 141 | 83.84 232 | 64.11 187 | 87.13 140 | 90.08 129 | 73.13 120 | 78.27 111 | 86.39 195 | 62.69 117 | 93.75 116 | 75.40 99 | 76.82 231 | 90.68 124 |
|
v1 | | | 80.19 122 | 79.31 118 | 82.85 141 | 83.83 234 | 64.12 185 | 87.14 137 | 90.07 131 | 73.13 120 | 78.27 111 | 86.38 199 | 62.72 116 | 93.75 116 | 75.41 98 | 76.82 231 | 90.68 124 |
|
QAPM | | | 80.88 95 | 79.50 112 | 85.03 67 | 88.01 145 | 68.97 84 | 91.59 24 | 92.00 63 | 66.63 221 | 75.15 181 | 92.16 52 | 57.70 173 | 95.45 45 | 63.52 191 | 88.76 88 | 90.66 127 |
|
v144192 | | | 79.47 138 | 78.37 141 | 82.78 149 | 83.35 242 | 63.96 189 | 86.96 146 | 90.36 116 | 69.99 171 | 77.50 128 | 85.67 216 | 60.66 155 | 93.77 114 | 74.27 107 | 76.58 234 | 90.62 128 |
|
v1240 | | | 78.99 148 | 77.78 153 | 82.64 153 | 83.21 246 | 63.54 193 | 86.62 159 | 90.30 120 | 69.74 177 | 77.33 131 | 85.68 215 | 57.04 182 | 93.76 115 | 73.13 120 | 76.92 225 | 90.62 128 |
|
v1144 | | | 80.03 126 | 79.03 126 | 83.01 130 | 83.78 235 | 64.51 172 | 87.11 142 | 90.57 108 | 71.96 145 | 78.08 120 | 86.20 203 | 61.41 141 | 93.94 100 | 74.93 103 | 77.23 218 | 90.60 130 |
|
testing_2 | | | 75.73 214 | 73.34 222 | 82.89 140 | 77.37 309 | 65.22 154 | 84.10 227 | 90.54 109 | 69.09 187 | 60.46 305 | 81.15 277 | 40.48 303 | 92.84 157 | 76.36 86 | 80.54 187 | 90.60 130 |
|
1112_ss | | | 77.40 188 | 76.43 173 | 80.32 200 | 89.11 114 | 60.41 230 | 83.65 232 | 87.72 197 | 62.13 264 | 73.05 199 | 86.72 176 | 62.58 123 | 89.97 228 | 62.11 205 | 80.80 181 | 90.59 132 |
|
v1neww | | | 80.40 113 | 79.54 108 | 82.98 132 | 84.10 220 | 64.51 172 | 87.57 119 | 90.22 122 | 73.25 117 | 78.47 103 | 86.65 184 | 62.83 110 | 93.86 106 | 75.72 92 | 77.02 222 | 90.58 133 |
|
v7new | | | 80.40 113 | 79.54 108 | 82.98 132 | 84.10 220 | 64.51 172 | 87.57 119 | 90.22 122 | 73.25 117 | 78.47 103 | 86.65 184 | 62.83 110 | 93.86 106 | 75.72 92 | 77.02 222 | 90.58 133 |
|
v6 | | | 80.40 113 | 79.54 108 | 82.98 132 | 84.09 222 | 64.50 176 | 87.57 119 | 90.22 122 | 73.25 117 | 78.47 103 | 86.63 186 | 62.84 109 | 93.86 106 | 75.73 91 | 77.02 222 | 90.58 133 |
|
CP-MVSNet | | | 78.22 158 | 78.34 142 | 77.84 244 | 87.83 153 | 54.54 291 | 87.94 111 | 91.17 95 | 77.65 33 | 73.48 194 | 88.49 129 | 62.24 131 | 88.43 259 | 62.19 202 | 74.07 262 | 90.55 136 |
|
PS-CasMVS | | | 78.01 166 | 78.09 146 | 77.77 246 | 87.71 160 | 54.39 293 | 88.02 107 | 91.22 92 | 77.50 40 | 73.26 196 | 88.64 124 | 60.73 152 | 88.41 260 | 61.88 206 | 73.88 266 | 90.53 137 |
|
v7 | | | 80.24 119 | 79.26 122 | 83.15 122 | 84.07 226 | 64.94 161 | 87.56 122 | 90.67 103 | 72.26 141 | 78.28 110 | 86.51 193 | 61.45 140 | 94.03 97 | 75.14 102 | 77.41 216 | 90.49 138 |
|
CDS-MVSNet | | | 79.07 146 | 77.70 155 | 83.17 121 | 87.60 162 | 68.23 103 | 84.40 220 | 86.20 216 | 67.49 214 | 76.36 150 | 86.54 191 | 61.54 138 | 90.79 219 | 61.86 207 | 87.33 108 | 90.49 138 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
TAMVS | | | 78.89 150 | 77.51 158 | 83.03 129 | 87.80 155 | 67.79 111 | 84.72 207 | 85.05 227 | 67.63 210 | 76.75 141 | 87.70 148 | 62.25 130 | 90.82 218 | 58.53 235 | 87.13 110 | 90.49 138 |
|
PEN-MVS | | | 77.73 173 | 77.69 156 | 77.84 244 | 87.07 175 | 53.91 295 | 87.91 113 | 91.18 94 | 77.56 37 | 73.14 198 | 88.82 120 | 61.23 145 | 89.17 246 | 59.95 221 | 72.37 275 | 90.43 141 |
|
Test_1112_low_res | | | 76.40 202 | 75.44 195 | 79.27 219 | 89.28 106 | 58.09 245 | 81.69 252 | 87.07 206 | 59.53 282 | 72.48 206 | 86.67 182 | 61.30 143 | 89.33 239 | 60.81 217 | 80.15 191 | 90.41 142 |
|
HY-MVS | | 69.67 12 | 77.95 168 | 77.15 163 | 80.36 198 | 87.57 166 | 60.21 231 | 83.37 241 | 87.78 196 | 66.11 225 | 75.37 173 | 87.06 171 | 63.27 98 | 90.48 223 | 61.38 212 | 82.43 165 | 90.40 143 |
|
CHOSEN 1792x2688 | | | 77.63 178 | 75.69 190 | 83.44 111 | 89.98 81 | 68.58 97 | 78.70 278 | 87.50 201 | 56.38 303 | 75.80 162 | 86.84 172 | 58.67 167 | 91.40 202 | 61.58 210 | 85.75 127 | 90.34 144 |
|
114514_t | | | 80.68 106 | 79.51 111 | 84.20 89 | 94.09 22 | 67.27 119 | 89.64 61 | 91.11 96 | 58.75 288 | 74.08 191 | 90.72 84 | 58.10 171 | 95.04 63 | 69.70 149 | 89.42 82 | 90.30 145 |
|
PVSNet_Blended_VisFu | | | 82.62 72 | 81.83 77 | 84.96 69 | 90.80 70 | 69.76 69 | 88.74 86 | 91.70 78 | 69.39 179 | 78.96 94 | 88.46 130 | 65.47 80 | 94.87 71 | 74.42 105 | 88.57 92 | 90.24 146 |
|
MVS_111021_LR | | | 82.61 73 | 82.11 71 | 84.11 91 | 88.82 120 | 71.58 43 | 85.15 201 | 86.16 217 | 74.69 88 | 80.47 84 | 91.04 77 | 62.29 129 | 90.55 222 | 80.33 55 | 90.08 75 | 90.20 147 |
|
MSLP-MVS++ | | | 85.43 46 | 85.76 40 | 84.45 81 | 91.93 56 | 70.24 59 | 90.71 36 | 92.86 33 | 77.46 41 | 84.22 44 | 92.81 50 | 67.16 68 | 92.94 152 | 80.36 54 | 94.35 39 | 90.16 148 |
|
mvs_tets | | | 79.13 145 | 77.77 154 | 83.22 120 | 84.70 204 | 66.37 131 | 89.17 68 | 90.19 125 | 69.38 181 | 75.40 172 | 89.46 108 | 44.17 285 | 93.15 142 | 76.78 85 | 80.70 183 | 90.14 149 |
|
BH-RMVSNet | | | 79.61 133 | 78.44 139 | 83.14 123 | 89.38 99 | 65.93 137 | 84.95 204 | 87.15 205 | 73.56 110 | 78.19 116 | 89.79 99 | 56.67 183 | 93.36 133 | 59.53 226 | 86.74 114 | 90.13 150 |
|
v7n | | | 78.97 149 | 77.58 157 | 83.14 123 | 83.45 241 | 65.51 145 | 88.32 99 | 91.21 93 | 73.69 107 | 72.41 211 | 86.32 200 | 57.93 172 | 93.81 110 | 69.18 153 | 75.65 245 | 90.11 151 |
|
jajsoiax | | | 79.29 142 | 77.96 148 | 83.27 118 | 84.68 205 | 66.57 129 | 89.25 67 | 90.16 126 | 69.20 185 | 75.46 169 | 89.49 105 | 45.75 279 | 93.13 144 | 76.84 84 | 80.80 181 | 90.11 151 |
|
v148 | | | 78.72 151 | 77.80 152 | 81.47 181 | 82.73 260 | 61.96 220 | 86.30 169 | 88.08 191 | 73.26 116 | 76.18 156 | 85.47 223 | 62.46 127 | 92.36 169 | 71.92 136 | 73.82 267 | 90.09 153 |
|
GBi-Net | | | 78.40 155 | 77.40 159 | 81.40 183 | 87.60 162 | 63.01 207 | 88.39 96 | 89.28 151 | 71.63 148 | 75.34 174 | 87.28 159 | 54.80 193 | 91.11 209 | 62.72 196 | 79.57 198 | 90.09 153 |
|
test1 | | | 78.40 155 | 77.40 159 | 81.40 183 | 87.60 162 | 63.01 207 | 88.39 96 | 89.28 151 | 71.63 148 | 75.34 174 | 87.28 159 | 54.80 193 | 91.11 209 | 62.72 196 | 79.57 198 | 90.09 153 |
|
FMVSNet1 | | | 77.44 186 | 76.12 182 | 81.40 183 | 86.81 179 | 63.01 207 | 88.39 96 | 89.28 151 | 70.49 165 | 74.39 190 | 87.28 159 | 49.06 262 | 91.11 209 | 60.91 215 | 78.52 206 | 90.09 153 |
|
WR-MVS_H | | | 78.51 154 | 78.49 136 | 78.56 234 | 88.02 144 | 56.38 275 | 88.43 92 | 92.67 40 | 77.14 43 | 73.89 192 | 87.55 153 | 66.25 73 | 89.24 241 | 58.92 230 | 73.55 269 | 90.06 157 |
|
DTE-MVSNet | | | 76.99 191 | 76.80 168 | 77.54 250 | 86.24 184 | 53.06 304 | 87.52 124 | 90.66 105 | 77.08 46 | 72.50 204 | 88.67 123 | 60.48 158 | 89.52 235 | 57.33 246 | 70.74 286 | 90.05 158 |
|
view600 | | | 76.20 205 | 75.21 201 | 79.16 223 | 89.64 86 | 55.82 281 | 85.74 184 | 82.06 261 | 73.88 101 | 75.74 163 | 87.85 143 | 51.84 227 | 91.66 194 | 46.75 292 | 83.42 149 | 90.00 159 |
|
view800 | | | 76.20 205 | 75.21 201 | 79.16 223 | 89.64 86 | 55.82 281 | 85.74 184 | 82.06 261 | 73.88 101 | 75.74 163 | 87.85 143 | 51.84 227 | 91.66 194 | 46.75 292 | 83.42 149 | 90.00 159 |
|
conf0.05thres1000 | | | 76.20 205 | 75.21 201 | 79.16 223 | 89.64 86 | 55.82 281 | 85.74 184 | 82.06 261 | 73.88 101 | 75.74 163 | 87.85 143 | 51.84 227 | 91.66 194 | 46.75 292 | 83.42 149 | 90.00 159 |
|
tfpn | | | 76.20 205 | 75.21 201 | 79.16 223 | 89.64 86 | 55.82 281 | 85.74 184 | 82.06 261 | 73.88 101 | 75.74 163 | 87.85 143 | 51.84 227 | 91.66 194 | 46.75 292 | 83.42 149 | 90.00 159 |
|
v8 | | | 79.97 128 | 79.02 127 | 82.80 146 | 84.09 222 | 64.50 176 | 87.96 109 | 90.29 121 | 74.13 96 | 75.24 179 | 86.81 173 | 62.88 107 | 93.89 105 | 74.39 106 | 75.40 250 | 90.00 159 |
|
thres600view7 | | | 76.50 198 | 75.44 195 | 79.68 211 | 89.40 98 | 57.16 260 | 85.53 194 | 83.23 245 | 73.79 106 | 76.26 153 | 87.09 169 | 51.89 224 | 91.89 181 | 48.05 288 | 83.72 146 | 90.00 159 |
|
thres400 | | | 76.50 198 | 75.37 198 | 79.86 207 | 89.13 112 | 57.65 255 | 85.17 199 | 83.60 238 | 73.41 114 | 76.45 146 | 86.39 195 | 52.12 218 | 91.95 177 | 48.33 282 | 83.75 142 | 90.00 159 |
|
OPM-MVS | | | 83.50 60 | 82.95 62 | 85.14 64 | 88.79 123 | 70.95 49 | 89.13 73 | 91.52 84 | 77.55 38 | 80.96 81 | 91.75 60 | 60.71 153 | 94.50 80 | 79.67 59 | 86.51 118 | 89.97 166 |
|
v10 | | | 79.74 132 | 78.67 130 | 82.97 136 | 84.06 227 | 64.95 160 | 87.88 114 | 90.62 106 | 73.11 123 | 75.11 182 | 86.56 190 | 61.46 139 | 94.05 96 | 73.68 111 | 75.55 247 | 89.90 167 |
|
MVSTER | | | 79.01 147 | 77.88 150 | 82.38 157 | 83.07 251 | 64.80 164 | 84.08 228 | 88.95 169 | 69.01 192 | 78.69 97 | 87.17 166 | 54.70 197 | 92.43 165 | 74.69 104 | 80.57 185 | 89.89 168 |
|
ACMP | | 74.13 6 | 81.51 90 | 80.57 90 | 84.36 84 | 89.42 97 | 68.69 95 | 89.97 51 | 91.50 87 | 74.46 90 | 75.04 184 | 90.41 88 | 53.82 205 | 94.54 77 | 77.56 74 | 82.91 158 | 89.86 169 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
LPG-MVS_test | | | 82.08 77 | 81.27 81 | 84.50 79 | 89.23 108 | 68.76 88 | 90.22 48 | 91.94 67 | 75.37 78 | 76.64 144 | 91.51 67 | 54.29 200 | 94.91 67 | 78.44 65 | 83.78 140 | 89.83 170 |
|
LGP-MVS_train | | | | | 84.50 79 | 89.23 108 | 68.76 88 | | 91.94 67 | 75.37 78 | 76.64 144 | 91.51 67 | 54.29 200 | 94.91 67 | 78.44 65 | 83.78 140 | 89.83 170 |
|
V42 | | | 79.38 141 | 78.24 145 | 82.83 144 | 81.10 283 | 65.50 146 | 85.55 192 | 89.82 137 | 71.57 151 | 78.21 115 | 86.12 204 | 60.66 155 | 93.18 141 | 75.64 95 | 75.46 249 | 89.81 172 |
|
conf200view11 | | | 76.55 196 | 75.55 193 | 79.57 216 | 89.52 93 | 56.99 263 | 85.83 180 | 83.23 245 | 73.94 98 | 76.32 151 | 87.12 167 | 51.89 224 | 91.95 177 | 48.33 282 | 83.75 142 | 89.78 173 |
|
MAR-MVS | | | 81.84 82 | 80.70 88 | 85.27 61 | 91.32 62 | 71.53 44 | 89.82 53 | 90.92 99 | 69.77 174 | 78.50 101 | 86.21 202 | 62.36 128 | 94.52 79 | 65.36 181 | 92.05 55 | 89.77 174 |
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 |
tpmp4_e23 | | | 73.45 233 | 71.17 247 | 80.31 201 | 83.55 239 | 59.56 235 | 81.88 248 | 82.33 256 | 57.94 293 | 70.51 233 | 81.62 273 | 51.19 237 | 91.63 198 | 53.96 260 | 77.51 215 | 89.75 175 |
|
v748 | | | 77.97 167 | 76.65 171 | 81.92 166 | 82.29 267 | 63.28 201 | 87.53 123 | 90.35 117 | 73.50 113 | 70.76 230 | 85.55 220 | 58.28 170 | 92.81 158 | 68.81 156 | 72.76 274 | 89.67 176 |
|
anonymousdsp | | | 78.60 153 | 77.15 163 | 82.98 132 | 80.51 289 | 67.08 121 | 87.24 135 | 89.53 144 | 65.66 231 | 75.16 180 | 87.19 165 | 52.52 210 | 92.25 172 | 77.17 79 | 79.34 202 | 89.61 177 |
|
FMVSNet2 | | | 78.20 160 | 77.21 162 | 81.20 186 | 87.60 162 | 62.89 211 | 87.47 126 | 89.02 160 | 71.63 148 | 75.29 178 | 87.28 159 | 54.80 193 | 91.10 212 | 62.38 200 | 79.38 201 | 89.61 177 |
|
FMVSNet3 | | | 77.88 171 | 76.85 167 | 80.97 191 | 86.84 178 | 62.36 214 | 86.52 162 | 88.77 176 | 71.13 154 | 75.34 174 | 86.66 183 | 54.07 203 | 91.10 212 | 62.72 196 | 79.57 198 | 89.45 179 |
|
cascas | | | 76.72 195 | 74.64 208 | 82.99 131 | 85.78 189 | 65.88 139 | 82.33 246 | 89.21 156 | 60.85 272 | 72.74 201 | 81.02 279 | 47.28 268 | 93.75 116 | 67.48 163 | 85.02 128 | 89.34 180 |
|
Fast-Effi-MVS+-dtu | | | 78.02 165 | 76.49 172 | 82.62 154 | 83.16 250 | 66.96 125 | 86.94 147 | 87.45 203 | 72.45 136 | 71.49 225 | 84.17 240 | 54.79 196 | 91.58 200 | 67.61 161 | 80.31 189 | 89.30 181 |
|
IB-MVS | | 68.01 15 | 75.85 213 | 73.36 221 | 83.31 116 | 84.76 203 | 66.03 134 | 83.38 236 | 85.06 226 | 70.21 170 | 69.40 250 | 81.05 278 | 45.76 278 | 94.66 76 | 65.10 183 | 75.49 248 | 89.25 182 |
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 |
thres100view900 | | | 76.50 198 | 75.55 193 | 79.33 218 | 89.52 93 | 56.99 263 | 85.83 180 | 83.23 245 | 73.94 98 | 76.32 151 | 87.12 167 | 51.89 224 | 91.95 177 | 48.33 282 | 83.75 142 | 89.07 183 |
|
tfpn200view9 | | | 76.42 201 | 75.37 198 | 79.55 217 | 89.13 112 | 57.65 255 | 85.17 199 | 83.60 238 | 73.41 114 | 76.45 146 | 86.39 195 | 52.12 218 | 91.95 177 | 48.33 282 | 83.75 142 | 89.07 183 |
|
xiu_mvs_v1_base_debu | | | 80.80 102 | 79.72 104 | 84.03 96 | 87.35 167 | 70.19 62 | 85.56 189 | 88.77 176 | 69.06 188 | 81.83 68 | 88.16 137 | 50.91 239 | 92.85 154 | 78.29 69 | 87.56 104 | 89.06 185 |
|
xiu_mvs_v1_base | | | 80.80 102 | 79.72 104 | 84.03 96 | 87.35 167 | 70.19 62 | 85.56 189 | 88.77 176 | 69.06 188 | 81.83 68 | 88.16 137 | 50.91 239 | 92.85 154 | 78.29 69 | 87.56 104 | 89.06 185 |
|
xiu_mvs_v1_base_debi | | | 80.80 102 | 79.72 104 | 84.03 96 | 87.35 167 | 70.19 62 | 85.56 189 | 88.77 176 | 69.06 188 | 81.83 68 | 88.16 137 | 50.91 239 | 92.85 154 | 78.29 69 | 87.56 104 | 89.06 185 |
|
v52 | | | 77.94 170 | 76.37 175 | 82.67 151 | 79.39 301 | 65.52 143 | 86.43 163 | 89.94 134 | 72.28 139 | 72.15 216 | 84.94 233 | 55.70 188 | 93.44 130 | 73.64 112 | 72.84 273 | 89.06 185 |
|
V4 | | | 77.95 168 | 76.37 175 | 82.67 151 | 79.40 300 | 65.52 143 | 86.43 163 | 89.94 134 | 72.28 139 | 72.14 217 | 84.95 232 | 55.72 187 | 93.44 130 | 73.64 112 | 72.86 272 | 89.05 189 |
|
EPNet_dtu | | | 75.46 217 | 74.86 206 | 77.23 254 | 82.57 264 | 54.60 290 | 86.89 149 | 83.09 249 | 71.64 147 | 66.25 283 | 85.86 212 | 55.99 186 | 88.04 264 | 54.92 256 | 86.55 117 | 89.05 189 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
pm-mvs1 | | | 77.25 189 | 76.68 170 | 78.93 228 | 84.22 212 | 58.62 240 | 86.41 165 | 88.36 185 | 71.37 153 | 73.31 195 | 88.01 141 | 61.22 146 | 89.15 247 | 64.24 189 | 73.01 271 | 89.03 191 |
|
PVSNet_Blended | | | 80.98 94 | 80.34 93 | 82.90 138 | 88.85 117 | 65.40 147 | 84.43 218 | 92.00 63 | 67.62 211 | 78.11 118 | 85.05 231 | 66.02 77 | 94.27 85 | 71.52 138 | 89.50 80 | 89.01 192 |
|
PAPM | | | 77.68 175 | 76.40 174 | 81.51 180 | 87.29 172 | 61.85 221 | 83.78 231 | 89.59 142 | 64.74 238 | 71.23 226 | 88.70 121 | 62.59 122 | 93.66 122 | 52.66 266 | 87.03 112 | 89.01 192 |
|
WTY-MVS | | | 75.65 215 | 75.68 191 | 75.57 271 | 86.40 183 | 56.82 266 | 77.92 284 | 82.40 255 | 65.10 235 | 76.18 156 | 87.72 147 | 63.13 105 | 80.90 299 | 60.31 219 | 81.96 168 | 89.00 194 |
|
æ— å…ˆéªŒ | | | | | | | | 87.48 125 | 88.98 166 | 60.00 278 | | | | 94.12 93 | 67.28 165 | | 88.97 195 |
|
ACMM | | 73.20 8 | 80.78 105 | 79.84 101 | 83.58 108 | 89.31 105 | 68.37 99 | 89.99 50 | 91.60 81 | 70.28 168 | 77.25 133 | 89.66 101 | 53.37 208 | 93.53 126 | 74.24 108 | 82.85 159 | 88.85 196 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
pmmvs6 | | | 74.69 221 | 73.39 220 | 78.61 233 | 81.38 278 | 57.48 258 | 86.64 158 | 87.95 193 | 64.99 237 | 70.18 237 | 86.61 187 | 50.43 250 | 89.52 235 | 62.12 204 | 70.18 288 | 88.83 197 |
|
原ACMM1 | | | | | 84.35 85 | 93.01 40 | 68.79 86 | | 92.44 45 | 63.96 248 | 81.09 79 | 91.57 66 | 66.06 76 | 95.45 45 | 67.19 167 | 94.82 30 | 88.81 198 |
|
CNLPA | | | 78.08 163 | 76.79 169 | 81.97 164 | 90.40 73 | 71.07 47 | 87.59 118 | 84.55 230 | 66.03 228 | 72.38 212 | 89.64 102 | 57.56 175 | 86.04 277 | 59.61 224 | 83.35 153 | 88.79 199 |
|
K. test v3 | | | 71.19 256 | 68.51 262 | 79.21 221 | 83.04 253 | 57.78 254 | 84.35 221 | 76.91 305 | 72.90 128 | 62.99 300 | 82.86 253 | 39.27 307 | 91.09 214 | 61.65 209 | 52.66 331 | 88.75 200 |
|
旧先验1 | | | | | | 91.96 55 | 65.79 141 | | 86.37 214 | | | 93.08 43 | 69.31 53 | | | 92.74 51 | 88.74 201 |
|
PatchmatchNet | | | 73.12 244 | 71.33 244 | 78.49 237 | 83.18 248 | 60.85 226 | 79.63 268 | 78.57 296 | 64.13 244 | 71.73 221 | 79.81 290 | 51.20 236 | 85.97 278 | 57.40 245 | 76.36 238 | 88.66 202 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
SixPastTwentyTwo | | | 73.37 239 | 71.26 246 | 79.70 210 | 85.08 201 | 57.89 251 | 85.57 188 | 83.56 240 | 71.03 157 | 65.66 285 | 85.88 211 | 42.10 297 | 92.57 162 | 59.11 229 | 63.34 313 | 88.65 203 |
|
v18 | | | 77.67 177 | 76.35 179 | 81.64 176 | 84.09 222 | 64.47 178 | 87.27 133 | 89.01 162 | 72.59 135 | 69.39 251 | 82.04 264 | 62.85 108 | 91.80 182 | 72.72 123 | 67.20 298 | 88.63 204 |
|
v17 | | | 77.68 175 | 76.35 179 | 81.69 173 | 84.15 217 | 64.65 167 | 87.33 130 | 88.99 164 | 72.70 133 | 69.25 255 | 82.07 263 | 62.82 112 | 91.79 183 | 72.69 125 | 67.15 299 | 88.63 204 |
|
v16 | | | 77.69 174 | 76.36 178 | 81.68 174 | 84.15 217 | 64.63 169 | 87.33 130 | 88.99 164 | 72.69 134 | 69.31 254 | 82.08 262 | 62.80 113 | 91.79 183 | 72.70 124 | 67.23 297 | 88.63 204 |
|
v15 | | | 77.51 182 | 76.12 182 | 81.66 175 | 84.09 222 | 64.65 167 | 87.14 137 | 88.96 168 | 72.76 131 | 68.90 256 | 81.91 271 | 62.74 115 | 91.73 187 | 72.32 129 | 66.29 304 | 88.61 207 |
|
V14 | | | 77.52 180 | 76.12 182 | 81.70 172 | 84.15 217 | 64.77 165 | 87.21 136 | 88.95 169 | 72.80 130 | 68.79 257 | 81.94 270 | 62.69 117 | 91.72 189 | 72.31 130 | 66.27 305 | 88.60 208 |
|
V9 | | | 77.52 180 | 76.11 185 | 81.73 171 | 84.19 216 | 64.89 162 | 87.26 134 | 88.94 172 | 72.87 129 | 68.65 260 | 81.96 269 | 62.65 120 | 91.72 189 | 72.27 131 | 66.24 306 | 88.60 208 |
|
PS-MVSNAJ | | | 81.69 84 | 81.02 86 | 83.70 105 | 89.51 95 | 68.21 104 | 84.28 223 | 90.09 128 | 70.79 159 | 81.26 78 | 85.62 219 | 63.15 102 | 94.29 83 | 75.62 96 | 88.87 86 | 88.59 210 |
|
v12 | | | 77.51 182 | 76.09 186 | 81.76 170 | 84.22 212 | 64.99 159 | 87.30 132 | 88.93 173 | 72.92 126 | 68.48 264 | 81.97 267 | 62.54 124 | 91.70 192 | 72.24 132 | 66.21 308 | 88.58 211 |
|
xiu_mvs_v2_base | | | 81.69 84 | 81.05 85 | 83.60 107 | 89.15 111 | 68.03 107 | 84.46 216 | 90.02 132 | 70.67 162 | 81.30 77 | 86.53 192 | 63.17 101 | 94.19 90 | 75.60 97 | 88.54 94 | 88.57 212 |
|
v13 | | | 77.50 184 | 76.07 187 | 81.77 168 | 84.23 211 | 65.07 158 | 87.34 129 | 88.91 174 | 72.92 126 | 68.35 265 | 81.97 267 | 62.53 125 | 91.69 193 | 72.20 133 | 66.22 307 | 88.56 213 |
|
v11 | | | 77.45 185 | 76.06 188 | 81.59 179 | 84.22 212 | 64.52 170 | 87.11 142 | 89.02 160 | 72.76 131 | 68.76 258 | 81.90 272 | 62.09 133 | 91.71 191 | 71.98 134 | 66.73 300 | 88.56 213 |
|
DWT-MVSNet_test | | | 73.70 229 | 71.86 238 | 79.21 221 | 82.91 256 | 58.94 238 | 82.34 245 | 82.17 258 | 65.21 233 | 71.05 229 | 78.31 296 | 44.21 284 | 90.17 227 | 63.29 194 | 77.28 217 | 88.53 215 |
|
CostFormer | | | 75.24 220 | 73.90 218 | 79.27 219 | 82.65 263 | 58.27 244 | 80.80 257 | 82.73 253 | 61.57 267 | 75.33 177 | 83.13 251 | 55.52 189 | 91.07 215 | 64.98 185 | 78.34 210 | 88.45 216 |
|
lessismore_v0 | | | | | 78.97 227 | 81.01 284 | 57.15 261 | | 65.99 339 | | 61.16 303 | 82.82 254 | 39.12 308 | 91.34 204 | 59.67 223 | 46.92 335 | 88.43 217 |
|
OpenMVS | | 72.83 10 | 79.77 131 | 78.33 143 | 84.09 92 | 85.17 196 | 69.91 66 | 90.57 38 | 90.97 98 | 66.70 217 | 72.17 214 | 91.91 57 | 54.70 197 | 93.96 98 | 61.81 208 | 90.95 66 | 88.41 218 |
|
OurMVSNet-221017-0 | | | 74.26 225 | 72.42 231 | 79.80 209 | 83.76 236 | 59.59 233 | 85.92 178 | 86.64 209 | 66.39 223 | 66.96 276 | 87.58 151 | 39.46 306 | 91.60 199 | 65.76 179 | 69.27 290 | 88.22 219 |
|
LS3D | | | 76.95 192 | 74.82 207 | 83.37 115 | 90.45 71 | 67.36 118 | 89.15 72 | 86.94 207 | 61.87 266 | 69.52 249 | 90.61 86 | 51.71 232 | 94.53 78 | 46.38 299 | 86.71 115 | 88.21 220 |
|
PatchFormer-LS_test | | | 74.50 222 | 73.05 224 | 78.86 229 | 82.95 255 | 59.55 236 | 81.65 253 | 82.30 257 | 67.44 215 | 71.62 223 | 78.15 298 | 52.34 214 | 88.92 255 | 65.05 184 | 75.90 242 | 88.12 221 |
|
XVG-ACMP-BASELINE | | | 76.11 210 | 74.27 215 | 81.62 177 | 83.20 247 | 64.67 166 | 83.60 234 | 89.75 139 | 69.75 175 | 71.85 220 | 87.09 169 | 32.78 322 | 92.11 175 | 69.99 147 | 80.43 188 | 88.09 222 |
|
Patchmatch-test1 | | | 73.49 232 | 71.85 239 | 78.41 238 | 84.05 228 | 62.17 218 | 79.96 266 | 79.29 289 | 66.30 224 | 72.38 212 | 79.58 291 | 51.95 223 | 85.08 284 | 55.46 254 | 77.67 214 | 87.99 223 |
|
tpm2 | | | 73.26 242 | 71.46 242 | 78.63 232 | 83.34 243 | 56.71 269 | 80.65 260 | 80.40 279 | 56.63 302 | 73.55 193 | 82.02 265 | 51.80 231 | 91.24 206 | 56.35 251 | 78.42 209 | 87.95 224 |
|
MDTV_nov1_ep13_2view | | | | | | | 37.79 338 | 75.16 297 | | 55.10 307 | 66.53 280 | | 49.34 258 | | 53.98 259 | | 87.94 225 |
|
Patchmatch-test | | | 64.82 290 | 63.24 288 | 69.57 302 | 79.42 299 | 49.82 319 | 63.49 331 | 69.05 335 | 51.98 321 | 59.95 308 | 80.13 286 | 50.91 239 | 70.98 334 | 40.66 319 | 73.57 268 | 87.90 226 |
|
PLC | | 70.83 11 | 78.05 164 | 76.37 175 | 83.08 126 | 91.88 58 | 67.80 110 | 88.19 104 | 89.46 147 | 64.33 243 | 69.87 246 | 88.38 132 | 53.66 206 | 93.58 123 | 58.86 231 | 82.73 161 | 87.86 227 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
tpm | | | 72.37 251 | 71.71 241 | 74.35 281 | 82.19 268 | 52.00 305 | 79.22 273 | 77.29 303 | 64.56 240 | 72.95 200 | 83.68 248 | 51.35 234 | 83.26 293 | 58.33 237 | 75.80 243 | 87.81 228 |
|
Patchmatch-RL test | | | 70.24 265 | 67.78 274 | 77.61 248 | 77.43 308 | 59.57 234 | 71.16 307 | 70.33 328 | 62.94 255 | 68.65 260 | 72.77 318 | 50.62 244 | 85.49 281 | 69.58 150 | 66.58 302 | 87.77 229 |
|
F-COLMAP | | | 76.38 203 | 74.33 214 | 82.50 155 | 89.28 106 | 66.95 126 | 88.41 95 | 89.03 159 | 64.05 245 | 66.83 277 | 88.61 125 | 46.78 271 | 92.89 153 | 57.48 243 | 78.55 205 | 87.67 230 |
|
Baseline_NR-MVSNet | | | 78.15 162 | 78.33 143 | 77.61 248 | 85.79 188 | 56.21 278 | 86.78 154 | 85.76 221 | 73.60 109 | 77.93 122 | 87.57 152 | 65.02 84 | 88.99 250 | 67.14 168 | 75.33 251 | 87.63 231 |
|
ACMH+ | | 68.96 14 | 76.01 211 | 74.01 216 | 82.03 163 | 88.60 128 | 65.31 152 | 88.86 78 | 87.55 199 | 70.25 169 | 67.75 268 | 87.47 156 | 41.27 300 | 93.19 140 | 58.37 236 | 75.94 241 | 87.60 232 |
|
1314 | | | 76.53 197 | 75.30 200 | 80.21 203 | 83.93 230 | 62.32 216 | 84.66 208 | 88.81 175 | 60.23 276 | 70.16 239 | 84.07 242 | 55.30 191 | 90.73 220 | 67.37 164 | 83.21 155 | 87.59 233 |
|
API-MVS | | | 81.99 80 | 81.23 82 | 84.26 88 | 90.94 66 | 70.18 65 | 91.10 31 | 89.32 150 | 71.51 152 | 78.66 99 | 88.28 135 | 65.26 81 | 95.10 61 | 64.74 187 | 91.23 64 | 87.51 234 |
|
AdaColmap | | | 80.58 110 | 79.42 113 | 84.06 93 | 93.09 39 | 68.91 85 | 89.36 64 | 88.97 167 | 69.27 183 | 75.70 167 | 89.69 100 | 57.20 180 | 95.77 37 | 63.06 195 | 88.41 97 | 87.50 235 |
|
PVSNet_BlendedMVS | | | 80.60 108 | 80.02 97 | 82.36 158 | 88.85 117 | 65.40 147 | 86.16 171 | 92.00 63 | 69.34 182 | 78.11 118 | 86.09 205 | 66.02 77 | 94.27 85 | 71.52 138 | 82.06 167 | 87.39 236 |
|
sss | | | 73.60 231 | 73.64 219 | 73.51 286 | 82.80 258 | 55.01 288 | 76.12 290 | 81.69 267 | 62.47 261 | 74.68 188 | 85.85 213 | 57.32 176 | 78.11 311 | 60.86 216 | 80.93 178 | 87.39 236 |
|
semantic-postprocess | | | | | 80.11 204 | 82.69 262 | 64.85 163 | | 83.47 242 | 69.16 186 | 70.49 234 | 84.15 241 | 50.83 243 | 88.15 262 | 69.23 152 | 72.14 278 | 87.34 238 |
|
PVSNet | | 64.34 18 | 72.08 252 | 70.87 250 | 75.69 269 | 86.21 185 | 56.44 273 | 74.37 302 | 80.73 275 | 62.06 265 | 70.17 238 | 82.23 260 | 42.86 292 | 83.31 292 | 54.77 257 | 84.45 136 | 87.32 239 |
|
æ–°å‡ ä½•1 | | | | | 83.42 112 | 93.13 36 | 70.71 55 | | 85.48 222 | 57.43 297 | 81.80 71 | 91.98 56 | 63.28 97 | 92.27 171 | 64.60 188 | 92.99 48 | 87.27 240 |
|
1121 | | | 80.84 97 | 79.77 102 | 84.05 94 | 93.11 38 | 70.78 54 | 84.66 208 | 85.42 223 | 57.37 298 | 81.76 72 | 92.02 55 | 63.41 95 | 94.12 93 | 67.28 165 | 92.93 49 | 87.26 241 |
|
TR-MVS | | | 77.44 186 | 76.18 181 | 81.20 186 | 88.24 139 | 63.24 202 | 84.61 212 | 86.40 213 | 67.55 213 | 77.81 123 | 86.48 194 | 54.10 202 | 93.15 142 | 57.75 242 | 82.72 162 | 87.20 242 |
|
TransMVSNet (Re) | | | 75.39 219 | 74.56 210 | 77.86 243 | 85.50 194 | 57.10 262 | 86.78 154 | 86.09 219 | 72.17 143 | 71.53 224 | 87.34 158 | 63.01 106 | 89.31 240 | 56.84 249 | 61.83 316 | 87.17 243 |
|
ACMH | | 67.68 16 | 75.89 212 | 73.93 217 | 81.77 168 | 88.71 126 | 66.61 128 | 88.62 88 | 89.01 162 | 69.81 173 | 66.78 278 | 86.70 181 | 41.95 299 | 91.51 201 | 55.64 253 | 78.14 211 | 87.17 243 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
EPMVS | | | 69.02 271 | 68.16 266 | 71.59 292 | 79.61 297 | 49.80 320 | 77.40 286 | 66.93 338 | 62.82 257 | 70.01 241 | 79.05 292 | 45.79 277 | 77.86 313 | 56.58 250 | 75.26 253 | 87.13 245 |
|
CR-MVSNet | | | 73.37 239 | 71.27 245 | 79.67 212 | 81.32 281 | 65.19 155 | 75.92 292 | 80.30 280 | 59.92 279 | 72.73 202 | 81.19 275 | 52.50 211 | 86.69 271 | 59.84 222 | 77.71 212 | 87.11 246 |
|
RPMNet | | | 71.62 253 | 68.94 260 | 79.67 212 | 81.32 281 | 65.19 155 | 75.92 292 | 78.30 298 | 57.60 296 | 72.73 202 | 76.45 308 | 52.30 215 | 86.69 271 | 48.14 287 | 77.71 212 | 87.11 246 |
|
XXY-MVS | | | 75.41 218 | 75.56 192 | 74.96 275 | 83.59 238 | 57.82 253 | 80.59 261 | 83.87 236 | 66.54 222 | 74.93 186 | 88.31 134 | 63.24 99 | 80.09 303 | 62.16 203 | 76.85 228 | 86.97 248 |
|
tpmrst | | | 72.39 249 | 72.13 236 | 73.18 288 | 80.54 288 | 49.91 318 | 79.91 267 | 79.08 290 | 63.11 251 | 71.69 222 | 79.95 287 | 55.32 190 | 82.77 294 | 65.66 180 | 73.89 265 | 86.87 249 |
|
thres200 | | | 75.55 216 | 74.47 212 | 78.82 230 | 87.78 158 | 57.85 252 | 83.07 242 | 83.51 241 | 72.44 138 | 75.84 161 | 84.42 239 | 52.08 220 | 91.75 186 | 47.41 290 | 83.64 147 | 86.86 250 |
|
ITE_SJBPF | | | | | 78.22 240 | 81.77 272 | 60.57 228 | | 83.30 244 | 69.25 184 | 67.54 270 | 87.20 164 | 36.33 318 | 87.28 269 | 54.34 258 | 74.62 259 | 86.80 251 |
|
test222 | | | | | | 91.50 60 | 68.26 102 | 84.16 225 | 83.20 248 | 54.63 309 | 79.74 86 | 91.63 64 | 58.97 166 | | | 91.42 61 | 86.77 252 |
|
MIMVSNet | | | 70.69 260 | 69.30 256 | 74.88 276 | 84.52 206 | 56.35 276 | 75.87 294 | 79.42 288 | 64.59 239 | 67.76 267 | 82.41 257 | 41.10 301 | 81.54 298 | 46.64 298 | 81.34 175 | 86.75 253 |
|
BH-untuned | | | 79.47 138 | 78.60 132 | 82.05 162 | 89.19 110 | 65.91 138 | 86.07 174 | 88.52 183 | 72.18 142 | 75.42 171 | 87.69 149 | 61.15 147 | 93.54 125 | 60.38 218 | 86.83 113 | 86.70 254 |
|
LTVRE_ROB | | 69.57 13 | 76.25 204 | 74.54 211 | 81.41 182 | 88.60 128 | 64.38 181 | 79.24 272 | 89.12 158 | 70.76 161 | 69.79 248 | 87.86 142 | 49.09 261 | 93.20 139 | 56.21 252 | 80.16 190 | 86.65 255 |
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 |
testdata | | | | | 79.97 206 | 90.90 67 | 64.21 182 | | 84.71 228 | 59.27 284 | 85.40 23 | 92.91 44 | 62.02 134 | 89.08 248 | 68.95 154 | 91.37 62 | 86.63 256 |
|
thresconf0.02 | | | 73.39 235 | 72.42 231 | 76.31 260 | 87.85 148 | 53.28 300 | 83.38 236 | 79.08 290 | 68.40 201 | 72.45 207 | 86.08 206 | 50.60 245 | 89.19 242 | 44.25 307 | 79.66 194 | 86.48 257 |
|
tfpn_n400 | | | 73.39 235 | 72.42 231 | 76.31 260 | 87.85 148 | 53.28 300 | 83.38 236 | 79.08 290 | 68.40 201 | 72.45 207 | 86.08 206 | 50.60 245 | 89.19 242 | 44.25 307 | 79.66 194 | 86.48 257 |
|
tfpnconf | | | 73.39 235 | 72.42 231 | 76.31 260 | 87.85 148 | 53.28 300 | 83.38 236 | 79.08 290 | 68.40 201 | 72.45 207 | 86.08 206 | 50.60 245 | 89.19 242 | 44.25 307 | 79.66 194 | 86.48 257 |
|
tfpnview11 | | | 73.39 235 | 72.42 231 | 76.31 260 | 87.85 148 | 53.28 300 | 83.38 236 | 79.08 290 | 68.40 201 | 72.45 207 | 86.08 206 | 50.60 245 | 89.19 242 | 44.25 307 | 79.66 194 | 86.48 257 |
|
MIMVSNet1 | | | 68.58 273 | 66.78 278 | 73.98 284 | 80.07 293 | 51.82 306 | 80.77 258 | 84.37 231 | 64.40 242 | 59.75 309 | 82.16 261 | 36.47 317 | 83.63 290 | 42.73 315 | 70.33 287 | 86.48 257 |
|
tfpnnormal | | | 74.39 223 | 73.16 223 | 78.08 241 | 86.10 186 | 58.05 246 | 84.65 211 | 87.53 200 | 70.32 167 | 71.22 227 | 85.63 218 | 54.97 192 | 89.86 229 | 43.03 314 | 75.02 255 | 86.32 262 |
|
tpm cat1 | | | 70.57 261 | 68.31 264 | 77.35 252 | 82.41 266 | 57.95 250 | 78.08 283 | 80.22 283 | 52.04 320 | 68.54 263 | 77.66 303 | 52.00 222 | 87.84 266 | 51.77 267 | 72.07 279 | 86.25 263 |
|
CVMVSNet | | | 72.99 246 | 72.58 228 | 74.25 282 | 84.28 209 | 50.85 314 | 86.41 165 | 83.45 243 | 44.56 329 | 73.23 197 | 87.54 154 | 49.38 257 | 85.70 279 | 65.90 177 | 78.44 208 | 86.19 264 |
|
tfpn1000 | | | 73.44 234 | 72.49 229 | 76.29 264 | 87.81 154 | 53.69 297 | 84.05 229 | 78.81 295 | 67.99 208 | 72.09 218 | 86.27 201 | 49.95 254 | 89.04 249 | 44.09 311 | 81.38 174 | 86.15 265 |
|
AllTest | | | 70.96 258 | 68.09 268 | 79.58 214 | 85.15 197 | 63.62 191 | 84.58 213 | 79.83 285 | 62.31 262 | 60.32 306 | 86.73 174 | 32.02 323 | 88.96 253 | 50.28 273 | 71.57 282 | 86.15 265 |
|
TestCases | | | | | 79.58 214 | 85.15 197 | 63.62 191 | | 79.83 285 | 62.31 262 | 60.32 306 | 86.73 174 | 32.02 323 | 88.96 253 | 50.28 273 | 71.57 282 | 86.15 265 |
|
test-LLR | | | 72.94 247 | 72.43 230 | 74.48 279 | 81.35 279 | 58.04 247 | 78.38 279 | 77.46 301 | 66.66 218 | 69.95 244 | 79.00 294 | 48.06 265 | 79.24 305 | 66.13 173 | 84.83 130 | 86.15 265 |
|
test-mter | | | 71.41 255 | 70.39 253 | 74.48 279 | 81.35 279 | 58.04 247 | 78.38 279 | 77.46 301 | 60.32 275 | 69.95 244 | 79.00 294 | 36.08 319 | 79.24 305 | 66.13 173 | 84.83 130 | 86.15 265 |
|
IterMVS | | | 74.29 224 | 72.94 225 | 78.35 239 | 81.53 275 | 63.49 195 | 81.58 254 | 82.49 254 | 68.06 207 | 69.99 243 | 83.69 247 | 51.66 233 | 85.54 280 | 65.85 178 | 71.64 281 | 86.01 270 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DP-MVS | | | 76.78 194 | 74.57 209 | 83.42 112 | 93.29 32 | 69.46 77 | 88.55 91 | 83.70 237 | 63.98 247 | 70.20 236 | 88.89 118 | 54.01 204 | 94.80 73 | 46.66 296 | 81.88 170 | 86.01 270 |
|
Patchmtry | | | 70.74 259 | 69.16 258 | 75.49 272 | 80.72 285 | 54.07 294 | 74.94 301 | 80.30 280 | 58.34 289 | 70.01 241 | 81.19 275 | 52.50 211 | 86.54 273 | 53.37 263 | 71.09 284 | 85.87 272 |
|
ambc | | | | | 75.24 274 | 73.16 324 | 50.51 316 | 63.05 332 | 87.47 202 | | 64.28 293 | 77.81 302 | 17.80 341 | 89.73 232 | 57.88 241 | 60.64 320 | 85.49 273 |
|
UnsupCasMVSNet_eth | | | 67.33 279 | 65.99 280 | 71.37 294 | 73.48 322 | 51.47 310 | 75.16 297 | 85.19 225 | 65.20 234 | 60.78 304 | 80.93 282 | 42.35 294 | 77.20 315 | 57.12 247 | 53.69 330 | 85.44 274 |
|
PatchT | | | 68.46 275 | 67.85 271 | 70.29 300 | 80.70 286 | 43.93 328 | 72.47 305 | 74.88 313 | 60.15 277 | 70.55 231 | 76.57 307 | 49.94 255 | 81.59 297 | 50.58 271 | 74.83 257 | 85.34 275 |
|
tfpn_ndepth | | | 73.70 229 | 72.75 226 | 76.52 258 | 87.78 158 | 54.92 289 | 84.32 222 | 80.28 282 | 67.57 212 | 72.50 204 | 84.82 234 | 50.12 252 | 89.44 238 | 45.73 302 | 81.66 172 | 85.20 276 |
|
ADS-MVSNet2 | | | 66.20 287 | 63.33 287 | 74.82 277 | 79.92 294 | 58.75 239 | 67.55 324 | 75.19 311 | 53.37 316 | 65.25 288 | 75.86 309 | 42.32 295 | 80.53 301 | 41.57 317 | 68.91 292 | 85.18 277 |
|
ADS-MVSNet | | | 64.36 292 | 62.88 291 | 68.78 307 | 79.92 294 | 47.17 323 | 67.55 324 | 71.18 327 | 53.37 316 | 65.25 288 | 75.86 309 | 42.32 295 | 73.99 328 | 41.57 317 | 68.91 292 | 85.18 277 |
|
FMVSNet5 | | | 69.50 269 | 67.96 269 | 74.15 283 | 82.97 254 | 55.35 287 | 80.01 265 | 82.12 260 | 62.56 260 | 63.02 298 | 81.53 274 | 36.92 316 | 81.92 296 | 48.42 281 | 74.06 263 | 85.17 279 |
|
pmmvs5 | | | 71.55 254 | 70.20 254 | 75.61 270 | 77.83 306 | 56.39 274 | 81.74 251 | 80.89 272 | 57.76 294 | 67.46 271 | 84.49 238 | 49.26 260 | 85.32 283 | 57.08 248 | 75.29 252 | 85.11 280 |
|
CMPMVS | | 51.72 21 | 70.19 266 | 68.16 266 | 76.28 265 | 73.15 325 | 57.55 257 | 79.47 270 | 83.92 235 | 48.02 327 | 56.48 320 | 84.81 235 | 43.13 289 | 86.42 275 | 62.67 199 | 81.81 171 | 84.89 281 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
testgi | | | 66.67 283 | 66.53 279 | 67.08 310 | 75.62 317 | 41.69 333 | 75.93 291 | 76.50 306 | 66.11 225 | 65.20 290 | 86.59 188 | 35.72 320 | 74.71 324 | 43.71 312 | 73.38 270 | 84.84 282 |
|
MSDG | | | 73.36 241 | 70.99 248 | 80.49 196 | 84.51 207 | 65.80 140 | 80.71 259 | 86.13 218 | 65.70 230 | 65.46 286 | 83.74 246 | 44.60 282 | 90.91 217 | 51.13 270 | 76.89 226 | 84.74 283 |
|
pmmvs4 | | | 74.03 227 | 71.91 237 | 80.39 197 | 81.96 270 | 68.32 100 | 81.45 255 | 82.14 259 | 59.32 283 | 69.87 246 | 85.13 228 | 52.40 213 | 88.13 263 | 60.21 220 | 74.74 258 | 84.73 284 |
|
gg-mvs-nofinetune | | | 69.95 267 | 67.96 269 | 75.94 267 | 83.07 251 | 54.51 292 | 77.23 287 | 70.29 329 | 63.11 251 | 70.32 235 | 62.33 330 | 43.62 287 | 88.69 257 | 53.88 261 | 87.76 101 | 84.62 285 |
|
BH-w/o | | | 78.21 159 | 77.33 161 | 80.84 192 | 88.81 121 | 65.13 157 | 84.87 205 | 87.85 195 | 69.75 175 | 74.52 189 | 84.74 237 | 61.34 142 | 93.11 145 | 58.24 238 | 85.84 126 | 84.27 286 |
|
MVS | | | 78.19 161 | 76.99 165 | 81.78 167 | 85.66 190 | 66.99 122 | 84.66 208 | 90.47 111 | 55.08 308 | 72.02 219 | 85.27 226 | 63.83 93 | 94.11 95 | 66.10 175 | 89.80 78 | 84.24 287 |
|
COLMAP_ROB | | 66.92 17 | 73.01 245 | 70.41 252 | 80.81 193 | 87.13 174 | 65.63 142 | 88.30 100 | 84.19 234 | 62.96 254 | 63.80 297 | 87.69 149 | 38.04 312 | 92.56 163 | 46.66 296 | 74.91 256 | 84.24 287 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
new-patchmatchnet | | | 61.73 295 | 61.73 295 | 61.70 318 | 72.74 326 | 24.50 349 | 69.16 317 | 78.03 299 | 61.40 268 | 56.72 319 | 75.53 311 | 38.42 310 | 76.48 318 | 45.95 301 | 57.67 323 | 84.13 289 |
|
TESTMET0.1,1 | | | 69.89 268 | 69.00 259 | 72.55 289 | 79.27 303 | 56.85 265 | 78.38 279 | 74.71 317 | 57.64 295 | 68.09 266 | 77.19 305 | 37.75 313 | 76.70 316 | 63.92 190 | 84.09 138 | 84.10 290 |
|
tpmvs | | | 71.09 257 | 69.29 257 | 76.49 259 | 82.04 269 | 56.04 279 | 78.92 276 | 81.37 271 | 64.05 245 | 67.18 275 | 78.28 297 | 49.74 256 | 89.77 230 | 49.67 278 | 72.37 275 | 83.67 291 |
|
test20.03 | | | 67.45 278 | 66.95 277 | 68.94 304 | 75.48 319 | 44.84 326 | 77.50 285 | 77.67 300 | 66.66 218 | 63.01 299 | 83.80 244 | 47.02 269 | 78.40 309 | 42.53 316 | 68.86 294 | 83.58 292 |
|
test0.0.03 1 | | | 68.00 276 | 67.69 275 | 68.90 305 | 77.55 307 | 47.43 322 | 75.70 295 | 72.95 324 | 66.66 218 | 66.56 279 | 82.29 259 | 48.06 265 | 75.87 320 | 44.97 306 | 74.51 260 | 83.41 293 |
|
Anonymous20231206 | | | 68.60 272 | 67.80 273 | 71.02 298 | 80.23 292 | 50.75 315 | 78.30 282 | 80.47 277 | 56.79 301 | 66.11 284 | 82.63 256 | 46.35 272 | 78.95 307 | 43.62 313 | 75.70 244 | 83.36 294 |
|
EU-MVSNet | | | 68.53 274 | 67.61 276 | 71.31 297 | 78.51 305 | 47.01 324 | 84.47 214 | 84.27 233 | 42.27 330 | 66.44 282 | 84.79 236 | 40.44 304 | 83.76 288 | 58.76 233 | 68.54 296 | 83.17 295 |
|
dp | | | 66.80 281 | 65.43 281 | 70.90 299 | 79.74 296 | 48.82 321 | 75.12 299 | 74.77 315 | 59.61 281 | 64.08 295 | 77.23 304 | 42.89 291 | 80.72 300 | 48.86 280 | 66.58 302 | 83.16 296 |
|
pmmvs-eth3d | | | 70.50 263 | 67.83 272 | 78.52 236 | 77.37 309 | 66.18 133 | 81.82 249 | 81.51 269 | 58.90 286 | 63.90 296 | 80.42 284 | 42.69 293 | 86.28 276 | 58.56 234 | 65.30 310 | 83.11 297 |
|
YYNet1 | | | 65.03 288 | 62.91 290 | 71.38 293 | 75.85 315 | 56.60 271 | 69.12 318 | 74.66 319 | 57.28 299 | 54.12 323 | 77.87 301 | 45.85 276 | 74.48 325 | 49.95 276 | 61.52 318 | 83.05 298 |
|
MDA-MVSNet-bldmvs | | | 66.68 282 | 63.66 286 | 75.75 268 | 79.28 302 | 60.56 229 | 73.92 303 | 78.35 297 | 64.43 241 | 50.13 331 | 79.87 289 | 44.02 286 | 83.67 289 | 46.10 300 | 56.86 325 | 83.03 299 |
|
Anonymous20231211 | | | 64.82 290 | 61.79 294 | 73.91 285 | 77.11 311 | 50.92 313 | 85.29 197 | 81.53 268 | 54.19 310 | 57.98 313 | 78.03 299 | 26.90 328 | 87.83 267 | 37.92 322 | 57.12 324 | 82.99 300 |
|
MDA-MVSNet_test_wron | | | 65.03 288 | 62.92 289 | 71.37 294 | 75.93 314 | 56.73 267 | 69.09 319 | 74.73 316 | 57.28 299 | 54.03 324 | 77.89 300 | 45.88 275 | 74.39 326 | 49.89 277 | 61.55 317 | 82.99 300 |
|
USDC | | | 70.33 264 | 68.37 263 | 76.21 266 | 80.60 287 | 56.23 277 | 79.19 274 | 86.49 211 | 60.89 271 | 61.29 302 | 85.47 223 | 31.78 325 | 89.47 237 | 53.37 263 | 76.21 239 | 82.94 302 |
|
OpenMVS_ROB | | 64.09 19 | 70.56 262 | 68.19 265 | 77.65 247 | 80.26 290 | 59.41 237 | 85.01 203 | 82.96 251 | 58.76 287 | 65.43 287 | 82.33 258 | 37.63 315 | 91.23 207 | 45.34 305 | 76.03 240 | 82.32 303 |
|
JIA-IIPM | | | 66.32 286 | 62.82 292 | 76.82 256 | 77.09 312 | 61.72 222 | 65.34 328 | 75.38 309 | 58.04 292 | 64.51 292 | 62.32 331 | 42.05 298 | 86.51 274 | 51.45 269 | 69.22 291 | 82.21 304 |
|
EG-PatchMatch MVS | | | 74.04 226 | 71.82 240 | 80.71 195 | 84.92 202 | 67.42 115 | 85.86 179 | 88.08 191 | 66.04 227 | 64.22 294 | 83.85 243 | 35.10 321 | 92.56 163 | 57.44 244 | 80.83 180 | 82.16 305 |
|
MVP-Stereo | | | 76.12 209 | 74.46 213 | 81.13 189 | 85.37 195 | 69.79 68 | 84.42 219 | 87.95 193 | 65.03 236 | 67.46 271 | 85.33 225 | 53.28 209 | 91.73 187 | 58.01 240 | 83.27 154 | 81.85 306 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
TDRefinement | | | 67.49 277 | 64.34 284 | 76.92 255 | 73.47 323 | 61.07 223 | 84.86 206 | 82.98 250 | 59.77 280 | 58.30 312 | 85.13 228 | 26.06 330 | 87.89 265 | 47.92 289 | 60.59 321 | 81.81 307 |
|
GG-mvs-BLEND | | | | | 75.38 273 | 81.59 274 | 55.80 285 | 79.32 271 | 69.63 331 | | 67.19 274 | 73.67 317 | 43.24 288 | 88.90 256 | 50.41 272 | 84.50 134 | 81.45 308 |
|
test_0402 | | | 72.79 248 | 70.44 251 | 79.84 208 | 88.13 141 | 65.99 136 | 85.93 177 | 84.29 232 | 65.57 232 | 67.40 273 | 85.49 222 | 46.92 270 | 92.61 161 | 35.88 325 | 74.38 261 | 80.94 309 |
|
UnsupCasMVSNet_bld | | | 63.70 294 | 61.53 296 | 70.21 301 | 73.69 321 | 51.39 311 | 72.82 304 | 81.89 265 | 55.63 306 | 57.81 314 | 71.80 320 | 38.67 309 | 78.61 308 | 49.26 279 | 52.21 332 | 80.63 310 |
|
LCM-MVSNet | | | 54.25 306 | 49.68 312 | 67.97 309 | 53.73 344 | 45.28 325 | 66.85 327 | 80.78 274 | 35.96 336 | 39.45 336 | 62.23 332 | 8.70 350 | 78.06 312 | 48.24 286 | 51.20 333 | 80.57 311 |
|
N_pmnet | | | 52.79 309 | 53.26 307 | 51.40 328 | 78.99 304 | 7.68 353 | 69.52 314 | 3.89 354 | 51.63 323 | 57.01 318 | 74.98 312 | 40.83 302 | 65.96 341 | 37.78 323 | 64.67 311 | 80.56 312 |
|
TinyColmap | | | 67.30 280 | 64.81 282 | 74.76 278 | 81.92 271 | 56.68 270 | 80.29 263 | 81.49 270 | 60.33 274 | 56.27 321 | 83.22 250 | 24.77 332 | 87.66 268 | 45.52 303 | 69.47 289 | 79.95 313 |
|
PM-MVS | | | 66.41 285 | 64.14 285 | 73.20 287 | 73.92 320 | 56.45 272 | 78.97 275 | 64.96 342 | 63.88 249 | 64.72 291 | 80.24 285 | 19.84 338 | 83.44 291 | 66.24 172 | 64.52 312 | 79.71 314 |
|
ANet_high | | | 50.57 312 | 46.10 314 | 63.99 313 | 48.67 347 | 39.13 335 | 70.99 310 | 80.85 273 | 61.39 269 | 31.18 339 | 57.70 335 | 17.02 342 | 73.65 329 | 31.22 334 | 15.89 346 | 79.18 315 |
|
LF4IMVS | | | 64.02 293 | 62.19 293 | 69.50 303 | 70.90 330 | 53.29 299 | 76.13 289 | 77.18 304 | 52.65 319 | 58.59 310 | 80.98 280 | 23.55 333 | 76.52 317 | 53.06 265 | 66.66 301 | 78.68 316 |
|
PatchMatch-RL | | | 72.38 250 | 70.90 249 | 76.80 257 | 88.60 128 | 67.38 117 | 79.53 269 | 76.17 307 | 62.75 258 | 69.36 252 | 82.00 266 | 45.51 280 | 84.89 285 | 53.62 262 | 80.58 184 | 78.12 317 |
|
1111 | | | 57.11 304 | 56.82 305 | 57.97 322 | 69.10 332 | 28.28 344 | 68.90 320 | 74.54 320 | 54.01 312 | 53.71 325 | 74.51 313 | 23.09 334 | 67.90 339 | 32.28 330 | 61.26 319 | 77.73 318 |
|
MS-PatchMatch | | | 73.83 228 | 72.67 227 | 77.30 253 | 83.87 231 | 66.02 135 | 81.82 249 | 84.66 229 | 61.37 270 | 68.61 262 | 82.82 254 | 47.29 267 | 88.21 261 | 59.27 227 | 84.32 137 | 77.68 319 |
|
testus | | | 59.00 300 | 57.91 299 | 62.25 317 | 72.25 327 | 39.09 336 | 69.74 312 | 75.02 312 | 53.04 318 | 57.21 317 | 73.72 316 | 18.76 340 | 70.33 335 | 32.86 328 | 68.57 295 | 77.35 320 |
|
LP | | | 61.36 297 | 57.78 300 | 72.09 290 | 75.54 318 | 58.53 241 | 67.16 326 | 75.22 310 | 51.90 322 | 54.13 322 | 69.97 324 | 37.73 314 | 80.45 302 | 32.74 329 | 55.63 327 | 77.29 321 |
|
DSMNet-mixed | | | 57.77 303 | 56.90 303 | 60.38 319 | 67.70 335 | 35.61 339 | 69.18 316 | 53.97 345 | 32.30 340 | 57.49 316 | 79.88 288 | 40.39 305 | 68.57 338 | 38.78 321 | 72.37 275 | 76.97 322 |
|
test2356 | | | 59.50 298 | 58.08 298 | 63.74 314 | 71.23 329 | 41.88 331 | 67.59 323 | 72.42 326 | 53.72 314 | 57.65 315 | 70.74 322 | 26.31 329 | 72.40 331 | 32.03 332 | 71.06 285 | 76.93 323 |
|
test1235678 | | | 58.74 301 | 56.89 304 | 64.30 312 | 69.70 331 | 41.87 332 | 71.05 308 | 74.87 314 | 54.06 311 | 50.63 330 | 71.53 321 | 25.30 331 | 74.10 327 | 31.80 333 | 63.10 314 | 76.93 323 |
|
CHOSEN 280x420 | | | 66.51 284 | 64.71 283 | 71.90 291 | 81.45 276 | 63.52 194 | 57.98 336 | 68.95 336 | 53.57 315 | 62.59 301 | 76.70 306 | 46.22 273 | 75.29 323 | 55.25 255 | 79.68 193 | 76.88 325 |
|
testmv | | | 53.85 307 | 51.03 309 | 62.31 316 | 61.46 339 | 38.88 337 | 70.95 311 | 74.69 318 | 51.11 324 | 41.26 333 | 66.85 327 | 14.28 344 | 72.13 332 | 29.19 335 | 49.51 334 | 75.93 326 |
|
PMMVS | | | 69.34 270 | 68.67 261 | 71.35 296 | 75.67 316 | 62.03 219 | 75.17 296 | 73.46 322 | 50.00 325 | 68.68 259 | 79.05 292 | 52.07 221 | 78.13 310 | 61.16 214 | 82.77 160 | 73.90 327 |
|
pmmvs3 | | | 57.79 302 | 54.26 306 | 68.37 308 | 64.02 337 | 56.72 268 | 75.12 299 | 65.17 340 | 40.20 332 | 52.93 327 | 69.86 325 | 20.36 337 | 75.48 322 | 45.45 304 | 55.25 329 | 72.90 328 |
|
PVSNet_0 | | 57.27 20 | 61.67 296 | 59.27 297 | 68.85 306 | 79.61 297 | 57.44 259 | 68.01 322 | 73.44 323 | 55.93 305 | 58.54 311 | 70.41 323 | 44.58 283 | 77.55 314 | 47.01 291 | 35.91 337 | 71.55 329 |
|
no-one | | | 51.08 310 | 45.79 315 | 66.95 311 | 57.92 342 | 50.49 317 | 59.63 335 | 76.04 308 | 48.04 326 | 31.85 337 | 56.10 337 | 19.12 339 | 80.08 304 | 36.89 324 | 26.52 339 | 70.29 330 |
|
PMMVS2 | | | 40.82 317 | 38.86 318 | 46.69 330 | 53.84 343 | 16.45 351 | 48.61 340 | 49.92 347 | 37.49 335 | 31.67 338 | 60.97 333 | 8.14 351 | 56.42 344 | 28.42 336 | 30.72 338 | 67.19 331 |
|
test12356 | | | 49.28 313 | 48.51 313 | 51.59 327 | 62.06 338 | 19.11 350 | 60.40 333 | 72.45 325 | 47.60 328 | 40.64 335 | 65.68 328 | 13.84 345 | 68.72 337 | 27.29 337 | 46.67 336 | 66.94 332 |
|
new_pmnet | | | 50.91 311 | 50.29 310 | 52.78 326 | 68.58 334 | 34.94 342 | 63.71 330 | 56.63 344 | 39.73 333 | 44.95 332 | 65.47 329 | 21.93 336 | 58.48 343 | 34.98 326 | 56.62 326 | 64.92 333 |
|
MVS-HIRNet | | | 59.14 299 | 57.67 301 | 63.57 315 | 81.65 273 | 43.50 329 | 71.73 306 | 65.06 341 | 39.59 334 | 51.43 329 | 57.73 334 | 38.34 311 | 82.58 295 | 39.53 320 | 73.95 264 | 64.62 334 |
|
wuykxyi23d | | | 39.76 318 | 33.18 321 | 59.51 321 | 46.98 348 | 44.01 327 | 57.70 337 | 67.74 337 | 24.13 342 | 13.98 348 | 34.33 342 | 1.27 355 | 71.33 333 | 34.23 327 | 18.23 342 | 63.18 335 |
|
FPMVS | | | 53.68 308 | 51.64 308 | 59.81 320 | 65.08 336 | 51.03 312 | 69.48 315 | 69.58 332 | 41.46 331 | 40.67 334 | 72.32 319 | 16.46 343 | 70.00 336 | 24.24 340 | 65.42 309 | 58.40 336 |
|
PMVS | | 37.38 22 | 44.16 316 | 40.28 317 | 55.82 323 | 40.82 350 | 42.54 330 | 65.12 329 | 63.99 343 | 34.43 337 | 24.48 341 | 57.12 336 | 3.92 352 | 76.17 319 | 17.10 343 | 55.52 328 | 48.75 337 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVE | | 26.22 23 | 30.37 323 | 25.89 325 | 43.81 331 | 44.55 349 | 35.46 341 | 28.87 345 | 39.07 350 | 18.20 344 | 18.58 345 | 40.18 340 | 2.68 353 | 47.37 347 | 17.07 344 | 23.78 341 | 48.60 338 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testpf | | | 56.51 305 | 57.58 302 | 53.30 325 | 71.99 328 | 41.19 334 | 46.89 341 | 69.32 334 | 58.06 291 | 52.87 328 | 69.45 326 | 27.99 327 | 72.73 330 | 59.59 225 | 62.07 315 | 45.98 339 |
|
PNet_i23d | | | 38.26 319 | 35.42 319 | 46.79 329 | 58.74 340 | 35.48 340 | 59.65 334 | 51.25 346 | 32.45 339 | 23.44 344 | 47.53 339 | 2.04 354 | 58.96 342 | 25.60 339 | 18.09 344 | 45.92 340 |
|
Gipuma | | | 45.18 315 | 41.86 316 | 55.16 324 | 77.03 313 | 51.52 309 | 32.50 344 | 80.52 276 | 32.46 338 | 27.12 340 | 35.02 341 | 9.52 349 | 75.50 321 | 22.31 341 | 60.21 322 | 38.45 341 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
DeepMVS_CX | | | | | 27.40 336 | 40.17 351 | 26.90 347 | | 24.59 353 | 17.44 345 | 23.95 342 | 48.61 338 | 9.77 348 | 26.48 348 | 18.06 342 | 24.47 340 | 28.83 342 |
|
E-PMN | | | 31.77 321 | 30.64 322 | 35.15 332 | 52.87 345 | 27.67 346 | 57.09 338 | 47.86 348 | 24.64 341 | 16.40 346 | 33.05 343 | 11.23 347 | 54.90 345 | 14.46 345 | 18.15 343 | 22.87 343 |
|
EMVS | | | 30.81 322 | 29.65 323 | 34.27 333 | 50.96 346 | 25.95 348 | 56.58 339 | 46.80 349 | 24.01 343 | 15.53 347 | 30.68 344 | 12.47 346 | 54.43 346 | 12.81 346 | 17.05 345 | 22.43 344 |
|
tmp_tt | | | 18.61 325 | 21.40 326 | 10.23 338 | 4.82 352 | 10.11 352 | 34.70 343 | 30.74 352 | 1.48 347 | 23.91 343 | 26.07 345 | 28.42 326 | 13.41 350 | 27.12 338 | 15.35 347 | 7.17 345 |
|
wuyk23d | | | 16.82 326 | 15.94 327 | 19.46 337 | 58.74 340 | 31.45 343 | 39.22 342 | 3.74 355 | 6.84 346 | 6.04 349 | 2.70 349 | 1.27 355 | 24.29 349 | 10.54 347 | 14.40 348 | 2.63 346 |
|
test123 | | | 6.12 328 | 8.11 329 | 0.14 339 | 0.06 354 | 0.09 354 | 71.05 308 | 0.03 357 | 0.04 349 | 0.25 351 | 1.30 351 | 0.05 357 | 0.03 352 | 0.21 349 | 0.01 351 | 0.29 347 |
|
.test1245 | | | 45.55 314 | 50.02 311 | 32.14 334 | 69.10 332 | 28.28 344 | 68.90 320 | 74.54 320 | 54.01 312 | 53.71 325 | 74.51 313 | 23.09 334 | 67.90 339 | 32.28 330 | 0.02 349 | 0.25 348 |
|
testmvs | | | 6.04 329 | 8.02 330 | 0.10 340 | 0.08 353 | 0.03 355 | 69.74 312 | 0.04 356 | 0.05 348 | 0.31 350 | 1.68 350 | 0.02 358 | 0.04 351 | 0.24 348 | 0.02 349 | 0.25 348 |
|
cdsmvs_eth3d_5k | | | 19.96 324 | 26.61 324 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 89.26 154 | 0.00 350 | 0.00 352 | 88.61 125 | 61.62 137 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
pcd_1.5k_mvsjas | | | 5.26 330 | 7.02 331 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 0.00 352 | 63.15 102 | 0.00 353 | 0.00 350 | 0.00 352 | 0.00 350 |
|
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 | | | 7.23 327 | 9.64 328 | 0.00 341 | 0.00 355 | 0.00 356 | 0.00 346 | 0.00 358 | 0.00 350 | 0.00 352 | 86.72 176 | 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 | | | | | | 95.06 1 | 72.65 26 | | | | 91.80 1 | | | | | | |
|
test_all | | | | | | | | | 94.22 1 | | | | | | | | |
|
sam_mvs1 | | | | | | | | | | | | | 51.32 235 | | | | |
|
sam_mvs | | | | | | | | | | | | | 50.01 253 | | | | |
|
MTGPA | | | | | | | | | 92.02 60 | | | | | | | | |
|
test_post1 | | | | | | | | 78.90 277 | | | | 5.43 348 | 48.81 264 | 85.44 282 | 59.25 228 | | |
|
test_post | | | | | | | | | | | | 5.46 347 | 50.36 251 | 84.24 287 | | | |
|
patchmatchnet-post | | | | | | | | | | | | 74.00 315 | 51.12 238 | 88.60 258 | | | |
|
MTMP | | | | | | | | | 32.83 351 | | | | | | | | |
|
gm-plane-assit | | | | | | 81.40 277 | 53.83 296 | | | 62.72 259 | | 80.94 281 | | 92.39 167 | 63.40 193 | | |
|
TEST9 | | | | | | 93.26 34 | 72.96 19 | 88.75 84 | 91.89 69 | 68.44 200 | 85.00 28 | 93.10 39 | 74.36 17 | 95.41 48 | | | |
|
test_8 | | | | | | 93.13 36 | 72.57 29 | 88.68 87 | 91.84 72 | 68.69 196 | 84.87 34 | 93.10 39 | 74.43 14 | 95.16 56 | | | |
|
agg_prior | | | | | | 92.85 42 | 71.94 40 | | 91.78 75 | | 84.41 40 | | | 94.93 65 | | | |
|
test_prior4 | | | | | | | 72.60 28 | 89.01 75 | | | | | | | | | |
|
test_prior2 | | | | | | | | 88.85 79 | | 75.41 76 | 84.91 30 | 93.54 29 | 74.28 18 | | 83.31 32 | 95.86 7 | |
|
旧先验2 | | | | | | | | 86.56 161 | | 58.10 290 | 87.04 14 | | | 88.98 251 | 74.07 109 | | |
|
æ–°å‡ ä½•2 | | | | | | | | 86.29 170 | | | | | | | | | |
|
原ACMM2 | | | | | | | | 86.86 150 | | | | | | | | | |
|
testdata2 | | | | | | | | | | | | | | 91.01 216 | 62.37 201 | | |
|
segment_acmp | | | | | | | | | | | | | 73.08 24 | | | | |
|
testdata1 | | | | | | | | 84.14 226 | | 75.71 71 | | | | | | | |
|
plane_prior7 | | | | | | 90.08 79 | 68.51 98 | | | | | | | | | | |
|
plane_prior6 | | | | | | 89.84 84 | 68.70 94 | | | | | | 60.42 159 | | | | |
|
plane_prior4 | | | | | | | | | | | | 91.00 80 | | | | | |
|
plane_prior3 | | | | | | | 68.60 96 | | | 78.44 30 | 78.92 95 | | | | | | |
|
plane_prior2 | | | | | | | | 91.25 28 | | 79.12 23 | | | | | | | |
|
plane_prior1 | | | | | | 89.90 83 | | | | | | | | | | | |
|
plane_prior | | | | | | | 68.71 92 | 90.38 44 | | 77.62 34 | | | | | | 86.16 122 | |
|
n2 | | | | | | | | | 0.00 358 | | | | | | | | |
|
nn | | | | | | | | | 0.00 358 | | | | | | | | |
|
door-mid | | | | | | | | | 69.98 330 | | | | | | | | |
|
test11 | | | | | | | | | 92.23 52 | | | | | | | | |
|
door | | | | | | | | | 69.44 333 | | | | | | | | |
|
HQP5-MVS | | | | | | | 66.98 123 | | | | | | | | | | |
|
HQP-NCC | | | | | | 89.33 100 | | 89.17 68 | | 76.41 59 | 77.23 135 | | | | | | |
|
ACMP_Plane | | | | | | 89.33 100 | | 89.17 68 | | 76.41 59 | 77.23 135 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 77.47 75 | | |
|
HQP3-MVS | | | | | | | | | 92.19 55 | | | | | | | 85.99 124 | |
|
HQP2-MVS | | | | | | | | | | | | | 60.17 162 | | | | |
|
NP-MVS | | | | | | 89.62 90 | 68.32 100 | | | | | 90.24 90 | | | | | |
|
MDTV_nov1_ep13 | | | | 69.97 255 | | 83.18 248 | 53.48 298 | 77.10 288 | 80.18 284 | 60.45 273 | 69.33 253 | 80.44 283 | 48.89 263 | 86.90 270 | 51.60 268 | 78.51 207 | |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 81.95 169 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 81.25 176 | |
|
Test By Simon | | | | | | | | | | | | | 64.33 88 | | | | |
|