test_part3 | | | | | | | | 86.37 5 | | 63.49 35 | | 91.40 3 | | 90.90 1 | 75.98 13 | | |
|
ESAPD | | | 80.72 1 | 81.17 2 | 79.38 6 | 87.58 3 | 60.47 37 | 86.37 5 | 86.64 3 | 63.49 35 | 83.42 2 | 91.40 3 | 65.59 1 | 90.90 1 | 75.98 13 | 90.06 3 | 86.78 39 |
|
SteuartSystems-ACMMP | | | 79.48 6 | 79.31 6 | 79.98 1 | 83.01 57 | 62.18 19 | 87.60 2 | 85.83 8 | 66.69 11 | 78.03 11 | 90.98 6 | 54.26 34 | 90.06 3 | 78.42 7 | 89.02 9 | 87.69 15 |
Skip Steuart: Steuart Systems R&D Blog. |
DeepC-MVS | | 69.38 2 | 78.56 10 | 78.14 13 | 79.83 2 | 83.60 51 | 61.62 24 | 84.17 26 | 86.85 2 | 63.23 37 | 73.84 39 | 90.25 20 | 57.68 14 | 89.96 4 | 74.62 20 | 89.03 8 | 87.89 9 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
APDe-MVS | | | 80.16 3 | 80.59 3 | 78.86 19 | 86.64 11 | 60.02 41 | 88.12 1 | 86.42 6 | 62.94 42 | 82.40 4 | 92.12 1 | 59.64 8 | 89.76 5 | 78.70 5 | 88.32 17 | 86.79 38 |
|
MVS_0304 | | | 76.73 30 | 76.04 31 | 78.78 20 | 81.32 74 | 58.89 55 | 82.50 51 | 84.07 28 | 67.73 5 | 72.08 61 | 87.28 56 | 49.49 75 | 89.57 6 | 73.52 29 | 86.40 41 | 87.87 11 |
|
3Dnovator+ | | 66.72 4 | 75.84 39 | 74.57 42 | 79.66 3 | 82.40 61 | 59.92 44 | 85.83 12 | 86.32 7 | 66.92 9 | 67.80 124 | 89.24 34 | 42.03 167 | 89.38 7 | 64.07 88 | 86.50 40 | 89.69 1 |
|
HSP-MVS | | | 80.69 2 | 81.20 1 | 79.14 9 | 86.21 18 | 62.73 12 | 86.09 10 | 85.03 14 | 65.51 15 | 83.81 1 | 90.51 13 | 63.71 3 | 89.23 8 | 81.51 1 | 88.44 13 | 85.45 80 |
|
NCCC | | | 78.58 9 | 78.31 10 | 79.39 5 | 87.51 5 | 62.61 16 | 85.20 19 | 84.42 21 | 66.73 10 | 74.67 30 | 89.38 32 | 55.30 25 | 89.18 9 | 74.19 22 | 87.34 29 | 86.38 43 |
|
CNVR-MVS | | | 79.84 5 | 79.97 5 | 79.45 4 | 87.90 2 | 62.17 20 | 84.37 22 | 85.03 14 | 66.96 6 | 77.58 12 | 90.06 22 | 59.47 10 | 89.13 10 | 78.67 6 | 89.73 5 | 87.03 33 |
|
DeepC-MVS_fast | | 68.24 3 | 77.25 23 | 76.63 26 | 79.12 11 | 86.15 20 | 60.86 33 | 84.71 20 | 84.85 18 | 61.98 61 | 73.06 50 | 88.88 40 | 53.72 41 | 89.06 11 | 68.27 49 | 88.04 23 | 87.42 24 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
DeepPCF-MVS | | 69.58 1 | 79.03 7 | 79.00 7 | 79.13 10 | 84.92 43 | 60.32 39 | 83.03 39 | 85.33 11 | 62.86 45 | 80.17 5 | 90.03 23 | 61.76 4 | 88.95 12 | 74.21 21 | 88.67 12 | 88.12 7 |
|
CANet | | | 76.46 32 | 75.93 33 | 78.06 30 | 81.29 75 | 57.53 70 | 82.35 52 | 83.31 52 | 67.78 3 | 70.09 75 | 86.34 71 | 54.92 28 | 88.90 13 | 72.68 32 | 84.55 49 | 87.76 14 |
|
PHI-MVS | | | 75.87 38 | 75.36 37 | 77.41 37 | 80.62 87 | 55.91 97 | 84.28 23 | 85.78 9 | 56.08 164 | 73.41 45 | 86.58 67 | 50.94 66 | 88.54 14 | 70.79 42 | 89.71 6 | 87.79 13 |
|
ACMMP_Plus | | | 78.77 8 | 78.78 8 | 78.74 21 | 85.44 31 | 61.04 31 | 83.84 30 | 85.16 12 | 62.88 44 | 78.10 10 | 91.26 5 | 52.51 50 | 88.39 15 | 79.34 3 | 90.52 1 | 86.78 39 |
|
HFP-MVS | | | 78.01 15 | 77.65 15 | 79.10 12 | 86.71 8 | 62.81 10 | 86.29 7 | 84.32 23 | 62.82 46 | 73.96 34 | 90.50 14 | 53.20 47 | 88.35 16 | 74.02 23 | 87.05 30 | 86.13 54 |
|
#test# | | | 77.83 16 | 77.41 18 | 79.10 12 | 86.71 8 | 62.81 10 | 85.69 16 | 84.32 23 | 61.61 64 | 73.96 34 | 90.50 14 | 53.20 47 | 88.35 16 | 73.68 26 | 87.05 30 | 86.13 54 |
|
APD-MVS | | | 78.02 14 | 78.04 14 | 77.98 32 | 86.44 15 | 60.81 34 | 85.52 17 | 84.36 22 | 60.61 75 | 79.05 8 | 90.30 18 | 55.54 24 | 88.32 18 | 73.48 30 | 87.03 32 | 84.83 105 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ACMMPR | | | 77.71 17 | 77.23 20 | 79.16 7 | 86.75 7 | 62.93 9 | 86.29 7 | 84.24 25 | 62.82 46 | 73.55 44 | 90.56 12 | 49.80 73 | 88.24 19 | 74.02 23 | 87.03 32 | 86.32 50 |
|
region2R | | | 77.67 19 | 77.18 21 | 79.15 8 | 86.76 6 | 62.95 8 | 86.29 7 | 84.16 27 | 62.81 48 | 73.30 46 | 90.58 11 | 49.90 71 | 88.21 20 | 73.78 25 | 87.03 32 | 86.29 52 |
|
PGM-MVS | | | 76.77 29 | 76.06 30 | 78.88 18 | 86.14 21 | 62.73 12 | 82.55 49 | 83.74 39 | 61.71 62 | 72.45 58 | 90.34 17 | 48.48 106 | 88.13 21 | 72.32 33 | 86.85 35 | 85.78 63 |
|
DP-MVS Recon | | | 72.15 74 | 70.73 81 | 76.40 50 | 86.57 12 | 57.99 65 | 81.15 73 | 82.96 58 | 57.03 140 | 66.78 135 | 85.56 85 | 44.50 150 | 88.11 22 | 51.77 173 | 80.23 86 | 83.10 161 |
|
test12 | | | | | 77.76 34 | 84.52 45 | 58.41 60 | | 83.36 50 | | 72.93 52 | | 54.61 32 | 88.05 23 | | 88.12 21 | 86.81 37 |
|
XVS | | | 77.17 24 | 76.56 27 | 79.00 15 | 86.32 16 | 62.62 14 | 85.83 12 | 83.92 32 | 64.55 22 | 72.17 59 | 90.01 24 | 47.95 111 | 88.01 24 | 71.55 38 | 86.74 37 | 86.37 46 |
|
X-MVStestdata | | | 70.21 105 | 67.28 141 | 79.00 15 | 86.32 16 | 62.62 14 | 85.83 12 | 83.92 32 | 64.55 22 | 72.17 59 | 6.49 350 | 47.95 111 | 88.01 24 | 71.55 38 | 86.74 37 | 86.37 46 |
|
CDPH-MVS | | | 76.31 33 | 75.67 36 | 78.22 28 | 85.35 34 | 59.14 51 | 81.31 71 | 84.02 29 | 56.32 158 | 74.05 33 | 88.98 38 | 53.34 45 | 87.92 26 | 69.23 46 | 88.42 14 | 87.59 18 |
|
API-MVS | | | 72.17 72 | 71.41 70 | 74.45 81 | 81.95 65 | 57.22 75 | 84.03 28 | 80.38 115 | 59.89 94 | 68.40 109 | 82.33 134 | 49.64 74 | 87.83 27 | 51.87 171 | 84.16 53 | 78.30 231 |
|
MG-MVS | | | 73.96 52 | 73.89 48 | 74.16 83 | 85.65 27 | 49.69 194 | 81.59 67 | 81.29 88 | 61.45 65 | 71.05 67 | 88.11 47 | 51.77 57 | 87.73 28 | 61.05 123 | 83.09 56 | 85.05 99 |
|
MCST-MVS | | | 77.48 21 | 77.45 17 | 77.54 35 | 86.67 10 | 58.36 61 | 83.22 37 | 86.93 1 | 56.91 143 | 74.91 26 | 88.19 46 | 59.15 11 | 87.68 29 | 73.67 27 | 87.45 28 | 86.57 42 |
|
TSAR-MVS + MP. | | | 78.44 11 | 78.28 11 | 78.90 17 | 84.96 39 | 61.41 27 | 84.03 28 | 83.82 38 | 59.34 115 | 79.37 7 | 89.76 28 | 59.84 6 | 87.62 30 | 76.69 11 | 86.74 37 | 87.68 16 |
|
HPM-MVS | | | 77.28 22 | 76.85 23 | 78.54 23 | 85.00 38 | 60.81 34 | 82.91 42 | 85.08 13 | 62.57 49 | 73.09 49 | 89.97 25 | 50.90 67 | 87.48 31 | 75.30 15 | 86.85 35 | 87.33 28 |
|
CP-MVS | | | 77.12 25 | 76.68 25 | 78.43 24 | 86.05 24 | 63.18 7 | 87.55 3 | 83.45 46 | 62.44 52 | 72.68 54 | 90.50 14 | 48.18 109 | 87.34 32 | 73.59 28 | 85.71 44 | 84.76 110 |
|
HPM-MVS++ | | | 79.88 4 | 80.14 4 | 79.10 12 | 88.17 1 | 64.80 1 | 86.59 4 | 83.70 40 | 65.37 16 | 78.78 9 | 90.64 9 | 58.63 13 | 87.24 33 | 79.00 4 | 90.37 2 | 85.26 93 |
|
TSAR-MVS + GP. | | | 74.90 43 | 74.15 46 | 77.17 40 | 82.00 64 | 58.77 57 | 81.80 60 | 78.57 154 | 58.58 123 | 74.32 32 | 84.51 102 | 55.94 21 | 87.22 34 | 67.11 59 | 84.48 51 | 85.52 75 |
|
HQP_MVS | | | 74.31 48 | 73.73 49 | 76.06 52 | 81.41 72 | 56.31 86 | 84.22 24 | 84.01 30 | 64.52 24 | 69.27 97 | 86.10 75 | 45.26 144 | 87.21 35 | 68.16 50 | 80.58 77 | 84.65 111 |
|
plane_prior5 | | | | | | | | | 84.01 30 | | | | | 87.21 35 | 68.16 50 | 80.58 77 | 84.65 111 |
|
ACMMP | | | 76.02 36 | 75.33 38 | 78.07 29 | 85.20 35 | 61.91 22 | 85.49 18 | 84.44 20 | 63.04 40 | 69.80 85 | 89.74 29 | 45.43 140 | 87.16 37 | 72.01 36 | 82.87 61 | 85.14 95 |
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 |
CLD-MVS | | | 73.33 57 | 72.68 59 | 75.29 68 | 78.82 113 | 53.33 125 | 78.23 111 | 84.79 19 | 61.30 68 | 70.41 71 | 81.04 170 | 52.41 53 | 87.12 38 | 64.61 78 | 82.49 64 | 85.41 87 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
114514_t | | | 70.83 86 | 69.56 96 | 74.64 77 | 86.21 18 | 54.63 112 | 82.34 53 | 81.81 75 | 48.22 250 | 63.01 178 | 85.83 81 | 40.92 188 | 87.10 39 | 57.91 137 | 79.79 91 | 82.18 175 |
|
AdaColmap | | | 69.99 109 | 68.66 113 | 73.97 86 | 84.94 41 | 57.83 66 | 82.63 47 | 78.71 151 | 56.28 160 | 64.34 167 | 84.14 106 | 41.57 175 | 87.06 40 | 46.45 207 | 78.88 104 | 77.02 248 |
|
HQP4-MVS | | | | | | | | | | | 67.85 119 | | | 86.93 41 | | | 84.32 118 |
|
HQP-MVS | | | 73.45 56 | 72.80 58 | 75.40 65 | 80.66 84 | 54.94 108 | 82.31 54 | 83.90 34 | 62.10 56 | 67.85 119 | 85.54 87 | 45.46 138 | 86.93 41 | 67.04 60 | 80.35 83 | 84.32 118 |
|
Regformer-2 | | | 75.63 40 | 74.99 39 | 77.54 35 | 80.43 89 | 58.32 62 | 79.50 94 | 82.92 59 | 67.84 1 | 75.94 17 | 80.75 182 | 55.73 22 | 86.80 43 | 71.44 40 | 80.38 81 | 87.50 20 |
|
DELS-MVS | | | 74.76 44 | 74.46 43 | 75.65 61 | 77.84 139 | 52.25 141 | 75.59 170 | 84.17 26 | 63.76 32 | 73.15 48 | 82.79 123 | 59.58 9 | 86.80 43 | 67.24 58 | 86.04 43 | 87.89 9 |
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 |
MP-MVS-pluss | | | 78.35 12 | 78.46 9 | 78.03 31 | 84.96 39 | 59.52 46 | 82.93 41 | 85.39 10 | 62.15 55 | 76.41 16 | 91.51 2 | 52.47 52 | 86.78 45 | 80.66 2 | 89.64 7 | 87.80 12 |
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss |
MP-MVS | | | 78.35 12 | 78.26 12 | 78.64 22 | 86.54 13 | 63.47 5 | 86.02 11 | 83.55 43 | 63.89 31 | 73.60 43 | 90.60 10 | 54.85 30 | 86.72 46 | 77.20 10 | 88.06 22 | 85.74 68 |
|
MPTG | | | 77.61 20 | 77.36 19 | 78.35 25 | 86.08 22 | 63.57 2 | 83.37 35 | 80.97 98 | 65.13 18 | 75.77 18 | 90.88 7 | 48.63 102 | 86.66 47 | 77.23 8 | 88.17 19 | 84.81 106 |
|
MTAPA | | | 76.90 27 | 76.42 28 | 78.35 25 | 86.08 22 | 63.57 2 | 74.92 186 | 80.97 98 | 65.13 18 | 75.77 18 | 90.88 7 | 48.63 102 | 86.66 47 | 77.23 8 | 88.17 19 | 84.81 106 |
|
abl_6 | | | 74.34 47 | 73.50 50 | 76.86 42 | 82.43 60 | 60.16 40 | 83.48 34 | 81.86 73 | 58.81 121 | 73.95 36 | 89.86 26 | 41.87 170 | 86.62 49 | 67.98 52 | 81.23 72 | 83.80 142 |
|
LPG-MVS_test | | | 72.74 63 | 71.74 66 | 75.76 56 | 80.22 92 | 57.51 71 | 82.55 49 | 83.40 48 | 61.32 66 | 66.67 136 | 87.33 54 | 39.15 198 | 86.59 50 | 67.70 54 | 77.30 121 | 83.19 157 |
|
LGP-MVS_train | | | | | 75.76 56 | 80.22 92 | 57.51 71 | | 83.40 48 | 61.32 66 | 66.67 136 | 87.33 54 | 39.15 198 | 86.59 50 | 67.70 54 | 77.30 121 | 83.19 157 |
|
CSCG | | | 76.92 26 | 76.75 24 | 77.41 37 | 83.96 50 | 59.60 45 | 82.95 40 | 86.50 5 | 60.78 73 | 75.27 21 | 84.83 93 | 60.76 5 | 86.56 52 | 67.86 53 | 87.87 27 | 86.06 57 |
|
mPP-MVS | | | 76.54 31 | 75.93 33 | 78.34 27 | 86.47 14 | 63.50 4 | 85.74 15 | 82.28 66 | 62.90 43 | 71.77 63 | 90.26 19 | 46.61 129 | 86.55 53 | 71.71 37 | 85.66 45 | 84.97 102 |
|
原ACMM1 | | | | | 74.69 74 | 85.39 33 | 59.40 47 | | 83.42 47 | 51.47 222 | 70.27 74 | 86.61 65 | 48.61 104 | 86.51 54 | 53.85 159 | 87.96 24 | 78.16 233 |
|
QAPM | | | 70.05 107 | 68.81 110 | 73.78 90 | 76.54 171 | 53.43 123 | 83.23 36 | 83.48 44 | 52.89 200 | 65.90 146 | 86.29 72 | 41.55 178 | 86.49 55 | 51.01 177 | 78.40 112 | 81.42 185 |
|
test_prior3 | | | 76.89 28 | 76.96 22 | 76.69 45 | 84.20 48 | 57.27 73 | 81.75 61 | 84.88 16 | 60.37 80 | 75.01 22 | 89.06 35 | 56.22 19 | 86.43 56 | 72.19 34 | 88.96 10 | 86.38 43 |
|
test_prior | | | | | 76.69 45 | 84.20 48 | 57.27 73 | | 84.88 16 | | | | | 86.43 56 | | | 86.38 43 |
|
EPP-MVSNet | | | 72.16 73 | 71.31 74 | 74.71 73 | 78.68 118 | 49.70 192 | 82.10 58 | 81.65 77 | 60.40 79 | 65.94 145 | 85.84 80 | 51.74 58 | 86.37 58 | 55.93 145 | 79.55 95 | 88.07 8 |
|
IB-MVS | | 56.42 12 | 65.40 192 | 62.73 203 | 73.40 114 | 74.89 188 | 52.78 132 | 73.09 209 | 75.13 200 | 55.69 170 | 58.48 246 | 73.73 282 | 32.86 265 | 86.32 59 | 50.63 179 | 70.11 207 | 81.10 198 |
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 |
Regformer-1 | | | 75.47 41 | 74.93 41 | 77.09 41 | 80.43 89 | 57.70 68 | 79.50 94 | 82.13 67 | 67.84 1 | 75.73 20 | 80.75 182 | 56.50 16 | 86.07 60 | 71.07 41 | 80.38 81 | 87.50 20 |
|
PAPM_NR | | | 72.63 64 | 71.80 65 | 75.13 69 | 81.72 67 | 53.42 124 | 79.91 86 | 83.28 54 | 59.14 117 | 66.31 142 | 85.90 79 | 51.86 56 | 86.06 61 | 57.45 139 | 80.62 75 | 85.91 60 |
|
PAPR | | | 71.72 78 | 70.82 80 | 74.41 82 | 81.20 79 | 51.17 151 | 79.55 93 | 83.33 51 | 55.81 168 | 66.93 134 | 84.61 98 | 50.95 65 | 86.06 61 | 55.79 148 | 79.20 101 | 86.00 58 |
|
ACMP | | 63.53 6 | 72.30 69 | 71.20 76 | 75.59 64 | 80.28 91 | 57.54 69 | 82.74 45 | 82.84 63 | 60.58 76 | 65.24 155 | 86.18 73 | 39.25 197 | 86.03 63 | 66.95 62 | 76.79 127 | 83.22 155 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
APD-MVS_3200maxsize | | | 74.96 42 | 74.39 44 | 76.67 47 | 82.20 62 | 58.24 63 | 83.67 31 | 83.29 53 | 58.41 127 | 73.71 41 | 90.14 21 | 45.62 135 | 85.99 64 | 69.64 44 | 82.85 62 | 85.78 63 |
|
Regformer-4 | | | 74.25 50 | 73.48 51 | 76.57 49 | 79.75 98 | 56.54 85 | 78.54 105 | 81.49 81 | 66.93 8 | 73.90 37 | 80.30 192 | 53.84 40 | 85.98 65 | 69.76 43 | 76.84 125 | 87.17 30 |
|
Effi-MVS+ | | | 73.31 58 | 72.54 60 | 75.62 62 | 77.87 138 | 53.64 119 | 79.62 92 | 79.61 124 | 61.63 63 | 72.02 62 | 82.61 128 | 56.44 17 | 85.97 66 | 63.99 91 | 79.07 103 | 87.25 29 |
|
DP-MVS | | | 65.68 186 | 63.66 193 | 71.75 155 | 84.93 42 | 56.87 83 | 80.74 77 | 73.16 218 | 53.06 198 | 59.09 238 | 82.35 133 | 36.79 229 | 85.94 67 | 32.82 286 | 69.96 211 | 72.45 295 |
|
OPM-MVS | | | 74.73 45 | 74.25 45 | 76.19 51 | 80.81 83 | 59.01 53 | 82.60 48 | 83.64 41 | 63.74 33 | 72.52 56 | 87.49 51 | 47.18 121 | 85.88 68 | 69.47 45 | 80.78 73 | 83.66 148 |
|
cascas | | | 65.98 185 | 63.42 195 | 73.64 100 | 77.26 159 | 52.58 135 | 72.26 222 | 77.21 179 | 48.56 245 | 61.21 213 | 74.60 277 | 32.57 274 | 85.82 69 | 50.38 181 | 76.75 128 | 82.52 170 |
|
IS-MVSNet | | | 71.57 79 | 71.00 78 | 73.27 119 | 78.86 112 | 45.63 227 | 80.22 82 | 78.69 152 | 64.14 29 | 66.46 138 | 87.36 53 | 49.30 79 | 85.60 70 | 50.26 182 | 83.71 54 | 88.59 3 |
|
HPM-MVS_fast | | | 74.30 49 | 73.46 53 | 76.80 44 | 84.45 47 | 59.04 52 | 83.65 32 | 81.05 94 | 60.15 86 | 70.43 70 | 89.84 27 | 41.09 185 | 85.59 71 | 67.61 56 | 82.90 60 | 85.77 65 |
|
SD-MVS | | | 77.70 18 | 77.62 16 | 77.93 33 | 84.47 46 | 61.88 23 | 84.55 21 | 83.87 36 | 60.37 80 | 79.89 6 | 89.38 32 | 54.97 27 | 85.58 72 | 76.12 12 | 84.94 47 | 86.33 49 |
|
mvs-test1 | | | 70.44 100 | 68.19 120 | 77.18 39 | 76.10 174 | 63.22 6 | 80.59 79 | 76.06 190 | 59.83 96 | 66.32 141 | 79.87 199 | 41.56 176 | 85.53 73 | 60.60 125 | 72.77 164 | 82.80 167 |
|
3Dnovator | | 64.47 5 | 72.49 66 | 71.39 71 | 75.79 55 | 77.70 141 | 58.99 54 | 80.66 78 | 83.15 56 | 62.24 54 | 65.46 150 | 86.59 66 | 42.38 165 | 85.52 74 | 59.59 133 | 84.72 48 | 82.85 166 |
|
MAR-MVS | | | 71.51 80 | 70.15 88 | 75.60 63 | 81.84 66 | 59.39 48 | 81.38 70 | 82.90 61 | 54.90 183 | 68.08 117 | 78.70 222 | 47.73 113 | 85.51 75 | 51.68 175 | 84.17 52 | 81.88 180 |
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 |
MVS | | | 67.37 160 | 66.33 166 | 70.51 179 | 75.46 183 | 50.94 152 | 73.95 197 | 81.85 74 | 41.57 304 | 62.54 186 | 78.57 227 | 47.98 110 | 85.47 76 | 52.97 166 | 82.05 66 | 75.14 265 |
|
EPNet | | | 73.09 60 | 72.16 62 | 75.90 54 | 75.95 177 | 56.28 88 | 83.05 38 | 72.39 221 | 66.53 13 | 65.27 153 | 87.00 57 | 50.40 69 | 85.47 76 | 62.48 105 | 86.32 42 | 85.94 59 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v7 | | | 70.57 93 | 69.48 102 | 73.85 87 | 73.50 215 | 50.92 154 | 78.27 109 | 81.43 82 | 58.93 118 | 69.61 87 | 81.49 160 | 47.56 116 | 85.43 78 | 63.94 92 | 70.62 190 | 85.21 94 |
|
v10 | | | 70.21 105 | 69.02 108 | 73.81 89 | 73.51 214 | 50.92 154 | 78.74 99 | 81.39 84 | 60.05 88 | 66.39 140 | 81.83 149 | 47.58 115 | 85.41 79 | 62.80 102 | 68.86 224 | 85.09 98 |
|
v1192 | | | 69.97 110 | 68.68 112 | 73.85 87 | 73.19 226 | 50.94 152 | 77.68 131 | 81.36 85 | 57.51 135 | 68.95 103 | 80.85 178 | 45.28 143 | 85.33 80 | 62.97 101 | 70.37 199 | 85.27 92 |
|
v1144 | | | 70.42 101 | 69.31 105 | 73.76 92 | 73.22 223 | 50.64 161 | 77.83 125 | 81.43 82 | 58.58 123 | 69.40 95 | 81.16 167 | 47.53 117 | 85.29 81 | 64.01 90 | 70.64 189 | 85.34 89 |
|
v1240 | | | 69.24 124 | 67.91 124 | 73.25 121 | 73.02 235 | 49.82 189 | 77.21 141 | 80.54 111 | 56.43 157 | 68.34 111 | 80.51 186 | 43.33 161 | 84.99 82 | 62.03 116 | 69.77 216 | 84.95 103 |
|
PAPM | | | 67.92 156 | 66.69 158 | 71.63 158 | 78.09 132 | 49.02 201 | 77.09 143 | 81.24 91 | 51.04 228 | 60.91 217 | 83.98 110 | 47.71 114 | 84.99 82 | 40.81 252 | 79.32 99 | 80.90 204 |
|
v1921920 | | | 69.47 119 | 68.17 121 | 73.36 115 | 73.06 233 | 50.10 184 | 77.39 136 | 80.56 110 | 56.58 155 | 68.59 105 | 80.37 188 | 44.72 146 | 84.98 84 | 62.47 106 | 69.82 213 | 85.00 100 |
|
v8 | | | 70.33 103 | 69.28 106 | 73.49 106 | 73.15 227 | 50.22 176 | 78.62 102 | 80.78 102 | 60.79 72 | 66.45 139 | 82.11 142 | 49.35 77 | 84.98 84 | 63.58 97 | 68.71 225 | 85.28 91 |
|
v144192 | | | 69.71 112 | 68.51 114 | 73.33 116 | 73.10 232 | 50.13 183 | 77.54 134 | 80.64 106 | 56.65 149 | 68.57 107 | 80.55 185 | 46.87 127 | 84.96 86 | 62.98 100 | 69.66 218 | 84.89 104 |
|
v1neww | | | 70.66 89 | 69.70 92 | 73.53 103 | 73.15 227 | 50.22 176 | 78.11 114 | 80.68 103 | 59.65 102 | 69.83 82 | 81.67 152 | 49.29 81 | 84.96 86 | 64.55 79 | 70.38 197 | 85.42 83 |
|
v7new | | | 70.66 89 | 69.70 92 | 73.53 103 | 73.15 227 | 50.22 176 | 78.11 114 | 80.68 103 | 59.65 102 | 69.83 82 | 81.67 152 | 49.29 81 | 84.96 86 | 64.55 79 | 70.38 197 | 85.42 83 |
|
v6 | | | 70.66 89 | 69.70 92 | 73.53 103 | 73.14 230 | 50.21 179 | 78.11 114 | 80.67 105 | 59.65 102 | 69.82 84 | 81.65 154 | 49.29 81 | 84.96 86 | 64.55 79 | 70.39 196 | 85.42 83 |
|
agg_prior3 | | | 76.13 35 | 75.89 35 | 76.85 43 | 85.76 25 | 62.02 21 | 81.65 63 | 81.01 97 | 55.51 174 | 73.73 40 | 88.60 45 | 53.23 46 | 84.90 90 | 75.24 17 | 88.33 15 | 83.65 149 |
|
EI-MVSNet-Vis-set | | | 72.42 68 | 71.59 67 | 74.91 70 | 78.47 123 | 54.02 115 | 77.05 144 | 79.33 141 | 65.03 20 | 71.68 64 | 79.35 216 | 52.75 49 | 84.89 91 | 66.46 63 | 74.23 142 | 85.83 62 |
|
PCF-MVS | | 61.88 8 | 70.95 85 | 69.49 101 | 75.35 66 | 77.63 144 | 55.71 99 | 76.04 165 | 81.81 75 | 50.30 233 | 69.66 86 | 85.40 90 | 52.51 50 | 84.89 91 | 51.82 172 | 80.24 85 | 85.45 80 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
v2v482 | | | 70.50 96 | 69.45 104 | 73.66 98 | 72.62 244 | 50.03 185 | 77.58 132 | 80.51 112 | 59.90 92 | 69.52 91 | 82.14 141 | 47.53 117 | 84.88 93 | 65.07 74 | 70.17 206 | 86.09 56 |
|
v1141 | | | 70.50 96 | 69.53 97 | 73.41 112 | 72.92 237 | 50.00 186 | 77.69 128 | 80.60 107 | 59.50 110 | 69.60 88 | 81.43 161 | 49.24 91 | 84.77 94 | 64.48 83 | 70.30 203 | 85.46 79 |
|
divwei89l23v2f112 | | | 70.50 96 | 69.53 97 | 73.41 112 | 72.91 238 | 50.00 186 | 77.69 128 | 80.59 108 | 59.50 110 | 69.60 88 | 81.43 161 | 49.26 86 | 84.77 94 | 64.48 83 | 70.31 202 | 85.47 77 |
|
v1 | | | 70.50 96 | 69.53 97 | 73.42 111 | 72.91 238 | 50.00 186 | 77.69 128 | 80.59 108 | 59.50 110 | 69.59 90 | 81.42 163 | 49.26 86 | 84.77 94 | 64.49 82 | 70.30 203 | 85.47 77 |
|
TEST9 | | | | | | 85.58 29 | 61.59 25 | 81.62 65 | 81.26 89 | 55.65 171 | 74.93 24 | 88.81 41 | 53.70 42 | 84.68 97 | | | |
|
train_agg | | | 76.27 34 | 76.15 29 | 76.64 48 | 85.58 29 | 61.59 25 | 81.62 65 | 81.26 89 | 55.86 165 | 74.93 24 | 88.81 41 | 53.70 42 | 84.68 97 | 75.24 17 | 88.33 15 | 83.65 149 |
|
EI-MVSNet-UG-set | | | 71.92 75 | 71.06 77 | 74.52 80 | 77.98 136 | 53.56 121 | 76.62 150 | 79.16 143 | 64.40 26 | 71.18 66 | 78.95 221 | 52.19 55 | 84.66 99 | 65.47 71 | 73.57 151 | 85.32 90 |
|
Regformer-3 | | | 73.89 53 | 73.28 55 | 75.71 59 | 79.75 98 | 55.48 105 | 78.54 105 | 79.93 119 | 66.58 12 | 73.62 42 | 80.30 192 | 54.87 29 | 84.54 100 | 69.09 47 | 76.84 125 | 87.10 32 |
|
v7n | | | 69.01 127 | 67.36 138 | 73.98 85 | 72.51 246 | 52.65 133 | 78.54 105 | 81.30 87 | 60.26 85 | 62.67 182 | 81.62 155 | 43.61 158 | 84.49 101 | 57.01 141 | 68.70 226 | 84.79 108 |
|
test_8 | | | | | | 85.40 32 | 60.96 32 | 81.54 68 | 81.18 92 | 55.86 165 | 74.81 27 | 88.80 43 | 53.70 42 | 84.45 102 | | | |
|
test_0402 | | | 63.25 211 | 61.01 225 | 69.96 185 | 80.00 96 | 54.37 114 | 76.86 148 | 72.02 223 | 54.58 186 | 58.71 241 | 80.79 180 | 35.00 243 | 84.36 103 | 26.41 326 | 64.71 257 | 71.15 308 |
|
PS-MVSNAJss | | | 72.24 70 | 71.21 75 | 75.31 67 | 78.50 121 | 55.93 96 | 81.63 64 | 82.12 68 | 56.24 161 | 70.02 79 | 85.68 84 | 47.05 122 | 84.34 104 | 65.27 72 | 74.41 141 | 85.67 70 |
|
DI_MVS_plusplus_test | | | 69.35 121 | 68.03 123 | 73.30 118 | 71.11 265 | 50.14 182 | 75.49 172 | 79.16 143 | 54.57 187 | 62.45 190 | 80.76 181 | 44.67 148 | 84.20 105 | 64.23 86 | 79.81 90 | 85.54 74 |
|
ACMH+ | | 57.40 11 | 66.12 183 | 64.06 186 | 72.30 150 | 77.79 140 | 52.83 131 | 80.39 80 | 78.03 166 | 57.30 136 | 57.47 258 | 82.55 130 | 27.68 298 | 84.17 106 | 45.54 218 | 69.78 214 | 79.90 216 |
|
OpenMVS | | 61.03 9 | 68.85 128 | 67.56 130 | 72.70 133 | 74.26 202 | 53.99 116 | 81.21 72 | 81.34 86 | 52.70 201 | 62.75 181 | 85.55 86 | 38.86 201 | 84.14 107 | 48.41 197 | 83.01 57 | 79.97 215 |
|
Fast-Effi-MVS+ | | | 70.28 104 | 69.12 107 | 73.73 95 | 78.50 121 | 51.50 150 | 75.01 183 | 79.46 137 | 56.16 162 | 68.59 105 | 79.55 212 | 53.97 37 | 84.05 108 | 53.34 164 | 77.53 117 | 85.65 72 |
|
agg_prior1 | | | 75.94 37 | 76.01 32 | 75.72 58 | 85.04 36 | 59.96 42 | 81.44 69 | 81.04 95 | 56.14 163 | 74.68 28 | 88.90 39 | 53.91 38 | 84.04 109 | 75.01 19 | 87.92 26 | 83.16 160 |
|
agg_prior | | | | | | 85.04 36 | 59.96 42 | | 81.04 95 | | 74.68 28 | | | 84.04 109 | | | |
|
EG-PatchMatch MVS | | | 64.71 198 | 62.87 200 | 70.22 181 | 77.68 142 | 53.48 122 | 77.99 121 | 78.82 148 | 53.37 196 | 56.03 267 | 77.41 250 | 24.75 316 | 84.04 109 | 46.37 208 | 73.42 154 | 73.14 288 |
|
Effi-MVS+-dtu | | | 69.64 115 | 67.53 132 | 75.95 53 | 76.10 174 | 62.29 18 | 80.20 83 | 76.06 190 | 59.83 96 | 65.26 154 | 77.09 251 | 41.56 176 | 84.02 112 | 60.60 125 | 71.09 187 | 81.53 183 |
|
MVS_111021_HR | | | 74.02 51 | 73.46 53 | 75.69 60 | 83.01 57 | 60.63 36 | 77.29 140 | 78.40 163 | 61.18 69 | 70.58 69 | 85.97 78 | 54.18 36 | 84.00 113 | 67.52 57 | 82.98 59 | 82.45 172 |
|
VDDNet | | | 71.81 76 | 71.33 73 | 73.26 120 | 82.80 59 | 47.60 215 | 78.74 99 | 75.27 197 | 59.59 108 | 72.94 51 | 89.40 31 | 41.51 179 | 83.91 114 | 58.75 135 | 82.99 58 | 88.26 5 |
|
test_normal | | | 69.26 123 | 67.90 125 | 73.32 117 | 70.84 268 | 50.38 171 | 75.30 175 | 79.17 142 | 54.23 191 | 62.00 197 | 80.61 184 | 44.69 147 | 83.89 115 | 64.33 85 | 79.95 89 | 85.69 69 |
|
BH-RMVSNet | | | 68.81 129 | 67.42 135 | 72.97 124 | 80.11 95 | 52.53 136 | 74.26 194 | 76.29 186 | 58.48 126 | 68.38 110 | 84.20 105 | 42.59 163 | 83.83 116 | 46.53 206 | 75.91 131 | 82.56 168 |
|
LFMVS | | | 71.78 77 | 71.59 67 | 72.32 149 | 83.40 53 | 46.38 223 | 79.75 89 | 71.08 225 | 64.18 28 | 72.80 53 | 88.64 44 | 42.58 164 | 83.72 117 | 57.41 140 | 84.49 50 | 86.86 36 |
|
TR-MVS | | | 66.59 179 | 65.07 182 | 71.17 168 | 79.18 106 | 49.63 196 | 73.48 205 | 75.20 199 | 52.95 199 | 67.90 118 | 80.33 191 | 39.81 192 | 83.68 118 | 43.20 237 | 73.56 152 | 80.20 212 |
|
MSLP-MVS++ | | | 73.77 55 | 73.47 52 | 74.66 75 | 83.02 56 | 59.29 50 | 82.30 57 | 81.88 72 | 59.34 115 | 71.59 65 | 86.83 58 | 45.94 133 | 83.65 119 | 65.09 73 | 85.22 46 | 81.06 199 |
|
NR-MVSNet | | | 69.54 117 | 68.85 109 | 71.59 159 | 78.05 134 | 43.81 242 | 74.20 195 | 80.86 101 | 65.18 17 | 62.76 180 | 84.52 100 | 52.35 54 | 83.59 120 | 50.96 178 | 70.78 188 | 87.37 25 |
|
BH-untuned | | | 68.27 146 | 67.29 140 | 71.21 166 | 79.74 100 | 53.22 126 | 76.06 163 | 77.46 176 | 57.19 137 | 66.10 143 | 81.61 156 | 45.37 142 | 83.50 121 | 45.42 222 | 76.68 129 | 76.91 251 |
|
UniMVSNet (Re) | | | 70.63 92 | 70.20 86 | 71.89 152 | 78.55 120 | 45.29 228 | 75.94 167 | 82.92 59 | 63.68 34 | 68.16 114 | 83.59 114 | 53.89 39 | 83.49 122 | 53.97 158 | 71.12 186 | 86.89 35 |
|
v52 | | | 67.09 166 | 65.16 180 | 72.87 127 | 66.77 299 | 51.60 148 | 73.69 203 | 79.45 138 | 57.88 132 | 62.46 189 | 78.57 227 | 40.95 187 | 83.34 123 | 61.99 117 | 64.70 259 | 83.68 145 |
|
V4 | | | 67.09 166 | 65.16 180 | 72.87 127 | 66.76 300 | 51.60 148 | 73.69 203 | 79.45 138 | 57.88 132 | 62.45 190 | 78.58 226 | 40.96 186 | 83.34 123 | 61.99 117 | 64.71 257 | 83.68 145 |
|
VDD-MVS | | | 72.50 65 | 72.09 63 | 73.75 94 | 81.58 68 | 49.69 194 | 77.76 127 | 77.63 172 | 63.21 38 | 73.21 47 | 89.02 37 | 42.14 166 | 83.32 125 | 61.72 120 | 82.50 63 | 88.25 6 |
|
UA-Net | | | 73.13 59 | 72.93 57 | 73.76 92 | 83.58 52 | 51.66 147 | 78.75 98 | 77.66 171 | 67.75 4 | 72.61 55 | 89.42 30 | 49.82 72 | 83.29 126 | 53.61 162 | 83.14 55 | 86.32 50 |
|
MVSFormer | | | 71.50 81 | 70.38 85 | 74.88 71 | 78.76 115 | 57.15 80 | 82.79 43 | 78.48 158 | 51.26 225 | 69.49 92 | 83.22 119 | 43.99 156 | 83.24 127 | 66.06 64 | 79.37 96 | 84.23 125 |
|
test_djsdf | | | 69.45 120 | 67.74 126 | 74.58 79 | 74.57 196 | 54.92 110 | 82.79 43 | 78.48 158 | 51.26 225 | 65.41 151 | 83.49 117 | 38.37 205 | 83.24 127 | 66.06 64 | 69.25 221 | 85.56 73 |
|
ACMM | | 61.98 7 | 70.80 88 | 69.73 91 | 74.02 84 | 80.59 88 | 58.59 59 | 82.68 46 | 82.02 71 | 55.46 175 | 67.18 131 | 84.39 104 | 38.51 203 | 83.17 129 | 60.65 124 | 76.10 130 | 80.30 211 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
testing_2 | | | 66.02 184 | 63.77 191 | 72.76 132 | 66.03 305 | 50.48 169 | 72.93 210 | 80.36 116 | 54.41 189 | 54.25 284 | 76.76 257 | 30.89 279 | 83.16 130 | 64.19 87 | 74.08 144 | 84.65 111 |
|
Test4 | | | 67.77 157 | 65.97 169 | 73.19 122 | 68.64 285 | 50.58 163 | 74.80 189 | 80.48 113 | 54.13 192 | 59.11 237 | 79.07 220 | 33.89 256 | 83.12 131 | 63.61 96 | 79.98 88 | 85.87 61 |
|
TranMVSNet+NR-MVSNet | | | 70.36 102 | 70.10 89 | 71.17 168 | 78.64 119 | 42.97 249 | 76.53 152 | 81.16 93 | 66.95 7 | 68.53 108 | 85.42 89 | 51.61 59 | 83.07 132 | 52.32 169 | 69.70 217 | 87.46 22 |
|
V42 | | | 68.65 133 | 67.35 139 | 72.56 137 | 68.93 284 | 50.18 180 | 72.90 211 | 79.47 136 | 56.92 142 | 69.45 94 | 80.26 194 | 46.29 131 | 82.99 133 | 64.07 88 | 67.82 238 | 84.53 114 |
|
SixPastTwentyTwo | | | 61.65 231 | 58.80 243 | 70.20 183 | 75.80 178 | 47.22 218 | 75.59 170 | 69.68 235 | 54.61 185 | 54.11 285 | 79.26 217 | 27.07 303 | 82.96 134 | 43.27 235 | 49.79 323 | 80.41 210 |
|
BH-w/o | | | 66.85 172 | 65.83 171 | 69.90 187 | 79.29 103 | 52.46 138 | 74.66 190 | 76.65 184 | 54.51 188 | 64.85 162 | 78.12 229 | 45.59 137 | 82.95 135 | 43.26 236 | 75.54 134 | 74.27 277 |
|
v18 | | | 68.33 143 | 66.96 149 | 72.42 146 | 73.13 231 | 50.16 181 | 77.97 122 | 79.57 133 | 59.57 109 | 61.80 201 | 77.50 248 | 49.30 79 | 82.90 136 | 62.31 109 | 61.50 281 | 84.20 131 |
|
v17 | | | 68.37 142 | 67.00 148 | 72.48 140 | 73.22 223 | 50.31 172 | 78.10 117 | 79.58 131 | 59.71 100 | 61.67 204 | 77.60 243 | 49.31 78 | 82.89 137 | 62.37 107 | 61.48 283 | 84.23 125 |
|
v16 | | | 68.38 141 | 67.01 147 | 72.47 144 | 73.22 223 | 50.29 173 | 78.10 117 | 79.59 129 | 59.71 100 | 61.72 203 | 77.60 243 | 49.28 84 | 82.89 137 | 62.36 108 | 61.54 280 | 84.23 125 |
|
PVSNet_BlendedMVS | | | 68.56 138 | 67.72 127 | 71.07 171 | 77.03 163 | 50.57 164 | 74.50 192 | 81.52 78 | 53.66 195 | 64.22 171 | 79.72 204 | 49.13 95 | 82.87 139 | 55.82 146 | 73.92 146 | 79.77 220 |
|
PVSNet_Blended | | | 68.59 134 | 67.72 127 | 71.19 167 | 77.03 163 | 50.57 164 | 72.51 218 | 81.52 78 | 51.91 209 | 64.22 171 | 77.77 237 | 49.13 95 | 82.87 139 | 55.82 146 | 79.58 93 | 80.14 214 |
|
UniMVSNet_NR-MVSNet | | | 71.11 84 | 71.00 78 | 71.44 160 | 79.20 105 | 44.13 238 | 76.02 166 | 82.60 64 | 66.48 14 | 68.20 112 | 84.60 99 | 56.82 15 | 82.82 141 | 54.62 154 | 70.43 194 | 87.36 27 |
|
DU-MVS | | | 70.01 108 | 69.53 97 | 71.44 160 | 78.05 134 | 44.13 238 | 75.01 183 | 81.51 80 | 64.37 27 | 68.20 112 | 84.52 100 | 49.12 97 | 82.82 141 | 54.62 154 | 70.43 194 | 87.37 25 |
|
v15 | | | 68.22 151 | 66.81 155 | 72.47 144 | 73.25 222 | 50.40 170 | 77.92 124 | 79.60 126 | 59.77 99 | 61.28 212 | 77.52 247 | 49.25 88 | 82.77 143 | 62.16 114 | 60.51 290 | 84.24 124 |
|
v13 | | | 68.29 144 | 66.84 151 | 72.63 134 | 73.50 215 | 50.83 157 | 78.25 110 | 79.58 131 | 60.05 88 | 60.76 219 | 77.68 240 | 49.11 100 | 82.77 143 | 62.17 112 | 60.45 294 | 84.30 120 |
|
v12 | | | 68.28 145 | 66.83 153 | 72.60 136 | 73.43 217 | 50.74 159 | 78.18 112 | 79.59 129 | 60.01 90 | 60.89 218 | 77.66 241 | 49.12 97 | 82.77 143 | 62.18 110 | 60.46 293 | 84.29 121 |
|
V14 | | | 68.25 148 | 66.82 154 | 72.52 139 | 73.33 221 | 50.53 167 | 78.02 120 | 79.60 126 | 59.83 96 | 61.16 214 | 77.57 245 | 49.19 92 | 82.77 143 | 62.18 110 | 60.50 291 | 84.26 123 |
|
V9 | | | 68.27 146 | 66.84 151 | 72.56 137 | 73.39 220 | 50.63 162 | 78.10 117 | 79.60 126 | 59.94 91 | 61.05 216 | 77.62 242 | 49.18 93 | 82.77 143 | 62.17 112 | 60.48 292 | 84.27 122 |
|
v11 | | | 68.15 154 | 66.73 156 | 72.42 146 | 73.43 217 | 50.28 174 | 77.94 123 | 79.65 123 | 59.88 95 | 61.11 215 | 77.55 246 | 48.25 108 | 82.75 148 | 61.88 119 | 60.85 287 | 84.23 125 |
|
MVP-Stereo | | | 65.41 191 | 63.80 190 | 70.22 181 | 77.62 148 | 55.53 103 | 76.30 156 | 78.53 156 | 50.59 232 | 56.47 265 | 78.65 224 | 39.84 191 | 82.68 149 | 44.10 229 | 72.12 178 | 72.44 296 |
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application. |
Vis-MVSNet | | | 72.18 71 | 71.37 72 | 74.61 78 | 81.29 75 | 55.41 106 | 80.90 74 | 78.28 165 | 60.73 74 | 69.23 100 | 88.09 48 | 44.36 153 | 82.65 150 | 57.68 138 | 81.75 69 | 85.77 65 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
PVSNet_Blended_VisFu | | | 71.45 82 | 70.39 84 | 74.65 76 | 82.01 63 | 58.82 56 | 79.93 85 | 80.35 117 | 55.09 179 | 65.82 149 | 82.16 140 | 49.17 94 | 82.64 151 | 60.34 127 | 78.62 110 | 82.50 171 |
|
EI-MVSNet | | | 69.27 122 | 68.44 117 | 71.73 156 | 74.47 197 | 49.39 198 | 75.20 179 | 78.45 160 | 59.60 105 | 69.16 101 | 76.51 261 | 51.29 60 | 82.50 152 | 59.86 132 | 71.45 184 | 83.30 153 |
|
MVSTER | | | 67.16 165 | 65.58 174 | 71.88 153 | 70.37 273 | 49.70 192 | 70.25 248 | 78.45 160 | 51.52 220 | 69.16 101 | 80.37 188 | 38.45 204 | 82.50 152 | 60.19 128 | 71.46 183 | 83.44 151 |
|
gm-plane-assit | | | | | | 71.40 263 | 41.72 257 | | | 48.85 244 | | 73.31 285 | | 82.48 154 | 48.90 193 | | |
|
LS3D | | | 64.71 198 | 62.50 205 | 71.34 164 | 79.72 102 | 55.71 99 | 79.82 87 | 74.72 206 | 48.50 247 | 56.62 263 | 84.62 97 | 33.59 259 | 82.34 155 | 29.65 310 | 75.23 135 | 75.97 255 |
|
PS-MVSNAJ | | | 70.51 95 | 69.70 92 | 72.93 125 | 81.52 69 | 55.79 98 | 74.92 186 | 79.00 146 | 55.04 181 | 69.88 81 | 78.66 223 | 47.05 122 | 82.19 156 | 61.61 121 | 79.58 93 | 80.83 205 |
|
v748 | | | 67.26 162 | 65.67 172 | 72.02 151 | 69.90 279 | 49.77 191 | 76.24 158 | 79.57 133 | 58.58 123 | 60.49 222 | 80.38 187 | 44.47 152 | 82.17 157 | 56.16 144 | 65.26 254 | 84.12 133 |
|
xiu_mvs_v2_base | | | 70.52 94 | 69.75 90 | 72.84 129 | 81.21 78 | 55.63 102 | 75.11 181 | 78.92 147 | 54.92 182 | 69.96 80 | 79.68 205 | 47.00 126 | 82.09 158 | 61.60 122 | 79.37 96 | 80.81 206 |
|
canonicalmvs | | | 74.67 46 | 74.98 40 | 73.71 96 | 78.94 111 | 50.56 166 | 80.23 81 | 83.87 36 | 60.30 84 | 77.15 13 | 86.56 68 | 59.65 7 | 82.00 159 | 66.01 66 | 82.12 65 | 88.58 4 |
|
v148 | | | 68.24 150 | 67.19 145 | 71.40 163 | 70.43 271 | 47.77 213 | 75.76 169 | 77.03 181 | 58.91 119 | 67.36 129 | 80.10 196 | 48.60 105 | 81.89 160 | 60.01 129 | 66.52 246 | 84.53 114 |
|
CPTT-MVS | | | 72.78 62 | 72.08 64 | 74.87 72 | 84.88 44 | 61.41 27 | 84.15 27 | 77.86 167 | 55.27 176 | 67.51 128 | 88.08 49 | 41.93 169 | 81.85 161 | 69.04 48 | 80.01 87 | 81.35 193 |
|
mvs_tets | | | 68.18 152 | 66.36 165 | 73.63 101 | 75.61 181 | 55.35 107 | 80.77 76 | 78.56 155 | 52.48 204 | 64.27 170 | 84.10 108 | 27.45 300 | 81.84 162 | 63.45 99 | 70.56 193 | 83.69 144 |
|
jajsoiax | | | 68.25 148 | 66.45 161 | 73.66 98 | 75.62 180 | 55.49 104 | 80.82 75 | 78.51 157 | 52.33 205 | 64.33 168 | 84.11 107 | 28.28 294 | 81.81 163 | 63.48 98 | 70.62 190 | 83.67 147 |
|
FIs | | | 70.82 87 | 71.43 69 | 68.98 197 | 78.33 125 | 38.14 281 | 76.96 146 | 83.59 42 | 61.02 70 | 67.33 130 | 86.73 59 | 55.07 26 | 81.64 164 | 54.61 156 | 79.22 100 | 87.14 31 |
|
HyFIR lowres test | | | 65.67 187 | 63.01 199 | 73.67 97 | 79.97 97 | 55.65 101 | 69.07 257 | 75.52 194 | 42.68 298 | 63.53 173 | 77.95 231 | 40.43 189 | 81.64 164 | 46.01 212 | 71.91 179 | 83.73 143 |
|
K. test v3 | | | 60.47 236 | 57.11 254 | 70.56 178 | 73.74 213 | 48.22 207 | 75.10 182 | 62.55 292 | 58.27 130 | 53.62 289 | 76.31 263 | 27.81 297 | 81.59 166 | 47.42 199 | 39.18 335 | 81.88 180 |
|
IterMVS-LS | | | 69.22 125 | 68.48 115 | 71.43 162 | 74.44 199 | 49.40 197 | 76.23 159 | 77.55 173 | 59.60 105 | 65.85 148 | 81.59 158 | 51.28 61 | 81.58 167 | 59.87 131 | 69.90 212 | 83.30 153 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
LTVRE_ROB | | 55.42 16 | 63.15 213 | 61.23 223 | 68.92 198 | 76.57 170 | 47.80 211 | 59.92 302 | 76.39 185 | 54.35 190 | 58.67 242 | 82.46 132 | 29.44 289 | 81.49 168 | 42.12 244 | 71.14 185 | 77.46 240 |
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 |
tpmp4_e23 | | | 62.71 220 | 60.13 229 | 70.45 180 | 73.40 219 | 48.39 206 | 72.82 212 | 69.49 239 | 44.88 278 | 59.91 226 | 74.99 273 | 37.79 212 | 81.47 169 | 40.22 254 | 67.71 240 | 81.48 184 |
|
lessismore_v0 | | | | | 69.91 186 | 71.42 262 | 47.80 211 | | 50.90 333 | | 50.39 303 | 75.56 270 | 27.43 301 | 81.33 170 | 45.91 213 | 34.10 338 | 80.59 207 |
|
OurMVSNet-221017-0 | | | 61.37 233 | 58.63 246 | 69.61 189 | 72.05 253 | 48.06 209 | 73.93 200 | 72.51 220 | 47.23 260 | 54.74 277 | 80.92 175 | 21.49 323 | 81.24 171 | 48.57 196 | 56.22 304 | 79.53 222 |
|
alignmvs | | | 73.86 54 | 73.99 47 | 73.45 108 | 78.20 128 | 50.50 168 | 78.57 103 | 82.43 65 | 59.40 113 | 76.57 14 | 86.71 61 | 56.42 18 | 81.23 172 | 65.84 68 | 81.79 67 | 88.62 2 |
|
MVS_Test | | | 72.45 67 | 72.46 61 | 72.42 146 | 74.88 189 | 48.50 205 | 76.28 157 | 83.14 57 | 59.40 113 | 72.46 57 | 84.68 95 | 55.66 23 | 81.12 173 | 65.98 67 | 79.66 92 | 87.63 17 |
|
GA-MVS | | | 65.53 189 | 63.70 192 | 71.02 172 | 70.87 267 | 48.10 208 | 70.48 244 | 74.40 209 | 56.69 148 | 64.70 164 | 76.77 256 | 33.66 258 | 81.10 174 | 55.42 151 | 70.32 201 | 83.87 138 |
|
MSDG | | | 61.81 230 | 59.23 233 | 69.55 193 | 72.64 243 | 52.63 134 | 70.45 245 | 75.81 192 | 51.38 223 | 53.70 287 | 76.11 264 | 29.52 287 | 81.08 175 | 37.70 264 | 65.79 250 | 74.93 270 |
|
ambc | | | | | 65.13 244 | 63.72 315 | 37.07 287 | 47.66 332 | 78.78 150 | | 54.37 283 | 71.42 293 | 11.24 341 | 80.94 176 | 45.64 216 | 53.85 313 | 77.38 241 |
|
ACMH | | 55.70 15 | 65.20 194 | 63.57 194 | 70.07 184 | 78.07 133 | 52.01 146 | 79.48 96 | 79.69 121 | 55.75 169 | 56.59 264 | 80.98 173 | 27.12 302 | 80.94 176 | 42.90 241 | 71.58 182 | 77.25 246 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
UGNet | | | 68.81 129 | 67.39 136 | 73.06 123 | 78.33 125 | 54.47 113 | 79.77 88 | 75.40 196 | 60.45 78 | 63.22 175 | 84.40 103 | 32.71 270 | 80.91 178 | 51.71 174 | 80.56 79 | 83.81 139 |
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 |
nrg030 | | | 72.96 61 | 73.01 56 | 72.84 129 | 75.41 184 | 50.24 175 | 80.02 84 | 82.89 62 | 58.36 129 | 74.44 31 | 86.73 59 | 58.90 12 | 80.83 179 | 65.84 68 | 74.46 139 | 87.44 23 |
|
tpm2 | | | 62.07 226 | 60.10 230 | 67.99 206 | 72.79 241 | 43.86 241 | 71.05 238 | 66.85 260 | 43.14 295 | 62.77 179 | 75.39 271 | 38.32 206 | 80.80 180 | 41.69 247 | 68.88 223 | 79.32 224 |
|
æ— å…ˆéªŒ | | | | | | | | 79.66 91 | 74.30 210 | 48.40 249 | | | | 80.78 181 | 53.62 160 | | 79.03 228 |
|
1121 | | | 68.53 139 | 67.16 146 | 72.63 134 | 85.64 28 | 61.14 29 | 73.95 197 | 66.46 262 | 44.61 282 | 70.28 73 | 86.68 62 | 41.42 180 | 80.78 181 | 53.62 160 | 81.79 67 | 75.97 255 |
|
FC-MVSNet-test | | | 69.80 111 | 70.58 83 | 67.46 210 | 77.61 149 | 34.73 306 | 76.05 164 | 83.19 55 | 60.84 71 | 65.88 147 | 86.46 69 | 54.52 33 | 80.76 183 | 52.52 168 | 78.12 113 | 86.91 34 |
|
OMC-MVS | | | 71.40 83 | 70.60 82 | 73.78 90 | 76.60 169 | 53.15 127 | 79.74 90 | 79.78 120 | 58.37 128 | 68.75 104 | 86.45 70 | 45.43 140 | 80.60 184 | 62.58 103 | 77.73 115 | 87.58 19 |
|
Fast-Effi-MVS+-dtu | | | 67.37 160 | 65.33 177 | 73.48 107 | 72.94 236 | 57.78 67 | 77.47 135 | 76.88 182 | 57.60 134 | 61.97 198 | 76.85 255 | 39.31 196 | 80.49 185 | 54.72 153 | 70.28 205 | 82.17 176 |
|
anonymousdsp | | | 67.00 170 | 64.82 184 | 73.57 102 | 70.09 275 | 56.13 91 | 76.35 155 | 77.35 178 | 48.43 248 | 64.99 161 | 80.84 179 | 33.01 263 | 80.34 186 | 64.66 76 | 67.64 241 | 84.23 125 |
|
GBi-Net | | | 67.21 163 | 66.55 159 | 69.19 194 | 77.63 144 | 43.33 245 | 77.31 137 | 77.83 168 | 56.62 152 | 65.04 158 | 82.70 124 | 41.85 171 | 80.33 187 | 47.18 201 | 72.76 165 | 83.92 135 |
|
test1 | | | 67.21 163 | 66.55 159 | 69.19 194 | 77.63 144 | 43.33 245 | 77.31 137 | 77.83 168 | 56.62 152 | 65.04 158 | 82.70 124 | 41.85 171 | 80.33 187 | 47.18 201 | 72.76 165 | 83.92 135 |
|
FMVSNet1 | | | 66.70 175 | 65.87 170 | 69.19 194 | 77.49 151 | 43.33 245 | 77.31 137 | 77.83 168 | 56.45 156 | 64.60 165 | 82.70 124 | 38.08 210 | 80.33 187 | 46.08 211 | 72.31 176 | 83.92 135 |
|
FMVSNet2 | | | 66.93 171 | 66.31 168 | 68.79 200 | 77.63 144 | 42.98 248 | 76.11 161 | 77.47 174 | 56.62 152 | 65.22 157 | 82.17 139 | 41.85 171 | 80.18 190 | 47.05 204 | 72.72 168 | 83.20 156 |
|
FMVSNet3 | | | 66.32 182 | 65.61 173 | 68.46 203 | 76.48 172 | 42.34 252 | 74.98 185 | 77.15 180 | 55.83 167 | 65.04 158 | 81.16 167 | 39.91 190 | 80.14 191 | 47.18 201 | 72.76 165 | 82.90 165 |
|
PLC | | 56.13 14 | 65.09 195 | 63.21 197 | 70.72 176 | 81.04 81 | 54.87 111 | 78.57 103 | 77.47 174 | 48.51 246 | 55.71 268 | 81.89 148 | 33.71 257 | 79.71 192 | 41.66 248 | 70.37 199 | 77.58 239 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
OpenMVS_ROB | | 52.78 18 | 60.03 237 | 58.14 249 | 65.69 235 | 70.47 270 | 44.82 230 | 75.33 174 | 70.86 227 | 45.04 277 | 56.06 266 | 76.00 265 | 26.89 305 | 79.65 193 | 35.36 278 | 67.29 242 | 72.60 292 |
|
CostFormer | | | 64.04 203 | 62.51 204 | 68.61 202 | 71.88 256 | 45.77 226 | 71.30 234 | 70.60 229 | 47.55 257 | 64.31 169 | 76.61 259 | 41.63 174 | 79.62 194 | 49.74 186 | 69.00 222 | 80.42 209 |
|
WR-MVS_H | | | 67.02 169 | 66.92 150 | 67.33 213 | 77.95 137 | 37.75 284 | 77.57 133 | 82.11 69 | 62.03 60 | 62.65 183 | 82.48 131 | 50.57 68 | 79.46 195 | 42.91 240 | 64.01 263 | 84.79 108 |
|
COLMAP_ROB | | 52.97 17 | 61.27 234 | 58.81 236 | 68.64 201 | 74.63 194 | 52.51 137 | 78.42 108 | 73.30 216 | 49.92 237 | 50.96 299 | 81.51 159 | 23.06 319 | 79.40 196 | 31.63 295 | 65.85 248 | 74.01 284 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
1314 | | | 64.61 200 | 63.21 197 | 68.80 199 | 71.87 257 | 47.46 216 | 73.95 197 | 78.39 164 | 42.88 297 | 59.97 225 | 76.60 260 | 38.11 209 | 79.39 197 | 54.84 152 | 72.32 175 | 79.55 221 |
|
XVG-ACMP-BASELINE | | | 64.36 202 | 62.23 209 | 70.74 175 | 72.35 248 | 52.45 139 | 70.80 241 | 78.45 160 | 53.84 194 | 59.87 227 | 81.10 169 | 16.24 329 | 79.32 198 | 55.64 150 | 71.76 180 | 80.47 208 |
|
lupinMVS | | | 69.57 116 | 68.28 119 | 73.44 109 | 78.76 115 | 57.15 80 | 76.57 151 | 73.29 217 | 46.19 268 | 69.49 92 | 82.18 137 | 43.99 156 | 79.23 199 | 64.66 76 | 79.37 96 | 83.93 134 |
|
jason | | | 69.65 114 | 68.39 118 | 73.43 110 | 78.27 127 | 56.88 82 | 77.12 142 | 73.71 215 | 46.53 264 | 69.34 96 | 83.22 119 | 43.37 160 | 79.18 200 | 64.77 75 | 79.20 101 | 84.23 125 |
jason: jason. |
conf200view11 | | | 63.38 207 | 62.41 206 | 66.29 223 | 77.31 153 | 38.66 276 | 72.65 213 | 69.11 244 | 57.07 138 | 62.45 190 | 81.03 171 | 37.01 221 | 79.17 201 | 31.84 290 | 73.25 157 | 81.03 200 |
|
thres100view900 | | | 63.28 210 | 62.41 206 | 65.89 232 | 77.31 153 | 38.66 276 | 72.65 213 | 69.11 244 | 57.07 138 | 62.45 190 | 81.03 171 | 37.01 221 | 79.17 201 | 31.84 290 | 73.25 157 | 79.83 217 |
|
tfpn200view9 | | | 63.18 212 | 62.18 210 | 66.21 225 | 76.85 166 | 39.62 267 | 71.96 230 | 69.44 240 | 56.63 150 | 62.61 184 | 79.83 200 | 37.18 216 | 79.17 201 | 31.84 290 | 73.25 157 | 79.83 217 |
|
thres400 | | | 63.31 208 | 62.18 210 | 66.72 216 | 76.85 166 | 39.62 267 | 71.96 230 | 69.44 240 | 56.63 150 | 62.61 184 | 79.83 200 | 37.18 216 | 79.17 201 | 31.84 290 | 73.25 157 | 81.36 187 |
|
DTE-MVSNet | | | 65.58 188 | 65.34 176 | 66.31 221 | 76.06 176 | 34.79 304 | 76.43 154 | 79.38 140 | 62.55 50 | 61.66 205 | 83.83 111 | 45.60 136 | 79.15 205 | 41.64 250 | 60.88 286 | 85.00 100 |
|
WR-MVS | | | 68.47 140 | 68.47 116 | 68.44 204 | 80.20 94 | 39.84 265 | 73.75 202 | 76.07 189 | 64.68 21 | 68.11 116 | 83.63 113 | 50.39 70 | 79.14 206 | 49.78 184 | 69.66 218 | 86.34 48 |
|
PEN-MVS | | | 66.60 177 | 66.45 161 | 67.04 214 | 77.11 161 | 36.56 295 | 77.03 145 | 80.42 114 | 62.95 41 | 62.51 188 | 84.03 109 | 46.69 128 | 79.07 207 | 44.22 226 | 63.08 271 | 85.51 76 |
|
xiu_mvs_v1_base_debu | | | 68.58 135 | 67.28 141 | 72.48 140 | 78.19 129 | 57.19 77 | 75.28 176 | 75.09 201 | 51.61 217 | 70.04 76 | 81.41 164 | 32.79 266 | 79.02 208 | 63.81 93 | 77.31 118 | 81.22 195 |
|
xiu_mvs_v1_base | | | 68.58 135 | 67.28 141 | 72.48 140 | 78.19 129 | 57.19 77 | 75.28 176 | 75.09 201 | 51.61 217 | 70.04 76 | 81.41 164 | 32.79 266 | 79.02 208 | 63.81 93 | 77.31 118 | 81.22 195 |
|
xiu_mvs_v1_base_debi | | | 68.58 135 | 67.28 141 | 72.48 140 | 78.19 129 | 57.19 77 | 75.28 176 | 75.09 201 | 51.61 217 | 70.04 76 | 81.41 164 | 32.79 266 | 79.02 208 | 63.81 93 | 77.31 118 | 81.22 195 |
|
diffmvs | | | 67.72 158 | 66.73 156 | 70.70 177 | 69.74 281 | 47.69 214 | 73.33 206 | 74.74 205 | 53.30 197 | 64.51 166 | 81.80 150 | 49.25 88 | 79.02 208 | 59.15 134 | 74.75 137 | 85.39 88 |
|
thres600view7 | | | 63.30 209 | 62.27 208 | 66.41 220 | 77.18 160 | 38.87 273 | 72.35 220 | 69.11 244 | 56.98 141 | 62.37 195 | 80.96 174 | 37.01 221 | 79.00 212 | 31.43 298 | 73.05 162 | 81.36 187 |
|
thres200 | | | 62.20 224 | 61.16 224 | 65.34 242 | 75.38 185 | 39.99 264 | 69.60 252 | 69.29 242 | 55.64 172 | 61.87 200 | 76.99 252 | 37.07 220 | 78.96 213 | 31.28 299 | 73.28 156 | 77.06 247 |
|
TAPA-MVS | | 59.36 10 | 66.60 177 | 65.20 179 | 70.81 173 | 76.63 168 | 48.75 203 | 76.52 153 | 80.04 118 | 50.64 231 | 65.24 155 | 84.93 92 | 39.15 198 | 78.54 214 | 36.77 269 | 76.88 124 | 85.14 95 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
view600 | | | 62.77 216 | 61.84 213 | 65.55 236 | 77.28 155 | 36.87 289 | 72.15 224 | 67.78 251 | 56.79 144 | 61.46 208 | 81.92 144 | 36.88 224 | 78.42 215 | 29.86 305 | 72.46 169 | 81.36 187 |
|
view800 | | | 62.77 216 | 61.84 213 | 65.55 236 | 77.28 155 | 36.87 289 | 72.15 224 | 67.78 251 | 56.79 144 | 61.46 208 | 81.92 144 | 36.88 224 | 78.42 215 | 29.86 305 | 72.46 169 | 81.36 187 |
|
conf0.05thres1000 | | | 62.77 216 | 61.84 213 | 65.55 236 | 77.28 155 | 36.87 289 | 72.15 224 | 67.78 251 | 56.79 144 | 61.46 208 | 81.92 144 | 36.88 224 | 78.42 215 | 29.86 305 | 72.46 169 | 81.36 187 |
|
tfpn | | | 62.77 216 | 61.84 213 | 65.55 236 | 77.28 155 | 36.87 289 | 72.15 224 | 67.78 251 | 56.79 144 | 61.46 208 | 81.92 144 | 36.88 224 | 78.42 215 | 29.86 305 | 72.46 169 | 81.36 187 |
|
PS-CasMVS | | | 66.42 181 | 66.32 167 | 66.70 218 | 77.60 150 | 36.30 299 | 76.94 147 | 79.61 124 | 62.36 53 | 62.43 194 | 83.66 112 | 45.69 134 | 78.37 219 | 45.35 223 | 63.26 269 | 85.42 83 |
|
CP-MVSNet | | | 66.49 180 | 66.41 164 | 66.72 216 | 77.67 143 | 36.33 297 | 76.83 149 | 79.52 135 | 62.45 51 | 62.54 186 | 83.47 118 | 46.32 130 | 78.37 219 | 45.47 221 | 63.43 268 | 85.45 80 |
|
XVG-OURS | | | 68.76 132 | 67.37 137 | 72.90 126 | 74.32 201 | 57.22 75 | 70.09 249 | 78.81 149 | 55.24 177 | 67.79 125 | 85.81 83 | 36.54 231 | 78.28 221 | 62.04 115 | 75.74 132 | 83.19 157 |
|
XVG-OURS-SEG-HR | | | 68.81 129 | 67.47 134 | 72.82 131 | 74.40 200 | 56.87 83 | 70.59 242 | 79.04 145 | 54.77 184 | 66.99 133 | 86.01 77 | 39.57 195 | 78.21 222 | 62.54 104 | 73.33 155 | 83.37 152 |
|
F-COLMAP | | | 63.05 214 | 60.87 226 | 69.58 192 | 76.99 165 | 53.63 120 | 78.12 113 | 76.16 187 | 47.97 254 | 52.41 294 | 81.61 156 | 27.87 296 | 78.11 223 | 40.07 255 | 66.66 244 | 77.00 249 |
|
TransMVSNet (Re) | | | 64.72 197 | 64.33 185 | 65.87 233 | 75.22 186 | 38.56 278 | 74.66 190 | 75.08 204 | 58.90 120 | 61.79 202 | 82.63 127 | 51.18 62 | 78.07 224 | 43.63 233 | 55.87 305 | 80.99 203 |
|
mvs_anonymous | | | 68.03 155 | 67.51 133 | 69.59 190 | 72.08 252 | 44.57 235 | 71.99 229 | 75.23 198 | 51.67 216 | 67.06 132 | 82.57 129 | 54.68 31 | 77.94 225 | 56.56 142 | 75.71 133 | 86.26 53 |
|
GG-mvs-BLEND | | | | | 62.34 262 | 71.36 264 | 37.04 288 | 69.20 256 | 57.33 311 | | 54.73 278 | 65.48 318 | 30.37 281 | 77.82 226 | 34.82 279 | 74.93 136 | 72.17 302 |
|
CHOSEN 1792x2688 | | | 65.08 196 | 62.84 201 | 71.82 154 | 81.49 71 | 56.26 89 | 66.32 273 | 74.20 211 | 40.53 310 | 63.16 177 | 78.65 224 | 41.30 181 | 77.80 227 | 45.80 214 | 74.09 143 | 81.40 186 |
|
DWT-MVSNet_test | | | 61.90 227 | 59.93 231 | 67.83 207 | 71.98 255 | 46.09 225 | 71.03 239 | 69.71 233 | 50.09 234 | 58.51 245 | 70.62 297 | 30.21 284 | 77.63 228 | 49.28 190 | 67.91 236 | 79.78 219 |
|
VPA-MVSNet | | | 69.02 126 | 69.47 103 | 67.69 209 | 77.42 152 | 41.00 262 | 74.04 196 | 79.68 122 | 60.06 87 | 69.26 99 | 84.81 94 | 51.06 64 | 77.58 229 | 54.44 157 | 74.43 140 | 84.48 116 |
|
MS-PatchMatch | | | 62.42 222 | 61.46 220 | 65.31 243 | 75.21 187 | 52.10 142 | 72.05 228 | 74.05 212 | 46.41 266 | 57.42 259 | 74.36 278 | 34.35 251 | 77.57 230 | 45.62 217 | 73.67 148 | 66.26 319 |
|
CANet_DTU | | | 68.18 152 | 67.71 129 | 69.59 190 | 74.83 190 | 46.24 224 | 78.66 101 | 76.85 183 | 59.60 105 | 63.45 174 | 82.09 143 | 35.25 242 | 77.41 231 | 59.88 130 | 78.76 107 | 85.14 95 |
|
TAMVS | | | 66.78 174 | 65.27 178 | 71.33 165 | 79.16 108 | 53.67 118 | 73.84 201 | 69.59 237 | 52.32 206 | 65.28 152 | 81.72 151 | 44.49 151 | 77.40 232 | 42.32 243 | 78.66 109 | 82.92 163 |
|
Baseline_NR-MVSNet | | | 67.05 168 | 67.56 130 | 65.50 240 | 75.65 179 | 37.70 285 | 75.42 173 | 74.65 207 | 59.90 92 | 68.14 115 | 83.15 122 | 49.12 97 | 77.20 233 | 52.23 170 | 69.78 214 | 81.60 182 |
|
CDS-MVSNet | | | 66.80 173 | 65.37 175 | 71.10 170 | 78.98 110 | 53.13 129 | 73.27 207 | 71.07 226 | 52.15 207 | 64.72 163 | 80.23 195 | 43.56 159 | 77.10 234 | 45.48 220 | 78.88 104 | 83.05 162 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
VNet | | | 69.68 113 | 70.19 87 | 68.16 205 | 79.73 101 | 41.63 258 | 70.53 243 | 77.38 177 | 60.37 80 | 70.69 68 | 86.63 64 | 51.08 63 | 77.09 235 | 53.61 162 | 81.69 71 | 85.75 67 |
|
pmmvs6 | | | 63.69 205 | 62.82 202 | 66.27 224 | 70.63 269 | 39.27 271 | 73.13 208 | 75.47 195 | 52.69 202 | 59.75 230 | 82.30 135 | 39.71 193 | 77.03 236 | 47.40 200 | 64.35 262 | 82.53 169 |
|
tfpnnormal | | | 62.47 221 | 61.63 219 | 64.99 245 | 74.81 191 | 39.01 272 | 71.22 235 | 73.72 214 | 55.22 178 | 60.21 223 | 80.09 197 | 41.26 184 | 76.98 237 | 30.02 304 | 68.09 235 | 78.97 229 |
|
LCM-MVSNet-Re | | | 61.88 229 | 61.35 221 | 63.46 252 | 74.58 195 | 31.48 326 | 61.42 296 | 58.14 307 | 58.71 122 | 53.02 293 | 79.55 212 | 43.07 162 | 76.80 238 | 45.69 215 | 77.96 114 | 82.11 177 |
|
Anonymous20231211 | | | 55.92 270 | 53.63 279 | 62.77 260 | 68.22 291 | 35.56 302 | 74.48 193 | 69.89 232 | 46.42 265 | 49.07 306 | 73.45 284 | 21.13 324 | 76.77 239 | 28.74 319 | 51.30 319 | 75.97 255 |
|
HY-MVS | | 56.14 13 | 64.55 201 | 63.89 188 | 66.55 219 | 74.73 193 | 41.02 260 | 69.96 250 | 74.43 208 | 49.29 239 | 61.66 205 | 80.92 175 | 47.43 120 | 76.68 240 | 44.91 225 | 71.69 181 | 81.94 178 |
|
VPNet | | | 67.52 159 | 68.11 122 | 65.74 234 | 79.18 106 | 36.80 293 | 72.17 223 | 72.83 219 | 62.04 59 | 67.79 125 | 85.83 81 | 48.88 101 | 76.60 241 | 51.30 176 | 72.97 163 | 83.81 139 |
|
pm-mvs1 | | | 65.24 193 | 64.97 183 | 66.04 228 | 72.38 247 | 39.40 270 | 72.62 216 | 75.63 193 | 55.53 173 | 62.35 196 | 83.18 121 | 47.45 119 | 76.47 242 | 49.06 192 | 66.54 245 | 82.24 174 |
|
gg-mvs-nofinetune | | | 57.86 259 | 56.43 261 | 62.18 263 | 72.62 244 | 35.35 303 | 66.57 270 | 56.33 316 | 50.65 230 | 57.64 257 | 57.10 332 | 30.65 280 | 76.36 243 | 37.38 266 | 78.88 104 | 74.82 272 |
|
MVS_111021_LR | | | 69.50 118 | 68.78 111 | 71.65 157 | 78.38 124 | 59.33 49 | 74.82 188 | 70.11 231 | 58.08 131 | 67.83 123 | 84.68 95 | 41.96 168 | 76.34 244 | 65.62 70 | 77.54 116 | 79.30 225 |
|
tpmvs | | | 58.47 254 | 56.95 257 | 63.03 259 | 70.20 274 | 41.21 259 | 67.90 268 | 67.23 258 | 49.62 238 | 54.73 278 | 70.84 296 | 34.14 252 | 76.24 245 | 36.64 272 | 61.29 284 | 71.64 304 |
|
ab-mvs | | | 66.65 176 | 66.42 163 | 67.37 211 | 76.17 173 | 41.73 256 | 70.41 246 | 76.14 188 | 53.99 193 | 65.98 144 | 83.51 116 | 49.48 76 | 76.24 245 | 48.60 195 | 73.46 153 | 84.14 132 |
|
tfpn_ndepth | | | 59.57 245 | 59.02 235 | 61.23 270 | 73.81 211 | 35.60 301 | 69.40 255 | 65.59 266 | 50.96 229 | 57.96 255 | 77.72 238 | 34.81 244 | 75.91 247 | 30.36 302 | 70.57 192 | 72.18 301 |
|
Vis-MVSNet (Re-imp) | | | 63.69 205 | 63.88 189 | 63.14 257 | 74.75 192 | 31.04 327 | 71.16 237 | 63.64 284 | 56.32 158 | 59.80 229 | 84.99 91 | 44.51 149 | 75.46 248 | 39.12 259 | 80.62 75 | 82.92 163 |
|
conf0.01 | | | 59.97 238 | 58.81 236 | 63.42 253 | 74.15 204 | 33.83 312 | 68.32 260 | 64.22 275 | 51.79 210 | 58.04 249 | 79.57 206 | 35.41 235 | 75.41 249 | 29.57 311 | 68.26 228 | 81.03 200 |
|
conf0.002 | | | 59.97 238 | 58.81 236 | 63.42 253 | 74.15 204 | 33.83 312 | 68.32 260 | 64.22 275 | 51.79 210 | 58.04 249 | 79.57 206 | 35.41 235 | 75.41 249 | 29.57 311 | 68.26 228 | 81.03 200 |
|
thresconf0.02 | | | 59.40 246 | 58.81 236 | 61.17 271 | 74.15 204 | 33.83 312 | 68.32 260 | 64.22 275 | 51.79 210 | 58.04 249 | 79.57 206 | 35.41 235 | 75.41 249 | 29.57 311 | 68.26 228 | 74.25 278 |
|
tfpn_n400 | | | 59.40 246 | 58.81 236 | 61.17 271 | 74.15 204 | 33.83 312 | 68.32 260 | 64.22 275 | 51.79 210 | 58.04 249 | 79.57 206 | 35.41 235 | 75.41 249 | 29.57 311 | 68.26 228 | 74.25 278 |
|
tfpnconf | | | 59.40 246 | 58.81 236 | 61.17 271 | 74.15 204 | 33.83 312 | 68.32 260 | 64.22 275 | 51.79 210 | 58.04 249 | 79.57 206 | 35.41 235 | 75.41 249 | 29.57 311 | 68.26 228 | 74.25 278 |
|
tfpnview11 | | | 59.40 246 | 58.81 236 | 61.17 271 | 74.15 204 | 33.83 312 | 68.32 260 | 64.22 275 | 51.79 210 | 58.04 249 | 79.57 206 | 35.41 235 | 75.41 249 | 29.57 311 | 68.26 228 | 74.25 278 |
|
tfpn1000 | | | 59.24 251 | 58.70 244 | 60.86 276 | 73.75 212 | 33.99 310 | 68.86 258 | 63.98 282 | 51.25 227 | 57.29 260 | 79.51 214 | 34.58 246 | 75.26 255 | 29.08 318 | 69.99 209 | 73.32 287 |
|
æ–°å‡ ä½•1 | | | | | 70.76 174 | 85.66 26 | 61.13 30 | | 66.43 263 | 44.68 281 | 70.29 72 | 86.64 63 | 41.29 182 | 75.23 256 | 49.72 187 | 81.75 69 | 75.93 258 |
|
PatchFormer-LS_test | | | 62.20 224 | 60.59 227 | 67.04 214 | 72.18 251 | 46.82 221 | 70.36 247 | 68.62 248 | 51.92 208 | 59.19 236 | 70.23 300 | 36.86 228 | 75.07 257 | 50.23 183 | 65.68 251 | 79.23 226 |
|
USDC | | | 56.35 267 | 54.24 275 | 62.69 261 | 64.74 310 | 40.31 263 | 65.05 282 | 73.83 213 | 43.93 289 | 47.58 308 | 77.71 239 | 15.36 331 | 75.05 258 | 38.19 263 | 61.81 278 | 72.70 291 |
|
pmmvs4 | | | 61.48 232 | 59.39 232 | 67.76 208 | 71.57 259 | 53.86 117 | 71.42 232 | 65.34 267 | 44.20 286 | 59.46 231 | 77.92 233 | 35.90 232 | 74.71 259 | 43.87 231 | 64.87 256 | 74.71 274 |
|
tpm cat1 | | | 59.25 250 | 56.95 257 | 66.15 226 | 72.19 250 | 46.96 219 | 68.09 267 | 65.76 264 | 40.03 313 | 57.81 256 | 70.56 298 | 38.32 206 | 74.51 260 | 38.26 262 | 61.50 281 | 77.00 249 |
|
patchmatchnet-post | | | | | | | | | | | | 64.03 319 | 34.50 248 | 74.27 261 | | | |
|
1112_ss | | | 64.00 204 | 63.36 196 | 65.93 231 | 79.28 104 | 42.58 251 | 71.35 233 | 72.36 222 | 46.41 266 | 60.55 221 | 77.89 234 | 46.27 132 | 73.28 262 | 46.18 209 | 69.97 210 | 81.92 179 |
|
TinyColmap | | | 54.14 277 | 51.72 285 | 61.40 268 | 66.84 298 | 41.97 253 | 66.52 271 | 68.51 249 | 44.81 279 | 42.69 327 | 75.77 268 | 11.66 339 | 72.94 263 | 31.96 288 | 56.77 303 | 69.27 315 |
|
pmmvs-eth3d | | | 58.81 252 | 56.31 262 | 66.30 222 | 67.61 293 | 52.42 140 | 72.30 221 | 64.76 271 | 43.55 291 | 54.94 276 | 74.19 280 | 28.95 291 | 72.60 264 | 43.31 234 | 57.21 302 | 73.88 285 |
|
IterMVS | | | 62.79 215 | 61.27 222 | 67.35 212 | 69.37 282 | 52.04 145 | 71.17 236 | 68.24 250 | 52.63 203 | 59.82 228 | 76.91 254 | 37.32 215 | 72.36 265 | 52.80 167 | 63.19 270 | 77.66 238 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Patchmatch-RL test | | | 58.16 257 | 55.49 267 | 66.15 226 | 67.92 292 | 48.89 202 | 60.66 301 | 51.07 332 | 47.86 255 | 59.36 232 | 62.71 325 | 34.02 254 | 72.27 266 | 56.41 143 | 59.40 297 | 77.30 242 |
|
testdata2 | | | | | | | | | | | | | | 72.18 267 | 46.95 205 | | |
|
CMPMVS | | 42.80 21 | 57.81 260 | 55.97 264 | 63.32 255 | 60.98 325 | 47.38 217 | 64.66 284 | 69.50 238 | 32.06 331 | 46.83 312 | 77.80 236 | 29.50 288 | 71.36 268 | 48.68 194 | 73.75 147 | 71.21 307 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
Test_1112_low_res | | | 62.32 223 | 61.77 217 | 64.00 251 | 79.08 109 | 39.53 269 | 68.17 266 | 70.17 230 | 43.25 293 | 59.03 239 | 79.90 198 | 44.08 154 | 71.24 269 | 43.79 232 | 68.42 227 | 81.25 194 |
|
CNLPA | | | 65.43 190 | 64.02 187 | 69.68 188 | 78.73 117 | 58.07 64 | 77.82 126 | 70.71 228 | 51.49 221 | 61.57 207 | 83.58 115 | 38.23 208 | 70.82 270 | 43.90 230 | 70.10 208 | 80.16 213 |
|
CR-MVSNet | | | 59.91 240 | 57.90 252 | 65.96 229 | 69.96 277 | 52.07 143 | 65.31 280 | 63.15 288 | 42.48 299 | 59.36 232 | 74.84 274 | 35.83 233 | 70.75 271 | 45.50 219 | 64.65 260 | 75.06 266 |
|
RPMNet | | | 58.70 253 | 56.29 263 | 65.96 229 | 69.96 277 | 52.07 143 | 65.31 280 | 62.15 295 | 43.20 294 | 59.36 232 | 70.15 302 | 35.37 241 | 70.75 271 | 36.42 275 | 64.65 260 | 75.06 266 |
|
MDTV_nov1_ep13 | | | | 57.00 256 | | 72.73 242 | 38.26 280 | 65.02 283 | 64.73 272 | 44.74 280 | 55.46 270 | 72.48 287 | 32.61 273 | 70.47 273 | 37.47 265 | 67.75 239 | |
|
JIA-IIPM | | | 51.56 289 | 47.68 297 | 63.21 256 | 64.61 311 | 50.73 160 | 47.71 331 | 58.77 305 | 42.90 296 | 48.46 307 | 51.72 336 | 24.97 314 | 70.24 274 | 36.06 277 | 53.89 312 | 68.64 317 |
|
Patchmatch-test1 | | | 59.75 242 | 58.00 251 | 64.98 246 | 74.14 210 | 48.06 209 | 63.35 288 | 63.23 287 | 49.13 241 | 59.33 235 | 71.46 292 | 37.45 214 | 69.59 275 | 41.39 251 | 62.57 274 | 77.30 242 |
|
test_post1 | | | | | | | | 68.67 259 | | | | 3.64 351 | 32.39 275 | 69.49 276 | 44.17 227 | | |
|
PatchmatchNet | | | 59.84 241 | 58.24 247 | 64.65 248 | 73.05 234 | 46.70 222 | 69.42 254 | 62.18 294 | 47.55 257 | 58.88 240 | 71.96 290 | 34.49 249 | 69.16 277 | 42.99 239 | 63.60 266 | 78.07 234 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
EU-MVSNet | | | 55.61 273 | 54.41 273 | 59.19 279 | 65.41 308 | 33.42 319 | 72.44 219 | 71.91 224 | 28.81 334 | 51.27 297 | 73.87 281 | 24.76 315 | 69.08 278 | 43.04 238 | 58.20 301 | 75.06 266 |
|
Patchmtry | | | 57.16 263 | 56.47 260 | 59.23 278 | 69.17 283 | 34.58 307 | 62.98 289 | 63.15 288 | 44.53 283 | 56.83 262 | 74.84 274 | 35.83 233 | 68.71 279 | 40.03 256 | 60.91 285 | 74.39 276 |
|
CVMVSNet | | | 59.63 244 | 59.14 234 | 61.08 275 | 74.47 197 | 38.84 274 | 75.20 179 | 68.74 247 | 31.15 332 | 58.24 247 | 76.51 261 | 32.39 275 | 68.58 280 | 49.77 185 | 65.84 249 | 75.81 259 |
|
MDA-MVSNet-bldmvs | | | 53.87 280 | 50.81 287 | 63.05 258 | 66.25 302 | 48.58 204 | 56.93 311 | 63.82 283 | 48.09 252 | 41.22 328 | 70.48 299 | 30.34 282 | 68.00 281 | 34.24 281 | 45.92 330 | 72.57 293 |
|
AllTest | | | 57.08 264 | 54.65 271 | 64.39 249 | 71.44 260 | 49.03 199 | 69.92 251 | 67.30 256 | 45.97 271 | 47.16 310 | 79.77 202 | 17.47 326 | 67.56 282 | 33.65 283 | 59.16 298 | 76.57 252 |
|
TestCases | | | | | 64.39 249 | 71.44 260 | 49.03 199 | | 67.30 256 | 45.97 271 | 47.16 310 | 79.77 202 | 17.47 326 | 67.56 282 | 33.65 283 | 59.16 298 | 76.57 252 |
|
pmmvs5 | | | 56.47 265 | 55.68 266 | 58.86 281 | 61.41 322 | 36.71 294 | 66.37 272 | 62.75 291 | 40.38 311 | 53.70 287 | 76.62 258 | 34.56 247 | 67.05 284 | 40.02 257 | 65.27 253 | 72.83 290 |
|
EPNet_dtu | | | 61.90 227 | 61.97 212 | 61.68 265 | 72.89 240 | 39.78 266 | 75.85 168 | 65.62 265 | 55.09 179 | 54.56 280 | 79.36 215 | 37.59 213 | 67.02 285 | 39.80 258 | 76.95 123 | 78.25 232 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PatchMatch-RL | | | 56.25 268 | 54.55 272 | 61.32 269 | 77.06 162 | 56.07 93 | 65.57 276 | 54.10 328 | 44.13 288 | 53.49 292 | 71.27 295 | 25.20 313 | 66.78 286 | 36.52 274 | 63.66 265 | 61.12 328 |
|
test_post | | | | | | | | | | | | 3.55 352 | 33.90 255 | 66.52 287 | | | |
|
TDRefinement | | | 53.44 282 | 50.72 288 | 61.60 266 | 64.31 313 | 46.96 219 | 70.89 240 | 65.27 269 | 41.78 300 | 44.61 319 | 77.98 230 | 11.52 340 | 66.36 288 | 28.57 321 | 51.59 317 | 71.49 305 |
|
testdata | | | | | 64.66 247 | 81.52 69 | 52.93 130 | | 65.29 268 | 46.09 269 | 73.88 38 | 87.46 52 | 38.08 210 | 66.26 289 | 53.31 165 | 78.48 111 | 74.78 273 |
|
no-one | | | 40.85 311 | 36.09 315 | 55.14 297 | 48.55 342 | 38.72 275 | 42.15 340 | 62.92 290 | 34.60 328 | 23.55 341 | 49.74 340 | 12.21 337 | 66.16 290 | 26.27 327 | 24.84 340 | 60.54 329 |
|
semantic-postprocess | | | | | 65.40 241 | 71.99 254 | 50.80 158 | | 69.63 236 | 45.71 275 | 60.61 220 | 77.93 232 | 36.56 230 | 65.99 291 | 55.67 149 | 63.50 267 | 79.42 223 |
|
PM-MVS | | | 52.33 286 | 50.19 289 | 58.75 282 | 62.10 319 | 45.14 229 | 65.75 274 | 40.38 345 | 43.60 290 | 53.52 290 | 72.65 286 | 9.16 345 | 65.87 292 | 50.41 180 | 54.18 311 | 65.24 321 |
|
旧先验2 | | | | | | | | 76.08 162 | | 45.32 276 | 76.55 15 | | | 65.56 293 | 58.75 135 | | |
|
PVSNet | | 50.76 19 | 58.40 255 | 57.39 253 | 61.42 267 | 75.53 182 | 44.04 240 | 61.43 295 | 63.45 285 | 47.04 262 | 56.91 261 | 73.61 283 | 27.00 304 | 64.76 294 | 39.12 259 | 72.40 173 | 75.47 263 |
|
MVS-HIRNet | | | 45.52 302 | 44.48 304 | 48.65 317 | 68.49 288 | 34.05 309 | 59.41 305 | 44.50 343 | 27.03 336 | 37.96 334 | 50.47 339 | 26.16 309 | 64.10 295 | 26.74 325 | 59.52 296 | 47.82 338 |
|
FMVSNet5 | | | 55.86 271 | 54.93 269 | 58.66 283 | 71.05 266 | 36.35 296 | 64.18 287 | 62.48 293 | 46.76 263 | 50.66 302 | 74.73 276 | 25.80 310 | 64.04 296 | 33.11 285 | 65.57 252 | 75.59 262 |
|
MIMVSNet1 | | | 55.17 275 | 54.31 274 | 57.77 288 | 70.03 276 | 32.01 324 | 65.68 275 | 64.81 270 | 49.19 240 | 46.75 313 | 76.00 265 | 25.53 312 | 64.04 296 | 28.65 320 | 62.13 277 | 77.26 245 |
|
ADS-MVSNet2 | | | 51.33 290 | 48.76 293 | 59.07 280 | 66.02 306 | 44.60 234 | 50.90 326 | 59.76 302 | 36.90 321 | 50.74 300 | 66.18 316 | 26.38 306 | 63.11 298 | 27.17 322 | 54.76 309 | 69.50 313 |
|
LP | | | 48.51 295 | 45.51 300 | 57.52 290 | 62.86 316 | 44.53 237 | 52.38 323 | 59.84 301 | 38.11 318 | 42.81 326 | 61.02 326 | 23.23 318 | 63.02 299 | 24.10 329 | 45.24 331 | 65.02 322 |
|
Gipuma | | | 34.77 318 | 31.91 320 | 43.33 325 | 62.05 320 | 37.87 282 | 20.39 347 | 67.03 259 | 23.23 340 | 18.41 344 | 25.84 345 | 4.24 351 | 62.73 300 | 14.71 343 | 51.32 318 | 29.38 346 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
ITE_SJBPF | | | | | 62.09 264 | 66.16 303 | 44.55 236 | | 64.32 274 | 47.36 259 | 55.31 273 | 80.34 190 | 19.27 325 | 62.68 301 | 36.29 276 | 62.39 276 | 79.04 227 |
|
ANet_high | | | 41.38 310 | 37.47 314 | 53.11 306 | 39.73 349 | 24.45 343 | 56.94 310 | 69.69 234 | 47.65 256 | 26.04 340 | 52.32 335 | 12.44 336 | 62.38 302 | 21.80 334 | 10.61 349 | 72.49 294 |
|
MIMVSNet | | | 57.35 261 | 57.07 255 | 58.22 284 | 74.21 203 | 37.18 286 | 62.46 291 | 60.88 299 | 48.88 243 | 55.29 274 | 75.99 267 | 31.68 277 | 62.04 303 | 31.87 289 | 72.35 174 | 75.43 264 |
|
LCM-MVSNet | | | 40.30 312 | 35.88 317 | 53.57 304 | 42.24 346 | 29.15 331 | 45.21 336 | 60.53 300 | 22.23 342 | 28.02 339 | 50.98 338 | 3.72 353 | 61.78 304 | 31.22 300 | 38.76 336 | 69.78 312 |
|
PatchT | | | 53.17 284 | 53.44 281 | 52.33 310 | 68.29 290 | 25.34 341 | 58.21 307 | 54.41 325 | 44.46 284 | 54.56 280 | 69.05 305 | 33.32 261 | 60.94 305 | 36.93 268 | 61.76 279 | 70.73 310 |
|
WTY-MVS | | | 59.75 242 | 60.39 228 | 57.85 287 | 72.32 249 | 37.83 283 | 61.05 300 | 64.18 281 | 45.95 273 | 61.91 199 | 79.11 219 | 47.01 125 | 60.88 306 | 42.50 242 | 69.49 220 | 74.83 271 |
|
XXY-MVS | | | 60.68 235 | 61.67 218 | 57.70 289 | 70.43 271 | 38.45 279 | 64.19 286 | 66.47 261 | 48.05 253 | 63.22 175 | 80.86 177 | 49.28 84 | 60.47 307 | 45.25 224 | 67.28 243 | 74.19 282 |
|
tpmrst | | | 58.24 256 | 58.70 244 | 56.84 291 | 66.97 296 | 34.32 308 | 69.57 253 | 61.14 298 | 47.17 261 | 58.58 244 | 71.60 291 | 41.28 183 | 60.41 308 | 49.20 191 | 62.84 272 | 75.78 260 |
|
PMVS | | 28.69 22 | 36.22 315 | 33.29 319 | 45.02 323 | 36.82 351 | 35.98 300 | 54.68 316 | 48.74 336 | 26.31 337 | 21.02 342 | 51.61 337 | 2.88 355 | 60.10 309 | 9.99 348 | 47.58 328 | 38.99 343 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
PMMVS | | | 53.96 278 | 53.26 282 | 56.04 293 | 62.60 318 | 50.92 154 | 61.17 299 | 56.09 318 | 32.81 329 | 53.51 291 | 66.84 312 | 34.04 253 | 59.93 310 | 44.14 228 | 68.18 234 | 57.27 334 |
|
UnsupCasMVSNet_bld | | | 50.07 293 | 48.87 292 | 53.66 303 | 60.97 326 | 33.67 318 | 57.62 309 | 64.56 273 | 39.47 315 | 47.38 309 | 64.02 321 | 27.47 299 | 59.32 311 | 34.69 280 | 43.68 333 | 67.98 318 |
|
testpf | | | 44.11 306 | 45.40 301 | 40.26 328 | 60.52 327 | 27.34 335 | 33.26 344 | 54.33 326 | 45.87 274 | 41.08 329 | 60.26 328 | 16.46 328 | 59.14 312 | 46.09 210 | 50.68 322 | 34.31 344 |
|
PVSNet_0 | | 43.31 20 | 47.46 299 | 45.64 299 | 52.92 307 | 67.60 294 | 44.65 233 | 54.06 317 | 54.64 323 | 41.59 303 | 46.15 314 | 58.75 331 | 30.99 278 | 58.66 313 | 32.18 287 | 24.81 341 | 55.46 335 |
|
test20.03 | | | 53.87 280 | 54.02 277 | 53.41 305 | 61.47 321 | 28.11 333 | 61.30 297 | 59.21 303 | 51.34 224 | 52.09 295 | 77.43 249 | 33.29 262 | 58.55 314 | 29.76 309 | 60.27 295 | 73.58 286 |
|
UnsupCasMVSNet_eth | | | 53.16 285 | 52.47 283 | 55.23 296 | 59.45 332 | 33.39 320 | 59.43 304 | 69.13 243 | 45.98 270 | 50.35 304 | 72.32 288 | 29.30 290 | 58.26 315 | 42.02 246 | 44.30 332 | 74.05 283 |
|
pmmvs3 | | | 44.92 303 | 41.95 308 | 53.86 302 | 52.58 339 | 43.55 244 | 62.11 294 | 46.90 342 | 26.05 338 | 40.63 331 | 60.19 329 | 11.08 342 | 57.91 316 | 31.83 294 | 46.15 329 | 60.11 330 |
|
test-LLR | | | 58.15 258 | 58.13 250 | 58.22 284 | 68.57 286 | 44.80 231 | 65.46 277 | 57.92 308 | 50.08 235 | 55.44 271 | 69.82 303 | 32.62 271 | 57.44 317 | 49.66 188 | 73.62 149 | 72.41 297 |
|
test-mter | | | 56.42 266 | 55.82 265 | 58.22 284 | 68.57 286 | 44.80 231 | 65.46 277 | 57.92 308 | 39.94 314 | 55.44 271 | 69.82 303 | 21.92 322 | 57.44 317 | 49.66 188 | 73.62 149 | 72.41 297 |
|
test1235678 | | | 45.66 300 | 44.46 305 | 49.26 315 | 59.88 330 | 28.68 332 | 56.36 313 | 55.54 322 | 39.12 317 | 40.89 330 | 63.40 323 | 14.41 333 | 57.32 319 | 21.05 335 | 49.47 325 | 61.78 326 |
|
new-patchmatchnet | | | 47.56 298 | 47.73 296 | 47.06 319 | 58.81 333 | 9.37 352 | 48.78 330 | 59.21 303 | 43.28 292 | 44.22 320 | 68.66 306 | 25.67 311 | 57.20 320 | 31.57 297 | 49.35 326 | 74.62 275 |
|
EPMVS | | | 53.96 278 | 53.69 278 | 54.79 300 | 66.12 304 | 31.96 325 | 62.34 293 | 49.05 335 | 44.42 285 | 55.54 269 | 71.33 294 | 30.22 283 | 56.70 321 | 41.65 249 | 62.54 275 | 75.71 261 |
|
dp | | | 51.89 288 | 51.60 286 | 52.77 308 | 68.44 289 | 32.45 322 | 62.36 292 | 54.57 324 | 44.16 287 | 49.31 305 | 67.91 307 | 28.87 293 | 56.61 322 | 33.89 282 | 54.89 308 | 69.24 316 |
|
Anonymous20231206 | | | 55.10 276 | 55.30 268 | 54.48 301 | 69.81 280 | 33.94 311 | 62.91 290 | 62.13 296 | 41.08 305 | 55.18 275 | 75.65 269 | 32.75 269 | 56.59 323 | 30.32 303 | 67.86 237 | 72.91 289 |
|
testmv | | | 42.25 308 | 40.11 311 | 48.66 316 | 53.23 337 | 27.02 336 | 56.62 312 | 55.74 321 | 37.25 320 | 33.10 336 | 59.52 330 | 7.78 347 | 56.58 324 | 19.61 339 | 38.13 337 | 62.40 325 |
|
wuykxyi23d | | | 28.12 321 | 22.54 326 | 44.87 324 | 34.97 352 | 32.11 323 | 37.96 343 | 47.31 340 | 13.32 347 | 9.29 351 | 23.72 347 | 0.45 358 | 56.58 324 | 21.85 333 | 13.98 345 | 45.93 340 |
|
sss | | | 56.17 269 | 56.57 259 | 54.96 298 | 66.93 297 | 36.32 298 | 57.94 308 | 61.69 297 | 41.67 302 | 58.64 243 | 75.32 272 | 38.72 202 | 56.25 326 | 42.04 245 | 66.19 247 | 72.31 300 |
|
RPSCF | | | 55.80 272 | 54.22 276 | 60.53 277 | 65.13 309 | 42.91 250 | 64.30 285 | 57.62 310 | 36.84 323 | 58.05 248 | 82.28 136 | 28.01 295 | 56.24 327 | 37.14 267 | 58.61 300 | 82.44 173 |
|
test0.0.03 1 | | | 53.32 283 | 53.59 280 | 52.50 309 | 62.81 317 | 29.45 330 | 59.51 303 | 54.11 327 | 50.08 235 | 54.40 282 | 74.31 279 | 32.62 271 | 55.92 328 | 30.50 301 | 63.95 264 | 72.15 303 |
|
testgi | | | 51.90 287 | 52.37 284 | 50.51 314 | 60.39 328 | 23.55 344 | 58.42 306 | 58.15 306 | 49.03 242 | 51.83 296 | 79.21 218 | 22.39 320 | 55.59 329 | 29.24 317 | 62.64 273 | 72.40 299 |
|
test2356 | | | 45.61 301 | 44.66 303 | 48.47 318 | 60.15 329 | 28.08 334 | 52.44 322 | 52.83 331 | 38.01 319 | 46.13 315 | 60.98 327 | 15.08 332 | 55.54 330 | 20.43 338 | 55.85 306 | 61.78 326 |
|
TESTMET0.1,1 | | | 55.28 274 | 54.90 270 | 56.42 292 | 66.56 301 | 43.67 243 | 65.46 277 | 56.27 317 | 39.18 316 | 53.83 286 | 67.44 310 | 24.21 317 | 55.46 331 | 48.04 198 | 73.11 161 | 70.13 311 |
|
YYNet1 | | | 50.73 291 | 48.96 290 | 56.03 294 | 61.10 324 | 41.78 255 | 51.94 324 | 56.44 315 | 40.94 307 | 44.84 317 | 67.80 309 | 30.08 285 | 55.08 332 | 36.77 269 | 50.71 320 | 71.22 306 |
|
MDA-MVSNet_test_wron | | | 50.71 292 | 48.95 291 | 56.00 295 | 61.17 323 | 41.84 254 | 51.90 325 | 56.45 314 | 40.96 306 | 44.79 318 | 67.84 308 | 30.04 286 | 55.07 333 | 36.71 271 | 50.69 321 | 71.11 309 |
|
tpm | | | 57.34 262 | 58.16 248 | 54.86 299 | 71.80 258 | 34.77 305 | 67.47 269 | 56.04 319 | 48.20 251 | 60.10 224 | 76.92 253 | 37.17 218 | 53.41 334 | 40.76 253 | 65.01 255 | 76.40 254 |
|
testus | | | 44.59 304 | 43.87 306 | 46.76 320 | 59.85 331 | 24.65 342 | 53.86 318 | 55.82 320 | 36.26 324 | 43.97 323 | 63.42 322 | 8.39 346 | 53.14 335 | 20.70 337 | 52.52 315 | 62.51 324 |
|
ADS-MVSNet | | | 48.48 296 | 47.77 295 | 50.63 313 | 66.02 306 | 29.92 329 | 50.90 326 | 50.87 334 | 36.90 321 | 50.74 300 | 66.18 316 | 26.38 306 | 52.47 336 | 27.17 322 | 54.76 309 | 69.50 313 |
|
FPMVS | | | 42.18 309 | 41.11 309 | 45.39 321 | 58.03 334 | 41.01 261 | 49.50 328 | 53.81 329 | 30.07 333 | 33.71 335 | 64.03 319 | 11.69 338 | 52.08 337 | 14.01 344 | 55.11 307 | 43.09 341 |
|
test12356 | | | 36.16 316 | 35.94 316 | 36.83 329 | 50.82 341 | 8.52 353 | 44.84 337 | 53.49 330 | 32.72 330 | 30.11 338 | 55.08 333 | 7.11 349 | 49.47 338 | 16.60 341 | 32.68 339 | 52.50 336 |
|
CHOSEN 280x420 | | | 47.83 297 | 46.36 298 | 52.24 311 | 67.37 295 | 49.78 190 | 38.91 342 | 43.11 344 | 35.00 326 | 43.27 325 | 63.30 324 | 28.95 291 | 49.19 339 | 36.53 273 | 60.80 288 | 57.76 333 |
|
Patchmatch-test | | | 49.08 294 | 48.28 294 | 51.50 312 | 64.40 312 | 30.85 328 | 45.68 334 | 48.46 338 | 35.60 325 | 46.10 316 | 72.10 289 | 34.47 250 | 46.37 340 | 27.08 324 | 60.65 289 | 77.27 244 |
|
1111 | | | 44.40 305 | 45.00 302 | 42.61 326 | 57.55 335 | 17.33 349 | 53.82 320 | 57.05 312 | 40.78 308 | 44.11 321 | 66.57 313 | 13.37 334 | 45.77 341 | 22.15 331 | 49.58 324 | 64.73 323 |
|
.test1245 | | | 34.88 317 | 39.49 312 | 21.04 338 | 57.55 335 | 17.33 349 | 53.82 320 | 57.05 312 | 40.78 308 | 44.11 321 | 66.57 313 | 13.37 334 | 45.77 341 | 22.15 331 | 0.00 352 | 0.03 353 |
|
DSMNet-mixed | | | 39.30 314 | 38.72 313 | 41.03 327 | 51.22 340 | 19.66 346 | 45.53 335 | 31.35 350 | 15.83 346 | 39.80 333 | 67.42 311 | 22.19 321 | 45.13 343 | 22.43 330 | 52.69 314 | 58.31 332 |
|
LF4IMVS | | | 42.95 307 | 42.26 307 | 45.04 322 | 48.30 343 | 32.50 321 | 54.80 315 | 48.49 337 | 28.03 335 | 40.51 332 | 70.16 301 | 9.24 344 | 43.89 344 | 31.63 295 | 49.18 327 | 58.72 331 |
|
PNet_i23d | | | 27.88 322 | 25.99 322 | 33.55 332 | 47.54 344 | 25.89 338 | 47.24 333 | 32.91 349 | 21.44 343 | 15.90 345 | 38.09 342 | 0.85 357 | 42.76 345 | 16.90 340 | 13.03 347 | 32.00 345 |
|
N_pmnet | | | 39.35 313 | 40.28 310 | 36.54 330 | 63.76 314 | 1.62 356 | 49.37 329 | 0.76 357 | 34.62 327 | 43.61 324 | 66.38 315 | 26.25 308 | 42.57 346 | 26.02 328 | 51.77 316 | 65.44 320 |
|
E-PMN | | | 23.77 324 | 22.73 325 | 26.90 336 | 42.02 347 | 20.67 345 | 42.66 339 | 35.70 346 | 17.43 344 | 10.28 349 | 25.05 346 | 6.42 350 | 42.39 347 | 10.28 347 | 14.71 344 | 17.63 347 |
|
EMVS | | | 22.97 325 | 21.84 327 | 26.36 337 | 40.20 348 | 19.53 347 | 41.95 341 | 34.64 347 | 17.09 345 | 9.73 350 | 22.83 348 | 7.29 348 | 42.22 348 | 9.18 349 | 13.66 346 | 17.32 348 |
|
PMMVS2 | | | 27.40 323 | 25.91 323 | 31.87 334 | 39.46 350 | 6.57 354 | 31.17 345 | 28.52 351 | 23.96 339 | 20.45 343 | 48.94 341 | 4.20 352 | 37.94 349 | 16.51 342 | 19.97 342 | 51.09 337 |
|
new_pmnet | | | 34.13 319 | 34.29 318 | 33.64 331 | 52.63 338 | 18.23 348 | 44.43 338 | 33.90 348 | 22.81 341 | 30.89 337 | 53.18 334 | 10.48 343 | 35.72 350 | 20.77 336 | 39.51 334 | 46.98 339 |
|
MVE | | 17.77 23 | 21.41 326 | 17.77 328 | 32.34 333 | 34.34 353 | 25.44 340 | 16.11 348 | 24.11 352 | 11.19 348 | 13.22 347 | 31.92 343 | 1.58 356 | 30.95 351 | 10.47 346 | 17.03 343 | 40.62 342 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
wuyk23d | | | 13.32 328 | 12.52 329 | 15.71 339 | 47.54 344 | 26.27 337 | 31.06 346 | 1.98 356 | 4.93 350 | 5.18 352 | 1.94 353 | 0.45 358 | 18.54 352 | 6.81 351 | 12.83 348 | 2.33 351 |
|
DeepMVS_CX | | | | | 12.03 340 | 17.97 354 | 10.91 351 | | 10.60 355 | 7.46 349 | 11.07 348 | 28.36 344 | 3.28 354 | 11.29 353 | 8.01 350 | 9.74 351 | 13.89 349 |
|
tmp_tt | | | 9.43 329 | 11.14 330 | 4.30 341 | 2.38 355 | 4.40 355 | 13.62 349 | 16.08 354 | 0.39 351 | 15.89 346 | 13.06 349 | 15.80 330 | 5.54 354 | 12.63 345 | 10.46 350 | 2.95 350 |
|
cdsmvs_eth3d_5k | | | 17.50 327 | 23.34 324 | 0.00 344 | 0.00 358 | 0.00 358 | 0.00 350 | 78.63 153 | 0.00 354 | 0.00 355 | 82.18 137 | 49.25 88 | 0.00 355 | 0.00 354 | 0.00 352 | 0.00 355 |
|
pcd_1.5k_mvsjas | | | 3.92 333 | 5.23 334 | 0.00 344 | 0.00 358 | 0.00 358 | 0.00 350 | 0.00 358 | 0.00 354 | 0.00 355 | 0.00 356 | 47.05 122 | 0.00 355 | 0.00 354 | 0.00 352 | 0.00 355 |
|
pcd1.5k->3k | | | 30.06 320 | 30.56 321 | 28.55 335 | 78.81 114 | 0.00 358 | 0.00 350 | 82.07 70 | 0.00 354 | 0.00 355 | 0.00 356 | 39.61 194 | 0.00 355 | 0.00 354 | 74.56 138 | 85.66 71 |
|
sosnet-low-res | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 358 | 0.00 350 | 0.00 358 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 361 | 0.00 355 | 0.00 354 | 0.00 352 | 0.00 355 |
|
sosnet | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 358 | 0.00 350 | 0.00 358 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 361 | 0.00 355 | 0.00 354 | 0.00 352 | 0.00 355 |
|
uncertanet | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 358 | 0.00 350 | 0.00 358 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 361 | 0.00 355 | 0.00 354 | 0.00 352 | 0.00 355 |
|
Regformer | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 358 | 0.00 350 | 0.00 358 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 361 | 0.00 355 | 0.00 354 | 0.00 352 | 0.00 355 |
|
testmvs | | | 4.52 332 | 6.03 333 | 0.01 343 | 0.01 356 | 0.00 358 | 53.86 318 | 0.00 358 | 0.01 352 | 0.04 353 | 0.27 354 | 0.00 361 | 0.00 355 | 0.04 352 | 0.00 352 | 0.03 353 |
|
test123 | | | 4.73 331 | 6.30 332 | 0.02 342 | 0.01 356 | 0.01 357 | 56.36 313 | 0.00 358 | 0.01 352 | 0.04 353 | 0.21 355 | 0.01 360 | 0.00 355 | 0.03 353 | 0.00 352 | 0.04 352 |
|
ab-mvs-re | | | 6.49 330 | 8.65 331 | 0.00 344 | 0.00 358 | 0.00 358 | 0.00 350 | 0.00 358 | 0.00 354 | 0.00 355 | 77.89 234 | 0.00 361 | 0.00 355 | 0.00 354 | 0.00 352 | 0.00 355 |
|
uanet | | | 0.00 334 | 0.00 335 | 0.00 344 | 0.00 358 | 0.00 358 | 0.00 350 | 0.00 358 | 0.00 354 | 0.00 355 | 0.00 356 | 0.00 361 | 0.00 355 | 0.00 354 | 0.00 352 | 0.00 355 |
|
GSMVS | | | | | | | | | | | | | | | | | 78.05 235 |
|
test_part2 | | | | | | 87.58 3 | 60.47 37 | | | | 83.42 2 | | | | | | |
|
test_part1 | | | | | | | | | 86.64 3 | | | | 65.59 1 | | | 90.06 3 | 86.78 39 |
|
sam_mvs1 | | | | | | | | | | | | | 34.74 245 | | | | 78.05 235 |
|
sam_mvs | | | | | | | | | | | | | 33.43 260 | | | | |
|
MTGPA | | | | | | | | | 80.97 98 | | | | | | | | |
|
MTMP | | | | | | | | | 17.08 353 | | | | | | | | |
|
test9_res | | | | | | | | | | | | | | | 75.28 16 | 88.31 18 | 83.81 139 |
|
agg_prior2 | | | | | | | | | | | | | | | 73.09 31 | 87.93 25 | 84.33 117 |
|
test_prior4 | | | | | | | 62.51 17 | 82.08 59 | | | | | | | | | |
|
test_prior2 | | | | | | | | 81.75 61 | | 60.37 80 | 75.01 22 | 89.06 35 | 56.22 19 | | 72.19 34 | 88.96 10 | |
|
æ–°å‡ ä½•2 | | | | | | | | 76.12 160 | | | | | | | | | |
|
旧先验1 | | | | | | 83.04 55 | 53.15 127 | | 67.52 255 | | | 87.85 50 | 44.08 154 | | | 80.76 74 | 78.03 237 |
|
原ACMM2 | | | | | | | | 79.02 97 | | | | | | | | | |
|
test222 | | | | | | 83.14 54 | 58.68 58 | 72.57 217 | 63.45 285 | 41.78 300 | 67.56 127 | 86.12 74 | 37.13 219 | | | 78.73 108 | 74.98 269 |
|
segment_acmp | | | | | | | | | | | | | 54.23 35 | | | | |
|
testdata1 | | | | | | | | 72.65 213 | | 60.50 77 | | | | | | | |
|
plane_prior7 | | | | | | 81.41 72 | 55.96 95 | | | | | | | | | | |
|
plane_prior6 | | | | | | 81.20 79 | 56.24 90 | | | | | | 45.26 144 | | | | |
|
plane_prior4 | | | | | | | | | | | | 86.10 75 | | | | | |
|
plane_prior3 | | | | | | | 56.09 92 | | | 63.92 30 | 69.27 97 | | | | | | |
|
plane_prior2 | | | | | | | | 84.22 24 | | 64.52 24 | | | | | | | |
|
plane_prior1 | | | | | | 81.27 77 | | | | | | | | | | | |
|
plane_prior | | | | | | | 56.31 86 | 83.58 33 | | 63.19 39 | | | | | | 80.48 80 | |
|
n2 | | | | | | | | | 0.00 358 | | | | | | | | |
|
nn | | | | | | | | | 0.00 358 | | | | | | | | |
|
door-mid | | | | | | | | | 47.19 341 | | | | | | | | |
|
test11 | | | | | | | | | 83.47 45 | | | | | | | | |
|
door | | | | | | | | | 47.60 339 | | | | | | | | |
|
HQP5-MVS | | | | | | | 54.94 108 | | | | | | | | | | |
|
HQP-NCC | | | | | | 80.66 84 | | 82.31 54 | | 62.10 56 | 67.85 119 | | | | | | |
|
ACMP_Plane | | | | | | 80.66 84 | | 82.31 54 | | 62.10 56 | 67.85 119 | | | | | | |
|
BP-MVS | | | | | | | | | | | | | | | 67.04 60 | | |
|
HQP3-MVS | | | | | | | | | 83.90 34 | | | | | | | 80.35 83 | |
|
HQP2-MVS | | | | | | | | | | | | | 45.46 138 | | | | |
|
NP-MVS | | | | | | 80.98 82 | 56.05 94 | | | | | 85.54 87 | | | | | |
|
MDTV_nov1_ep13_2view | | | | | | | 25.89 338 | 61.22 298 | | 40.10 312 | 51.10 298 | | 32.97 264 | | 38.49 261 | | 78.61 230 |
|
ACMMP++_ref | | | | | | | | | | | | | | | | 74.07 145 | |
|
ACMMP++ | | | | | | | | | | | | | | | | 72.16 177 | |
|
Test By Simon | | | | | | | | | | | | | 48.33 107 | | | | |
|