APDe-MVS | | | 99.49 1 | 99.64 1 | 99.32 1 | 99.74 4 | 99.74 3 | 99.75 1 | 98.34 2 | 99.56 9 | 98.72 3 | 99.57 4 | 99.97 4 | 99.53 15 | 99.65 2 | 99.25 14 | 99.84 5 | 99.77 49 |
|
SMA-MVS | | | 99.30 5 | 99.62 2 | 98.93 17 | 99.76 2 | 99.64 25 | 99.44 24 | 98.21 14 | 99.53 12 | 96.79 29 | 99.41 9 | 99.98 1 | 99.67 4 | 99.63 3 | 99.37 9 | 99.71 69 | 99.78 41 |
|
TSAR-MVS + MP. | | | 99.27 7 | 99.57 3 | 98.92 19 | 98.78 49 | 99.53 52 | 99.72 2 | 98.11 24 | 99.73 2 | 97.43 20 | 99.15 20 | 99.96 10 | 99.59 9 | 99.73 1 | 99.07 22 | 99.88 1 | 99.82 25 |
|
ACMMPR | | | 99.30 5 | 99.54 4 | 99.03 12 | 99.66 14 | 99.64 25 | 99.68 5 | 98.25 12 | 99.56 9 | 97.12 25 | 99.19 17 | 99.95 15 | 99.72 1 | 99.43 14 | 99.25 14 | 99.72 60 | 99.77 49 |
|
HFP-MVS | | | 99.32 2 | 99.53 5 | 99.07 9 | 99.69 8 | 99.59 44 | 99.63 10 | 98.31 5 | 99.56 9 | 97.37 21 | 99.27 14 | 99.97 4 | 99.70 3 | 99.35 19 | 99.24 16 | 99.71 69 | 99.76 53 |
|
SteuartSystems-ACMMP | | | 99.20 13 | 99.51 6 | 98.83 23 | 99.66 14 | 99.66 19 | 99.71 4 | 98.12 23 | 99.14 53 | 96.62 30 | 99.16 19 | 99.98 1 | 99.12 50 | 99.63 3 | 99.19 20 | 99.78 37 | 99.83 24 |
Skip Steuart: Steuart Systems R&D Blog. |
SD-MVS | | | 99.25 9 | 99.50 7 | 98.96 16 | 98.79 48 | 99.55 51 | 99.33 30 | 98.29 9 | 99.75 1 | 97.96 13 | 99.15 20 | 99.95 15 | 99.61 6 | 99.17 26 | 99.06 23 | 99.81 26 | 99.84 20 |
|
DeepPCF-MVS | | 97.74 3 | 98.34 42 | 99.46 8 | 97.04 59 | 98.82 47 | 99.33 77 | 96.28 135 | 97.47 33 | 99.58 7 | 94.70 57 | 98.99 28 | 99.85 36 | 97.24 107 | 99.55 7 | 99.34 10 | 97.73 211 | 99.56 136 |
|
ACMMP_Plus | | | 99.05 22 | 99.45 9 | 98.58 27 | 99.73 5 | 99.60 42 | 99.64 8 | 98.28 10 | 99.23 43 | 94.57 58 | 99.35 12 | 99.97 4 | 99.55 13 | 99.63 3 | 98.66 44 | 99.70 78 | 99.74 65 |
|
TSAR-MVS + ACMM | | | 98.77 30 | 99.45 9 | 97.98 39 | 99.37 32 | 99.46 59 | 99.44 24 | 98.13 22 | 99.65 4 | 92.30 96 | 98.91 36 | 99.95 15 | 99.05 56 | 99.42 15 | 98.95 29 | 99.58 150 | 99.82 25 |
|
DeepC-MVS_fast | | 98.34 1 | 99.17 14 | 99.45 9 | 98.85 21 | 99.55 24 | 99.37 70 | 99.64 8 | 98.05 26 | 99.53 12 | 96.58 31 | 98.93 31 | 99.92 25 | 99.49 18 | 99.46 12 | 99.32 11 | 99.80 33 | 99.64 121 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
zzz-MVS | | | 99.31 3 | 99.44 12 | 99.16 4 | 99.73 5 | 99.65 20 | 99.63 10 | 98.26 11 | 99.27 36 | 98.01 12 | 99.27 14 | 99.97 4 | 99.60 7 | 99.59 6 | 98.58 50 | 99.71 69 | 99.73 69 |
|
CP-MVS | | | 99.27 7 | 99.44 12 | 99.08 8 | 99.62 18 | 99.58 46 | 99.53 15 | 98.16 17 | 99.21 46 | 97.79 15 | 99.15 20 | 99.96 10 | 99.59 9 | 99.54 8 | 98.86 36 | 99.78 37 | 99.74 65 |
|
HSP-MVS | | | 99.31 3 | 99.43 14 | 99.17 2 | 99.68 11 | 99.75 2 | 99.72 2 | 98.31 5 | 99.45 17 | 98.16 9 | 99.28 13 | 99.98 1 | 99.30 31 | 99.34 20 | 98.41 59 | 99.81 26 | 99.81 30 |
|
PHI-MVS | | | 99.08 19 | 99.43 14 | 98.67 25 | 99.15 41 | 99.59 44 | 99.11 39 | 97.35 34 | 99.14 53 | 97.30 22 | 99.44 8 | 99.96 10 | 99.32 29 | 98.89 43 | 99.39 7 | 99.79 34 | 99.58 130 |
|
ESAPD | | | 99.23 11 | 99.41 16 | 99.01 14 | 99.70 7 | 99.69 11 | 99.40 27 | 98.31 5 | 98.94 75 | 97.70 17 | 99.40 10 | 99.97 4 | 99.17 42 | 99.54 8 | 98.67 43 | 99.78 37 | 99.67 105 |
|
MVS_111021_LR | | | 98.67 34 | 99.41 16 | 97.81 42 | 99.37 32 | 99.53 52 | 98.51 60 | 95.52 42 | 99.27 36 | 94.85 54 | 99.56 5 | 99.69 44 | 99.04 57 | 99.36 18 | 98.88 34 | 99.60 140 | 99.58 130 |
|
APD-MVS | | | 99.25 9 | 99.38 18 | 99.09 7 | 99.69 8 | 99.58 46 | 99.56 14 | 98.32 4 | 98.85 82 | 97.87 14 | 98.91 36 | 99.92 25 | 99.30 31 | 99.45 13 | 99.38 8 | 99.79 34 | 99.58 130 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
X-MVS | | | 98.93 26 | 99.37 19 | 98.42 28 | 99.67 12 | 99.62 34 | 99.60 12 | 98.15 19 | 99.08 60 | 93.81 80 | 98.46 54 | 99.95 15 | 99.59 9 | 99.49 11 | 99.21 19 | 99.68 89 | 99.75 62 |
|
MP-MVS | | | 99.07 20 | 99.36 20 | 98.74 24 | 99.63 17 | 99.57 48 | 99.66 7 | 98.25 12 | 99.00 70 | 95.62 38 | 98.97 29 | 99.94 23 | 99.54 14 | 99.51 10 | 98.79 41 | 99.71 69 | 99.73 69 |
|
TSAR-MVS + GP. | | | 98.66 36 | 99.36 20 | 97.85 41 | 97.16 75 | 99.46 59 | 99.03 45 | 94.59 56 | 99.09 58 | 97.19 24 | 99.73 3 | 99.95 15 | 99.39 25 | 98.95 37 | 98.69 42 | 99.75 45 | 99.65 117 |
|
MVS_111021_HR | | | 98.59 38 | 99.36 20 | 97.68 43 | 99.42 30 | 99.61 38 | 98.14 82 | 94.81 49 | 99.31 31 | 95.00 51 | 99.51 6 | 99.79 40 | 99.00 60 | 98.94 38 | 98.83 38 | 99.69 80 | 99.57 135 |
|
PGM-MVS | | | 98.86 28 | 99.35 23 | 98.29 31 | 99.77 1 | 99.63 30 | 99.67 6 | 95.63 40 | 98.66 102 | 95.27 45 | 99.11 23 | 99.82 37 | 99.67 4 | 99.33 21 | 99.19 20 | 99.73 55 | 99.74 65 |
|
HPM-MVS++ | | | 99.10 18 | 99.30 24 | 98.86 20 | 99.69 8 | 99.48 57 | 99.59 13 | 98.34 2 | 99.26 39 | 96.55 33 | 99.10 24 | 99.96 10 | 99.36 26 | 99.25 24 | 98.37 64 | 99.64 118 | 99.66 114 |
|
CNVR-MVS | | | 99.23 11 | 99.28 25 | 99.17 2 | 99.65 16 | 99.34 75 | 99.46 21 | 98.21 14 | 99.28 34 | 98.47 5 | 98.89 38 | 99.94 23 | 99.50 16 | 99.42 15 | 98.61 47 | 99.73 55 | 99.52 141 |
|
MCST-MVS | | | 99.11 17 | 99.27 26 | 98.93 17 | 99.67 12 | 99.33 77 | 99.51 17 | 98.31 5 | 99.28 34 | 96.57 32 | 99.10 24 | 99.90 28 | 99.71 2 | 99.19 25 | 98.35 66 | 99.82 13 | 99.71 85 |
|
CHOSEN 280x420 | | | 97.99 49 | 99.24 27 | 96.53 79 | 98.34 54 | 99.61 38 | 98.36 73 | 89.80 139 | 99.27 36 | 95.08 49 | 99.81 1 | 98.58 57 | 98.64 68 | 99.02 34 | 98.92 31 | 98.93 193 | 99.48 150 |
|
MSLP-MVS++ | | | 99.15 15 | 99.24 27 | 99.04 11 | 99.52 27 | 99.49 56 | 99.09 41 | 98.07 25 | 99.37 22 | 98.47 5 | 97.79 71 | 99.89 30 | 99.50 16 | 98.93 39 | 99.45 4 | 99.61 133 | 99.76 53 |
|
CPTT-MVS | | | 99.14 16 | 99.20 29 | 99.06 10 | 99.58 21 | 99.53 52 | 99.45 22 | 97.80 31 | 99.19 49 | 98.32 8 | 98.58 48 | 99.95 15 | 99.60 7 | 99.28 23 | 98.20 76 | 99.64 118 | 99.69 93 |
|
CANet | | | 98.46 39 | 99.16 30 | 97.64 44 | 98.48 52 | 99.64 25 | 99.35 29 | 94.71 52 | 99.53 12 | 95.17 47 | 97.63 77 | 99.59 47 | 98.38 76 | 98.88 44 | 98.99 27 | 99.74 49 | 99.86 15 |
|
UA-Net | | | 97.13 74 | 99.14 31 | 94.78 107 | 97.21 73 | 99.38 68 | 97.56 100 | 92.04 95 | 98.48 112 | 88.03 122 | 98.39 57 | 99.91 27 | 94.03 195 | 99.33 21 | 99.23 17 | 99.81 26 | 99.25 163 |
|
train_agg | | | 98.73 32 | 99.11 32 | 98.28 32 | 99.36 34 | 99.35 73 | 99.48 20 | 97.96 28 | 98.83 85 | 93.86 79 | 98.70 45 | 99.86 33 | 99.44 22 | 99.08 32 | 98.38 62 | 99.61 133 | 99.58 130 |
|
CDPH-MVS | | | 98.41 40 | 99.10 33 | 97.61 45 | 99.32 38 | 99.36 71 | 99.49 18 | 96.15 39 | 98.82 87 | 91.82 99 | 98.41 55 | 99.66 45 | 99.10 53 | 98.93 39 | 98.97 28 | 99.75 45 | 99.58 130 |
|
CANet_DTU | | | 96.64 93 | 99.08 34 | 93.81 119 | 97.10 76 | 99.42 65 | 98.85 50 | 90.01 133 | 99.31 31 | 79.98 177 | 99.78 2 | 99.10 53 | 97.42 104 | 98.35 79 | 98.05 82 | 99.47 169 | 99.53 139 |
|
NCCC | | | 99.05 22 | 99.08 34 | 99.02 13 | 99.62 18 | 99.38 68 | 99.43 26 | 98.21 14 | 99.36 24 | 97.66 18 | 97.79 71 | 99.90 28 | 99.45 21 | 99.17 26 | 98.43 56 | 99.77 42 | 99.51 145 |
|
UGNet | | | 97.66 58 | 99.07 36 | 96.01 93 | 97.19 74 | 99.65 20 | 97.09 119 | 93.39 86 | 99.35 26 | 94.40 67 | 98.79 41 | 99.59 47 | 94.24 192 | 98.04 106 | 98.29 73 | 99.73 55 | 99.80 32 |
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 |
CNLPA | | | 99.03 24 | 99.05 37 | 99.01 14 | 99.27 39 | 99.22 91 | 99.03 45 | 97.98 27 | 99.34 29 | 99.00 2 | 98.25 60 | 99.71 43 | 99.31 30 | 98.80 48 | 98.82 39 | 99.48 167 | 99.17 167 |
|
3Dnovator+ | | 96.92 7 | 98.71 33 | 99.05 37 | 98.32 30 | 99.53 25 | 99.34 75 | 99.06 43 | 94.61 54 | 99.65 4 | 97.49 19 | 96.75 95 | 99.86 33 | 99.44 22 | 98.78 50 | 99.30 12 | 99.81 26 | 99.67 105 |
|
3Dnovator | | 96.92 7 | 98.67 34 | 99.05 37 | 98.23 34 | 99.57 22 | 99.45 61 | 99.11 39 | 94.66 53 | 99.69 3 | 96.80 28 | 96.55 105 | 99.61 46 | 99.40 24 | 98.87 45 | 99.49 3 | 99.85 4 | 99.66 114 |
|
QAPM | | | 98.62 37 | 99.04 40 | 98.13 35 | 99.57 22 | 99.48 57 | 99.17 36 | 94.78 50 | 99.57 8 | 96.16 35 | 96.73 97 | 99.80 38 | 99.33 28 | 98.79 49 | 99.29 13 | 99.75 45 | 99.64 121 |
|
MVS_0304 | | | 98.14 47 | 99.03 41 | 97.10 55 | 98.05 59 | 99.63 30 | 99.27 32 | 94.33 59 | 99.63 6 | 93.06 90 | 97.32 80 | 99.05 54 | 98.09 86 | 98.82 47 | 98.87 35 | 99.81 26 | 99.89 7 |
|
ACMMP | | | 98.74 31 | 99.03 41 | 98.40 29 | 99.36 34 | 99.64 25 | 99.20 34 | 97.75 32 | 98.82 87 | 95.24 46 | 98.85 39 | 99.87 32 | 99.17 42 | 98.74 55 | 97.50 109 | 99.71 69 | 99.76 53 |
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 |
OMC-MVS | | | 98.84 29 | 99.01 43 | 98.65 26 | 99.39 31 | 99.23 90 | 99.22 33 | 96.70 36 | 99.40 19 | 97.77 16 | 97.89 70 | 99.80 38 | 99.21 35 | 99.02 34 | 98.65 45 | 99.57 154 | 99.07 174 |
|
AdaColmap | | | 99.06 21 | 98.98 44 | 99.15 5 | 99.60 20 | 99.30 81 | 99.38 28 | 98.16 17 | 99.02 69 | 98.55 4 | 98.71 44 | 99.57 49 | 99.58 12 | 99.09 30 | 97.84 92 | 99.64 118 | 99.36 158 |
|
PLC | | 97.93 2 | 99.02 25 | 98.94 45 | 99.11 6 | 99.46 29 | 99.24 89 | 99.06 43 | 97.96 28 | 99.31 31 | 99.16 1 | 97.90 69 | 99.79 40 | 99.36 26 | 98.71 56 | 98.12 79 | 99.65 107 | 99.52 141 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CSCG | | | 98.90 27 | 98.93 46 | 98.85 21 | 99.75 3 | 99.72 4 | 99.49 18 | 96.58 37 | 99.38 20 | 98.05 11 | 98.97 29 | 97.87 64 | 99.49 18 | 97.78 117 | 98.92 31 | 99.78 37 | 99.90 3 |
|
Vis-MVSNet (Re-imp) | | | 97.40 66 | 98.89 47 | 95.66 100 | 95.99 106 | 99.62 34 | 97.82 93 | 93.22 89 | 98.82 87 | 91.40 103 | 96.94 92 | 98.56 58 | 95.70 146 | 99.14 28 | 99.41 6 | 99.79 34 | 99.75 62 |
|
EPNet | | | 98.05 48 | 98.86 48 | 97.10 55 | 99.02 44 | 99.43 64 | 98.47 61 | 94.73 51 | 99.05 66 | 95.62 38 | 98.93 31 | 97.62 68 | 95.48 157 | 98.59 68 | 98.55 51 | 99.29 185 | 99.84 20 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
IS_MVSNet | | | 97.86 51 | 98.86 48 | 96.68 75 | 96.02 103 | 99.72 4 | 98.35 74 | 93.37 88 | 98.75 99 | 94.01 73 | 96.88 94 | 98.40 60 | 98.48 73 | 99.09 30 | 99.42 5 | 99.83 9 | 99.80 32 |
|
TAPA-MVS | | 97.53 5 | 98.41 40 | 98.84 50 | 97.91 40 | 99.08 43 | 99.33 77 | 99.15 37 | 97.13 35 | 99.34 29 | 93.20 87 | 97.75 73 | 99.19 52 | 99.20 36 | 98.66 58 | 98.13 78 | 99.66 101 | 99.48 150 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
DELS-MVS | | | 98.19 45 | 98.77 51 | 97.52 46 | 98.29 55 | 99.71 8 | 99.12 38 | 94.58 57 | 98.80 90 | 95.38 44 | 96.24 111 | 98.24 62 | 97.92 91 | 99.06 33 | 99.52 1 | 99.82 13 | 99.79 39 |
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 |
EPP-MVSNet | | | 97.75 55 | 98.71 52 | 96.63 78 | 95.68 115 | 99.56 49 | 97.51 102 | 93.10 90 | 99.22 44 | 94.99 52 | 97.18 86 | 97.30 71 | 98.65 67 | 98.83 46 | 98.93 30 | 99.84 5 | 99.92 1 |
|
diffmvs | | | 97.50 64 | 98.63 53 | 96.18 85 | 95.88 109 | 99.26 86 | 98.19 80 | 91.08 114 | 99.36 24 | 94.32 70 | 98.24 61 | 96.83 75 | 98.22 82 | 98.45 74 | 98.42 57 | 99.42 176 | 99.86 15 |
|
DeepC-MVS | | 97.63 4 | 98.33 43 | 98.57 54 | 98.04 37 | 98.62 51 | 99.65 20 | 99.45 22 | 98.15 19 | 99.51 15 | 92.80 93 | 95.74 124 | 96.44 78 | 99.46 20 | 99.37 17 | 99.50 2 | 99.78 37 | 99.81 30 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MVS_Test | | | 97.30 71 | 98.54 55 | 95.87 94 | 95.74 113 | 99.28 84 | 98.19 80 | 91.40 108 | 99.18 50 | 91.59 102 | 98.17 62 | 96.18 81 | 98.63 69 | 98.61 64 | 98.55 51 | 99.66 101 | 99.78 41 |
|
EPNet_dtu | | | 96.30 102 | 98.53 56 | 93.70 123 | 98.97 45 | 98.24 147 | 97.36 106 | 94.23 62 | 98.85 82 | 79.18 191 | 99.19 17 | 98.47 59 | 94.09 194 | 97.89 112 | 98.21 75 | 98.39 201 | 98.85 184 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PVSNet_Blended_VisFu | | | 97.41 65 | 98.49 57 | 96.15 87 | 97.49 65 | 99.76 1 | 96.02 138 | 93.75 82 | 99.26 39 | 93.38 86 | 93.73 144 | 99.35 50 | 96.47 130 | 98.96 36 | 98.46 55 | 99.77 42 | 99.90 3 |
|
OpenMVS | | 96.23 11 | 97.95 50 | 98.45 58 | 97.35 47 | 99.52 27 | 99.42 65 | 98.91 49 | 94.61 54 | 98.87 79 | 92.24 97 | 94.61 138 | 99.05 54 | 99.10 53 | 98.64 62 | 99.05 24 | 99.74 49 | 99.51 145 |
|
PMMVS | | | 97.52 61 | 98.39 59 | 96.51 81 | 95.82 112 | 98.73 119 | 97.80 95 | 93.05 91 | 98.76 97 | 94.39 68 | 99.07 27 | 97.03 74 | 98.55 71 | 98.31 81 | 97.61 104 | 99.43 174 | 99.21 166 |
|
tfpn_n400 | | | 97.32 67 | 98.38 60 | 96.09 90 | 96.07 100 | 99.30 81 | 98.00 88 | 93.84 79 | 99.35 26 | 90.50 109 | 98.93 31 | 94.24 103 | 98.30 80 | 98.65 59 | 98.60 48 | 99.83 9 | 99.60 126 |
|
tfpnconf | | | 97.32 67 | 98.38 60 | 96.09 90 | 96.07 100 | 99.30 81 | 98.00 88 | 93.84 79 | 99.35 26 | 90.50 109 | 98.93 31 | 94.24 103 | 98.30 80 | 98.65 59 | 98.60 48 | 99.83 9 | 99.60 126 |
|
PCF-MVS | | 97.50 6 | 98.18 46 | 98.35 62 | 97.99 38 | 98.65 50 | 99.36 71 | 98.94 48 | 98.14 21 | 98.59 104 | 93.62 83 | 96.61 101 | 99.76 42 | 99.03 58 | 97.77 118 | 97.45 113 | 99.57 154 | 98.89 182 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
tfpnview11 | | | 97.32 67 | 98.33 63 | 96.14 88 | 96.07 100 | 99.31 80 | 98.08 86 | 93.96 74 | 99.25 41 | 90.50 109 | 98.93 31 | 94.24 103 | 98.38 76 | 98.61 64 | 98.36 65 | 99.84 5 | 99.59 128 |
|
tfpn_ndepth | | | 97.71 56 | 98.30 64 | 97.02 64 | 96.31 84 | 99.56 49 | 98.05 87 | 93.94 76 | 98.95 72 | 95.59 40 | 98.40 56 | 94.79 96 | 98.39 75 | 98.40 78 | 98.42 57 | 99.86 2 | 99.56 136 |
|
MSDG | | | 98.27 44 | 98.29 65 | 98.24 33 | 99.20 40 | 99.22 91 | 99.20 34 | 97.82 30 | 99.37 22 | 94.43 65 | 95.90 119 | 97.31 70 | 99.12 50 | 98.76 52 | 98.35 66 | 99.67 96 | 99.14 171 |
|
PatchMatch-RL | | | 97.77 54 | 98.25 66 | 97.21 53 | 99.11 42 | 99.25 87 | 97.06 121 | 94.09 65 | 98.72 100 | 95.14 48 | 98.47 53 | 96.29 80 | 98.43 74 | 98.65 59 | 97.44 114 | 99.45 171 | 98.94 177 |
|
LS3D | | | 97.79 52 | 98.25 66 | 97.26 52 | 98.40 53 | 99.63 30 | 99.53 15 | 98.63 1 | 99.25 41 | 88.13 121 | 96.93 93 | 94.14 106 | 99.19 38 | 99.14 28 | 99.23 17 | 99.69 80 | 99.42 154 |
|
Vis-MVSNet | | | 96.16 106 | 98.22 68 | 93.75 120 | 95.33 126 | 99.70 10 | 97.27 110 | 90.85 116 | 98.30 117 | 85.51 138 | 95.72 126 | 96.45 76 | 93.69 201 | 98.70 57 | 99.00 26 | 99.84 5 | 99.69 93 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
tfpn1000 | | | 97.60 60 | 98.21 69 | 96.89 73 | 96.32 83 | 99.60 42 | 97.99 90 | 93.85 78 | 99.21 46 | 95.03 50 | 98.49 51 | 93.69 110 | 98.31 79 | 98.50 73 | 98.31 72 | 99.86 2 | 99.70 87 |
|
Fast-Effi-MVS+-dtu | | | 95.38 120 | 98.20 70 | 92.09 150 | 93.91 141 | 98.87 107 | 97.35 107 | 85.01 186 | 99.08 60 | 81.09 162 | 98.10 63 | 96.36 79 | 95.62 150 | 98.43 77 | 97.03 121 | 99.55 158 | 99.50 147 |
|
COLMAP_ROB | | 96.15 12 | 97.78 53 | 98.17 71 | 97.32 48 | 98.84 46 | 99.45 61 | 99.28 31 | 95.43 43 | 99.48 16 | 91.80 100 | 94.83 136 | 98.36 61 | 98.90 61 | 98.09 97 | 97.85 91 | 99.68 89 | 99.15 168 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
RPSCF | | | 97.61 59 | 98.16 72 | 96.96 71 | 98.10 56 | 99.00 98 | 98.84 51 | 93.76 81 | 99.45 17 | 94.78 56 | 99.39 11 | 99.31 51 | 98.53 72 | 96.61 150 | 95.43 160 | 97.74 209 | 97.93 200 |
|
GG-mvs-BLEND | | | 69.11 228 | 98.13 73 | 35.26 234 | 3.49 240 | 98.20 149 | 94.89 159 | 2.38 238 | 98.42 114 | 5.82 243 | 96.37 109 | 98.60 56 | 5.97 238 | 98.75 54 | 97.98 85 | 99.01 192 | 98.61 186 |
|
test0.0.03 1 | | | 96.69 90 | 98.12 74 | 95.01 105 | 95.49 120 | 98.99 100 | 95.86 140 | 90.82 117 | 98.38 115 | 92.54 95 | 96.66 99 | 97.33 69 | 95.75 144 | 97.75 120 | 98.34 68 | 99.60 140 | 99.40 156 |
|
FMVSNet3 | | | 97.02 76 | 98.12 74 | 95.73 99 | 93.59 150 | 97.98 152 | 98.34 75 | 91.32 109 | 98.80 90 | 93.92 76 | 97.21 83 | 95.94 85 | 97.63 99 | 98.61 64 | 98.62 46 | 99.61 133 | 99.65 117 |
|
Effi-MVS+-dtu | | | 95.74 113 | 98.04 76 | 93.06 137 | 93.92 140 | 99.16 95 | 97.90 91 | 88.16 159 | 99.07 65 | 82.02 158 | 98.02 67 | 94.32 101 | 96.74 121 | 98.53 71 | 97.56 106 | 99.61 133 | 99.62 124 |
|
MAR-MVS | | | 97.71 56 | 98.04 76 | 97.32 48 | 99.35 36 | 98.91 105 | 97.65 99 | 91.68 101 | 98.00 129 | 97.01 26 | 97.72 75 | 94.83 94 | 98.85 62 | 98.44 76 | 98.86 36 | 99.41 177 | 99.52 141 |
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 |
CDS-MVSNet | | | 96.59 96 | 98.02 78 | 94.92 106 | 94.45 137 | 98.96 103 | 97.46 104 | 91.75 100 | 97.86 141 | 90.07 113 | 96.02 115 | 97.25 72 | 96.21 133 | 98.04 106 | 98.38 62 | 99.60 140 | 99.65 117 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
GBi-Net | | | 96.98 77 | 98.00 79 | 95.78 95 | 93.81 144 | 97.98 152 | 98.09 83 | 91.32 109 | 98.80 90 | 93.92 76 | 97.21 83 | 95.94 85 | 97.89 92 | 98.07 100 | 98.34 68 | 99.68 89 | 99.67 105 |
|
test1 | | | 96.98 77 | 98.00 79 | 95.78 95 | 93.81 144 | 97.98 152 | 98.09 83 | 91.32 109 | 98.80 90 | 93.92 76 | 97.21 83 | 95.94 85 | 97.89 92 | 98.07 100 | 98.34 68 | 99.68 89 | 99.67 105 |
|
FC-MVSNet-test | | | 96.07 108 | 97.94 81 | 93.89 117 | 93.60 149 | 98.67 122 | 96.62 128 | 90.30 128 | 98.76 97 | 88.62 118 | 95.57 130 | 97.63 67 | 94.48 188 | 97.97 108 | 97.48 112 | 99.71 69 | 99.52 141 |
|
FC-MVSNet-train | | | 97.04 75 | 97.91 82 | 96.03 92 | 96.00 105 | 98.41 139 | 96.53 131 | 93.42 85 | 99.04 68 | 93.02 91 | 98.03 66 | 94.32 101 | 97.47 103 | 97.93 110 | 97.77 99 | 99.75 45 | 99.88 11 |
|
thresconf0.02 | | | 97.18 72 | 97.81 83 | 96.45 83 | 96.11 99 | 99.20 94 | 98.21 78 | 94.26 61 | 99.14 53 | 91.72 101 | 98.65 46 | 91.51 123 | 98.57 70 | 98.22 90 | 98.47 54 | 99.82 13 | 99.50 147 |
|
canonicalmvs | | | 97.31 70 | 97.81 83 | 96.72 74 | 96.20 97 | 99.45 61 | 98.21 78 | 91.60 103 | 99.22 44 | 95.39 43 | 98.48 52 | 90.95 124 | 99.16 44 | 97.66 123 | 99.05 24 | 99.76 44 | 99.90 3 |
|
MVSTER | | | 97.16 73 | 97.71 85 | 96.52 80 | 95.97 107 | 98.48 132 | 98.63 57 | 92.10 94 | 98.68 101 | 95.96 37 | 99.23 16 | 91.79 121 | 96.87 117 | 98.76 52 | 97.37 117 | 99.57 154 | 99.68 100 |
|
PVSNet_BlendedMVS | | | 97.51 62 | 97.71 85 | 97.28 50 | 98.06 57 | 99.61 38 | 97.31 108 | 95.02 46 | 99.08 60 | 95.51 41 | 98.05 64 | 90.11 126 | 98.07 87 | 98.91 41 | 98.40 60 | 99.72 60 | 99.78 41 |
|
PVSNet_Blended | | | 97.51 62 | 97.71 85 | 97.28 50 | 98.06 57 | 99.61 38 | 97.31 108 | 95.02 46 | 99.08 60 | 95.51 41 | 98.05 64 | 90.11 126 | 98.07 87 | 98.91 41 | 98.40 60 | 99.72 60 | 99.78 41 |
|
IterMVS | | | 94.81 130 | 97.71 85 | 91.42 169 | 94.83 135 | 97.63 174 | 97.38 105 | 85.08 184 | 98.93 77 | 75.67 206 | 94.02 141 | 97.64 66 | 96.66 124 | 98.45 74 | 97.60 105 | 98.90 194 | 99.72 81 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MIMVSNet | | | 94.49 138 | 97.59 89 | 90.87 186 | 91.74 186 | 98.70 121 | 94.68 180 | 78.73 220 | 97.98 130 | 83.71 146 | 97.71 76 | 94.81 95 | 96.96 114 | 97.97 108 | 97.92 87 | 99.40 179 | 98.04 198 |
|
FMVSNet2 | | | 96.64 93 | 97.50 90 | 95.63 101 | 93.81 144 | 97.98 152 | 98.09 83 | 90.87 115 | 98.99 71 | 93.48 84 | 93.17 151 | 95.25 90 | 97.89 92 | 98.63 63 | 98.80 40 | 99.68 89 | 99.67 105 |
|
DI_MVS_plusplus_trai | | | 96.90 80 | 97.49 91 | 96.21 84 | 95.61 117 | 99.40 67 | 98.72 55 | 92.11 93 | 99.14 53 | 92.98 92 | 93.08 154 | 95.14 91 | 98.13 85 | 98.05 104 | 97.91 88 | 99.74 49 | 99.73 69 |
|
testgi | | | 95.67 114 | 97.48 92 | 93.56 126 | 95.07 130 | 99.00 98 | 95.33 151 | 88.47 153 | 98.80 90 | 86.90 130 | 97.30 81 | 92.33 118 | 95.97 141 | 97.66 123 | 97.91 88 | 99.60 140 | 99.38 157 |
|
MDTV_nov1_ep13 | | | 95.57 115 | 97.48 92 | 93.35 134 | 95.43 122 | 98.97 102 | 97.19 114 | 83.72 200 | 98.92 78 | 87.91 124 | 97.75 73 | 96.12 83 | 97.88 95 | 96.84 149 | 95.64 158 | 97.96 207 | 98.10 196 |
|
IterMVS-LS | | | 96.12 107 | 97.48 92 | 94.53 109 | 95.19 128 | 97.56 180 | 97.15 115 | 89.19 145 | 99.08 60 | 88.23 120 | 94.97 134 | 94.73 97 | 97.84 96 | 97.86 114 | 98.26 74 | 99.60 140 | 99.88 11 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchT | | | 93.96 146 | 97.36 95 | 90.00 198 | 94.76 136 | 98.65 123 | 90.11 214 | 78.57 221 | 97.96 133 | 80.42 170 | 96.07 114 | 94.10 107 | 96.85 118 | 98.10 95 | 97.49 110 | 99.26 186 | 99.15 168 |
|
CR-MVSNet | | | 94.57 137 | 97.34 96 | 91.33 171 | 94.90 133 | 98.59 127 | 97.15 115 | 79.14 216 | 97.98 130 | 80.42 170 | 96.59 104 | 93.50 112 | 96.85 118 | 98.10 95 | 97.49 110 | 99.50 166 | 99.15 168 |
|
test-LLR | | | 95.50 117 | 97.32 97 | 93.37 132 | 95.49 120 | 98.74 117 | 96.44 133 | 90.82 117 | 98.18 121 | 82.75 153 | 96.60 102 | 94.67 98 | 95.54 153 | 98.09 97 | 96.00 147 | 99.20 188 | 98.93 178 |
|
TESTMET0.1,1 | | | 94.95 128 | 97.32 97 | 92.20 147 | 92.62 155 | 98.74 117 | 96.44 133 | 86.67 172 | 98.18 121 | 82.75 153 | 96.60 102 | 94.67 98 | 95.54 153 | 98.09 97 | 96.00 147 | 99.20 188 | 98.93 178 |
|
test-mter | | | 94.86 129 | 97.32 97 | 92.00 154 | 92.41 159 | 98.82 109 | 96.18 137 | 86.35 176 | 98.05 127 | 82.28 156 | 96.48 106 | 94.39 100 | 95.46 163 | 98.17 92 | 96.20 143 | 99.32 183 | 99.13 172 |
|
Effi-MVS+ | | | 95.81 111 | 97.31 100 | 94.06 115 | 95.09 129 | 99.35 73 | 97.24 112 | 88.22 156 | 98.54 108 | 85.38 139 | 98.52 49 | 88.68 130 | 98.70 65 | 98.32 80 | 97.93 86 | 99.74 49 | 99.84 20 |
|
MS-PatchMatch | | | 95.99 109 | 97.26 101 | 94.51 110 | 97.46 66 | 98.76 115 | 97.27 110 | 86.97 169 | 99.09 58 | 89.83 116 | 93.51 146 | 97.78 65 | 96.18 135 | 97.53 130 | 95.71 157 | 99.35 181 | 98.41 191 |
|
RPMNet | | | 94.66 132 | 97.16 102 | 91.75 163 | 94.98 131 | 98.59 127 | 97.00 122 | 78.37 222 | 97.98 130 | 83.78 143 | 96.27 110 | 94.09 108 | 96.91 115 | 97.36 134 | 96.73 127 | 99.48 167 | 99.09 173 |
|
CVMVSNet | | | 95.33 123 | 97.09 103 | 93.27 135 | 95.23 127 | 98.39 141 | 95.49 147 | 92.58 92 | 97.71 148 | 83.00 152 | 94.44 140 | 93.28 113 | 93.92 198 | 97.79 116 | 98.54 53 | 99.41 177 | 99.45 152 |
|
PatchmatchNet | | | 94.70 131 | 97.08 104 | 91.92 157 | 95.53 118 | 98.85 108 | 95.77 141 | 79.54 213 | 98.95 72 | 85.98 134 | 98.52 49 | 96.45 76 | 97.39 105 | 95.32 178 | 94.09 202 | 97.32 219 | 97.38 207 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
ADS-MVSNet | | | 94.65 133 | 97.04 105 | 91.88 160 | 95.68 115 | 98.99 100 | 95.89 139 | 79.03 218 | 99.15 51 | 85.81 136 | 96.96 91 | 98.21 63 | 97.10 110 | 94.48 205 | 94.24 200 | 97.74 209 | 97.21 208 |
|
CHOSEN 1792x2688 | | | 96.41 97 | 96.99 106 | 95.74 98 | 98.01 60 | 99.72 4 | 97.70 98 | 90.78 119 | 99.13 57 | 90.03 114 | 87.35 203 | 95.36 89 | 98.33 78 | 98.59 68 | 98.91 33 | 99.59 146 | 99.87 13 |
|
tfpn111 | | | 96.96 79 | 96.91 107 | 97.03 60 | 96.31 84 | 99.67 13 | 98.41 64 | 93.99 68 | 97.35 154 | 94.50 61 | 98.65 46 | 86.93 138 | 99.14 45 | 98.26 84 | 97.80 94 | 99.82 13 | 99.70 87 |
|
LGP-MVS_train | | | 96.23 103 | 96.89 108 | 95.46 102 | 97.32 69 | 98.77 113 | 98.81 52 | 93.60 83 | 98.58 105 | 85.52 137 | 99.08 26 | 86.67 145 | 97.83 97 | 97.87 113 | 97.51 108 | 99.69 80 | 99.73 69 |
|
EPMVS | | | 95.05 126 | 96.86 109 | 92.94 140 | 95.84 111 | 98.96 103 | 96.68 125 | 79.87 209 | 99.05 66 | 90.15 112 | 97.12 87 | 95.99 84 | 97.49 102 | 95.17 186 | 94.75 195 | 97.59 214 | 96.96 212 |
|
ACMP | | 96.25 10 | 96.62 95 | 96.72 110 | 96.50 82 | 96.96 78 | 98.75 116 | 97.80 95 | 94.30 60 | 98.85 82 | 93.12 89 | 98.78 42 | 86.61 146 | 97.23 108 | 97.73 121 | 96.61 131 | 99.62 130 | 99.71 85 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMM | | 96.26 9 | 96.67 92 | 96.69 111 | 96.66 76 | 97.29 72 | 98.46 133 | 96.48 132 | 95.09 45 | 99.21 46 | 93.19 88 | 98.78 42 | 86.73 144 | 98.17 83 | 97.84 115 | 96.32 139 | 99.74 49 | 99.49 149 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
HQP-MVS | | | 96.37 98 | 96.58 112 | 96.13 89 | 97.31 71 | 98.44 136 | 98.45 62 | 95.22 44 | 98.86 80 | 88.58 119 | 98.33 58 | 87.00 137 | 97.67 98 | 97.23 138 | 96.56 133 | 99.56 157 | 99.62 124 |
|
HyFIR lowres test | | | 95.99 109 | 96.56 113 | 95.32 103 | 97.99 61 | 99.65 20 | 96.54 129 | 88.86 147 | 98.44 113 | 89.77 117 | 84.14 215 | 97.05 73 | 99.03 58 | 98.55 70 | 98.19 77 | 99.73 55 | 99.86 15 |
|
TSAR-MVS + COLMAP | | | 96.79 81 | 96.55 114 | 97.06 58 | 97.70 64 | 98.46 133 | 99.07 42 | 96.23 38 | 99.38 20 | 91.32 104 | 98.80 40 | 85.61 155 | 98.69 66 | 97.64 126 | 96.92 124 | 99.37 180 | 99.06 175 |
|
thres200 | | | 96.76 82 | 96.53 115 | 97.03 60 | 96.31 84 | 99.67 13 | 98.37 72 | 93.99 68 | 97.68 149 | 94.49 63 | 95.83 123 | 86.77 143 | 99.18 40 | 98.26 84 | 97.82 93 | 99.82 13 | 99.66 114 |
|
Fast-Effi-MVS+ | | | 95.38 120 | 96.52 116 | 94.05 116 | 94.15 139 | 99.14 96 | 97.24 112 | 86.79 170 | 98.53 109 | 87.62 126 | 94.51 139 | 87.06 135 | 98.76 63 | 98.60 67 | 98.04 83 | 99.72 60 | 99.77 49 |
|
conf200view11 | | | 96.75 83 | 96.51 117 | 97.03 60 | 96.31 84 | 99.67 13 | 98.41 64 | 93.99 68 | 97.35 154 | 94.50 61 | 95.90 119 | 86.93 138 | 99.14 45 | 98.26 84 | 97.80 94 | 99.82 13 | 99.70 87 |
|
tfpn200view9 | | | 96.75 83 | 96.51 117 | 97.03 60 | 96.31 84 | 99.67 13 | 98.41 64 | 93.99 68 | 97.35 154 | 94.52 59 | 95.90 119 | 86.93 138 | 99.14 45 | 98.26 84 | 97.80 94 | 99.82 13 | 99.70 87 |
|
CLD-MVS | | | 96.74 85 | 96.51 117 | 97.01 66 | 96.71 80 | 98.62 125 | 98.73 54 | 94.38 58 | 98.94 75 | 94.46 64 | 97.33 79 | 87.03 136 | 98.07 87 | 97.20 140 | 96.87 125 | 99.72 60 | 99.54 138 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
TAMVS | | | 95.53 116 | 96.50 120 | 94.39 112 | 93.86 143 | 99.03 97 | 96.67 126 | 89.55 142 | 97.33 160 | 90.64 107 | 93.02 155 | 91.58 122 | 96.21 133 | 97.72 122 | 97.43 115 | 99.43 174 | 99.36 158 |
|
thres100view900 | | | 96.72 86 | 96.47 121 | 97.00 68 | 96.31 84 | 99.52 55 | 98.28 77 | 94.01 66 | 97.35 154 | 94.52 59 | 95.90 119 | 86.93 138 | 99.09 55 | 98.07 100 | 97.87 90 | 99.81 26 | 99.63 123 |
|
conf0.05thres1000 | | | 96.34 100 | 96.47 121 | 96.17 86 | 96.16 98 | 99.71 8 | 97.82 93 | 93.46 84 | 98.10 125 | 90.69 106 | 96.75 95 | 85.26 160 | 99.11 52 | 98.05 104 | 97.65 101 | 99.82 13 | 99.80 32 |
|
FMVSNet5 | | | 95.42 118 | 96.47 121 | 94.20 113 | 92.26 161 | 95.99 205 | 95.66 143 | 87.15 166 | 97.87 139 | 93.46 85 | 96.68 98 | 93.79 109 | 97.52 100 | 97.10 144 | 97.21 119 | 99.11 191 | 96.62 218 |
|
view800 | | | 96.70 88 | 96.45 124 | 96.99 70 | 96.29 91 | 99.69 11 | 98.39 71 | 93.95 75 | 97.92 136 | 94.25 72 | 96.23 112 | 85.57 156 | 99.22 33 | 98.28 82 | 97.71 100 | 99.82 13 | 99.76 53 |
|
thres400 | | | 96.71 87 | 96.45 124 | 97.02 64 | 96.28 94 | 99.63 30 | 98.41 64 | 94.00 67 | 97.82 144 | 94.42 66 | 95.74 124 | 86.26 149 | 99.18 40 | 98.20 91 | 97.79 98 | 99.81 26 | 99.70 87 |
|
view600 | | | 96.70 88 | 96.44 126 | 97.01 66 | 96.28 94 | 99.67 13 | 98.42 63 | 93.99 68 | 97.87 139 | 94.34 69 | 95.99 116 | 85.94 152 | 99.20 36 | 98.26 84 | 97.64 102 | 99.82 13 | 99.73 69 |
|
IB-MVS | | 93.96 15 | 95.02 127 | 96.44 126 | 93.36 133 | 97.05 77 | 99.28 84 | 90.43 211 | 93.39 86 | 98.02 128 | 96.02 36 | 94.92 135 | 92.07 120 | 83.52 223 | 95.38 177 | 95.82 153 | 99.72 60 | 99.59 128 |
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 |
thres600view7 | | | 96.69 90 | 96.43 128 | 97.00 68 | 96.28 94 | 99.67 13 | 98.41 64 | 93.99 68 | 97.85 142 | 94.29 71 | 95.96 117 | 85.91 153 | 99.19 38 | 98.26 84 | 97.63 103 | 99.82 13 | 99.73 69 |
|
FMVSNet1 | | | 95.77 112 | 96.41 129 | 95.03 104 | 93.42 151 | 97.86 159 | 97.11 118 | 89.89 136 | 98.53 109 | 92.00 98 | 89.17 178 | 93.23 114 | 98.15 84 | 98.07 100 | 98.34 68 | 99.61 133 | 99.69 93 |
|
GA-MVS | | | 93.93 147 | 96.31 130 | 91.16 177 | 93.61 148 | 98.79 110 | 95.39 150 | 90.69 121 | 98.25 119 | 73.28 214 | 96.15 113 | 88.42 131 | 94.39 190 | 97.76 119 | 95.35 164 | 99.58 150 | 99.45 152 |
|
ACMH+ | | 95.51 13 | 95.40 119 | 96.00 131 | 94.70 108 | 96.33 82 | 98.79 110 | 96.79 124 | 91.32 109 | 98.77 96 | 87.18 128 | 95.60 129 | 85.46 157 | 96.97 113 | 97.15 141 | 96.59 132 | 99.59 146 | 99.65 117 |
|
MVS-HIRNet | | | 92.51 180 | 95.97 132 | 88.48 207 | 93.73 147 | 98.37 142 | 90.33 212 | 75.36 230 | 98.32 116 | 77.78 197 | 89.15 179 | 94.87 93 | 95.14 180 | 97.62 127 | 96.39 137 | 98.51 197 | 97.11 209 |
|
ACMH | | 95.42 14 | 95.27 124 | 95.96 133 | 94.45 111 | 96.83 79 | 98.78 112 | 94.72 178 | 91.67 102 | 98.95 72 | 86.82 131 | 96.42 108 | 83.67 172 | 97.00 112 | 97.48 132 | 96.68 129 | 99.69 80 | 99.76 53 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
pmmvs4 | | | 95.09 125 | 95.90 134 | 94.14 114 | 92.29 160 | 97.70 166 | 95.45 148 | 90.31 126 | 98.60 103 | 90.70 105 | 93.25 149 | 89.90 128 | 96.67 123 | 97.13 142 | 95.42 161 | 99.44 173 | 99.28 161 |
|
tpmrst | | | 93.86 149 | 95.88 135 | 91.50 167 | 95.69 114 | 98.62 125 | 95.64 144 | 79.41 214 | 98.80 90 | 83.76 145 | 95.63 128 | 96.13 82 | 97.25 106 | 92.92 211 | 92.31 217 | 97.27 220 | 96.74 215 |
|
anonymousdsp | | | 93.12 158 | 95.86 136 | 89.93 200 | 91.09 205 | 98.25 146 | 95.12 152 | 85.08 184 | 97.44 152 | 73.30 213 | 90.89 160 | 90.78 125 | 95.25 178 | 97.91 111 | 95.96 151 | 99.71 69 | 99.82 25 |
|
OPM-MVS | | | 96.22 104 | 95.85 137 | 96.65 77 | 97.75 62 | 98.54 130 | 99.00 47 | 95.53 41 | 96.88 178 | 89.88 115 | 95.95 118 | 86.46 148 | 98.07 87 | 97.65 125 | 96.63 130 | 99.67 96 | 98.83 185 |
|
conf0.01 | | | 96.35 99 | 95.71 138 | 97.10 55 | 96.30 90 | 99.65 20 | 98.41 64 | 94.10 64 | 97.35 154 | 94.82 55 | 95.44 132 | 81.88 199 | 99.14 45 | 98.16 93 | 97.80 94 | 99.82 13 | 99.69 93 |
|
MDTV_nov1_ep13_2view | | | 92.44 182 | 95.66 139 | 88.68 205 | 91.05 206 | 97.92 156 | 92.17 204 | 79.64 211 | 98.83 85 | 76.20 204 | 91.45 157 | 93.51 111 | 95.04 181 | 95.68 175 | 93.70 205 | 97.96 207 | 98.53 188 |
|
conf0.002 | | | 96.31 101 | 95.63 140 | 97.11 54 | 96.29 91 | 99.64 25 | 98.41 64 | 94.11 63 | 97.35 154 | 94.86 53 | 95.49 131 | 81.06 204 | 99.14 45 | 98.14 94 | 98.02 84 | 99.82 13 | 99.69 93 |
|
tfpn | | | 96.22 104 | 95.62 141 | 96.93 72 | 96.29 91 | 99.72 4 | 98.34 75 | 93.94 76 | 97.96 133 | 93.94 75 | 96.45 107 | 79.09 214 | 99.22 33 | 98.28 82 | 98.06 81 | 99.83 9 | 99.78 41 |
|
pm-mvs1 | | | 94.27 139 | 95.57 142 | 92.75 141 | 92.58 156 | 98.13 150 | 94.87 163 | 90.71 120 | 96.70 184 | 83.78 143 | 89.94 173 | 89.85 129 | 94.96 183 | 97.58 128 | 97.07 120 | 99.61 133 | 99.72 81 |
|
UniMVSNet_NR-MVSNet | | | 94.59 135 | 95.47 143 | 93.55 127 | 91.85 175 | 97.89 158 | 95.03 154 | 92.00 96 | 97.33 160 | 86.12 132 | 93.19 150 | 87.29 134 | 96.60 126 | 96.12 168 | 96.70 128 | 99.72 60 | 99.80 32 |
|
UniMVSNet (Re) | | | 94.58 136 | 95.34 144 | 93.71 122 | 92.25 162 | 98.08 151 | 94.97 156 | 91.29 113 | 97.03 171 | 87.94 123 | 93.97 143 | 86.25 150 | 96.07 138 | 96.27 165 | 95.97 150 | 99.72 60 | 99.79 39 |
|
SixPastTwentyTwo | | | 93.44 155 | 95.32 145 | 91.24 175 | 92.11 165 | 98.40 140 | 92.77 201 | 88.64 152 | 98.09 126 | 77.83 196 | 93.51 146 | 85.74 154 | 96.52 129 | 96.91 147 | 94.89 192 | 99.59 146 | 99.73 69 |
|
dps | | | 94.63 134 | 95.31 146 | 93.84 118 | 95.53 118 | 98.71 120 | 96.54 129 | 80.12 208 | 97.81 146 | 97.21 23 | 96.98 90 | 92.37 117 | 96.34 132 | 92.46 217 | 91.77 221 | 97.26 221 | 97.08 210 |
|
DWT-MVSNet_training | | | 95.38 120 | 95.05 147 | 95.78 95 | 95.86 110 | 98.88 106 | 97.55 101 | 90.09 132 | 98.23 120 | 96.49 34 | 97.62 78 | 86.92 142 | 97.16 109 | 92.03 220 | 94.12 201 | 97.52 215 | 97.50 203 |
|
LTVRE_ROB | | 93.20 16 | 92.84 166 | 94.92 148 | 90.43 194 | 92.83 153 | 98.63 124 | 97.08 120 | 87.87 162 | 97.91 137 | 68.42 221 | 93.54 145 | 79.46 213 | 96.62 125 | 97.55 129 | 97.40 116 | 99.74 49 | 99.92 1 |
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 |
tpm cat1 | | | 94.06 142 | 94.90 149 | 93.06 137 | 95.42 124 | 98.52 131 | 96.64 127 | 80.67 204 | 97.82 144 | 92.63 94 | 93.39 148 | 95.00 92 | 96.06 139 | 91.36 224 | 91.58 223 | 96.98 224 | 96.66 217 |
|
CostFormer | | | 94.25 141 | 94.88 150 | 93.51 129 | 95.43 122 | 98.34 143 | 96.21 136 | 80.64 205 | 97.94 135 | 94.01 73 | 98.30 59 | 86.20 151 | 97.52 100 | 92.71 212 | 92.69 212 | 97.23 223 | 98.02 199 |
|
USDC | | | 94.26 140 | 94.83 151 | 93.59 125 | 96.02 103 | 98.44 136 | 97.84 92 | 88.65 151 | 98.86 80 | 82.73 155 | 94.02 141 | 80.56 205 | 96.76 120 | 97.28 137 | 96.15 146 | 99.55 158 | 98.50 189 |
|
tpm | | | 92.38 186 | 94.79 152 | 89.56 201 | 94.30 138 | 97.50 185 | 94.24 192 | 78.97 219 | 97.72 147 | 74.93 210 | 97.97 68 | 82.91 183 | 96.60 126 | 93.65 210 | 94.81 193 | 98.33 202 | 98.98 176 |
|
EU-MVSNet | | | 92.80 169 | 94.76 153 | 90.51 192 | 91.88 173 | 96.74 202 | 92.48 203 | 88.69 150 | 96.21 199 | 79.00 192 | 91.51 156 | 87.82 132 | 91.83 209 | 95.87 173 | 96.27 140 | 99.21 187 | 98.92 181 |
|
WR-MVS_H | | | 93.54 153 | 94.67 154 | 92.22 145 | 91.95 171 | 97.91 157 | 94.58 186 | 88.75 149 | 96.64 188 | 83.88 142 | 90.66 163 | 85.13 161 | 94.40 189 | 96.54 155 | 95.91 152 | 99.73 55 | 99.89 7 |
|
LP | | | 92.12 196 | 94.60 155 | 89.22 203 | 94.96 132 | 98.45 135 | 93.01 199 | 77.58 223 | 97.85 142 | 77.26 200 | 89.80 174 | 93.00 115 | 94.54 185 | 93.69 208 | 92.58 213 | 98.00 206 | 96.83 214 |
|
N_pmnet | | | 92.21 193 | 94.60 155 | 89.42 202 | 91.88 173 | 97.38 192 | 89.15 216 | 89.74 140 | 97.89 138 | 73.75 212 | 87.94 200 | 92.23 119 | 93.85 199 | 96.10 169 | 93.20 208 | 98.15 205 | 97.43 206 |
|
tpmp4_e23 | | | 93.84 151 | 94.58 157 | 92.98 139 | 95.41 125 | 98.29 144 | 96.81 123 | 80.57 206 | 98.15 123 | 90.53 108 | 97.00 89 | 84.39 168 | 96.91 115 | 93.69 208 | 92.45 215 | 97.67 212 | 98.06 197 |
|
NR-MVSNet | | | 94.01 143 | 94.51 158 | 93.44 130 | 92.56 157 | 97.77 160 | 95.67 142 | 91.57 104 | 97.17 165 | 85.84 135 | 93.13 152 | 80.53 206 | 95.29 176 | 97.01 145 | 96.17 144 | 99.69 80 | 99.75 62 |
|
WR-MVS | | | 93.43 156 | 94.48 159 | 92.21 146 | 91.52 197 | 97.69 170 | 94.66 182 | 89.98 134 | 96.86 179 | 83.43 147 | 90.12 165 | 85.03 162 | 93.94 197 | 96.02 171 | 95.82 153 | 99.71 69 | 99.82 25 |
|
DU-MVS | | | 93.98 145 | 94.44 160 | 93.44 130 | 91.66 190 | 97.77 160 | 95.03 154 | 91.57 104 | 97.17 165 | 86.12 132 | 93.13 152 | 81.13 203 | 96.60 126 | 95.10 197 | 97.01 123 | 99.67 96 | 99.80 32 |
|
testpf | | | 91.80 201 | 94.43 161 | 88.74 204 | 93.89 142 | 95.30 218 | 92.05 205 | 71.77 231 | 97.52 151 | 87.24 127 | 94.77 137 | 92.68 116 | 91.48 210 | 91.75 223 | 92.11 220 | 96.02 228 | 96.89 213 |
|
TinyColmap | | | 94.00 144 | 94.35 162 | 93.60 124 | 95.89 108 | 98.26 145 | 97.49 103 | 88.82 148 | 98.56 107 | 83.21 149 | 91.28 159 | 80.48 207 | 96.68 122 | 97.34 135 | 96.26 142 | 99.53 163 | 98.24 194 |
|
pmmvs5 | | | 92.71 176 | 94.27 163 | 90.90 184 | 91.42 199 | 97.74 162 | 93.23 197 | 86.66 173 | 95.99 206 | 78.96 193 | 91.45 157 | 83.44 174 | 95.55 152 | 97.30 136 | 95.05 173 | 99.58 150 | 98.93 178 |
|
gg-mvs-nofinetune | | | 90.85 204 | 94.14 164 | 87.02 210 | 94.89 134 | 99.25 87 | 98.64 56 | 76.29 227 | 88.24 228 | 57.50 232 | 79.93 223 | 95.45 88 | 95.18 179 | 98.77 51 | 98.07 80 | 99.62 130 | 99.24 164 |
|
TranMVSNet+NR-MVSNet | | | 93.67 152 | 94.14 164 | 93.13 136 | 91.28 204 | 97.58 179 | 95.60 145 | 91.97 97 | 97.06 169 | 84.05 140 | 90.64 164 | 82.22 194 | 96.17 136 | 94.94 201 | 96.78 126 | 99.69 80 | 99.78 41 |
|
tfpnnormal | | | 93.85 150 | 94.12 166 | 93.54 128 | 93.22 152 | 98.24 147 | 95.45 148 | 91.96 98 | 94.61 216 | 83.91 141 | 90.74 161 | 81.75 201 | 97.04 111 | 97.49 131 | 96.16 145 | 99.68 89 | 99.84 20 |
|
v7 | | | 92.97 164 | 94.11 167 | 91.65 166 | 91.83 176 | 97.55 182 | 94.86 166 | 88.19 158 | 96.96 174 | 79.72 182 | 88.16 195 | 84.68 165 | 95.63 148 | 96.33 162 | 95.30 166 | 99.65 107 | 99.77 49 |
|
TransMVSNet (Re) | | | 93.45 154 | 94.08 168 | 92.72 142 | 92.83 153 | 97.62 177 | 94.94 157 | 91.54 106 | 95.65 212 | 83.06 151 | 88.93 181 | 83.53 173 | 94.25 191 | 97.41 133 | 97.03 121 | 99.67 96 | 98.40 193 |
|
v10 | | | 92.79 171 | 94.06 169 | 91.31 173 | 91.78 181 | 97.29 196 | 94.87 163 | 86.10 177 | 96.97 173 | 79.82 179 | 88.16 195 | 84.56 166 | 95.63 148 | 96.33 162 | 95.31 165 | 99.65 107 | 99.80 32 |
|
v1144 | | | 92.81 167 | 94.03 170 | 91.40 170 | 91.68 189 | 97.60 178 | 94.73 177 | 88.40 154 | 96.71 183 | 78.48 194 | 88.14 197 | 84.46 167 | 95.45 164 | 96.31 164 | 95.22 168 | 99.65 107 | 99.76 53 |
|
CP-MVSNet | | | 93.25 157 | 94.00 171 | 92.38 144 | 91.65 192 | 97.56 180 | 94.38 189 | 89.20 144 | 96.05 204 | 83.16 150 | 89.51 176 | 81.97 198 | 96.16 137 | 96.43 157 | 96.56 133 | 99.71 69 | 99.89 7 |
|
v6 | | | 93.11 159 | 93.98 172 | 92.10 149 | 92.01 168 | 97.71 163 | 94.86 166 | 90.15 129 | 96.96 174 | 80.47 169 | 90.01 168 | 83.26 176 | 95.48 157 | 95.17 186 | 95.01 179 | 99.64 118 | 99.76 53 |
|
Baseline_NR-MVSNet | | | 93.87 148 | 93.98 172 | 93.75 120 | 91.66 190 | 97.02 197 | 95.53 146 | 91.52 107 | 97.16 167 | 87.77 125 | 87.93 201 | 83.69 171 | 96.35 131 | 95.10 197 | 97.23 118 | 99.68 89 | 99.73 69 |
|
v1neww | | | 93.06 160 | 93.94 174 | 92.03 152 | 91.99 169 | 97.70 166 | 94.79 170 | 90.14 130 | 96.93 176 | 80.13 174 | 89.97 170 | 83.01 180 | 95.48 157 | 95.16 190 | 95.01 179 | 99.63 124 | 99.76 53 |
|
v7new | | | 93.06 160 | 93.94 174 | 92.03 152 | 91.99 169 | 97.70 166 | 94.79 170 | 90.14 130 | 96.93 176 | 80.13 174 | 89.97 170 | 83.01 180 | 95.48 157 | 95.16 190 | 95.01 179 | 99.63 124 | 99.76 53 |
|
Anonymous20231206 | | | 90.70 206 | 93.93 176 | 86.92 211 | 90.21 214 | 96.79 200 | 90.30 213 | 86.61 174 | 96.05 204 | 69.25 219 | 88.46 192 | 84.86 164 | 85.86 218 | 97.11 143 | 96.47 136 | 99.30 184 | 97.80 202 |
|
EG-PatchMatch MVS | | | 92.45 181 | 93.92 177 | 90.72 189 | 92.56 157 | 98.43 138 | 94.88 162 | 84.54 190 | 97.18 164 | 79.55 185 | 86.12 213 | 83.23 177 | 93.15 204 | 97.22 139 | 96.00 147 | 99.67 96 | 99.27 162 |
|
V42 | | | 93.05 162 | 93.90 178 | 92.04 151 | 91.91 172 | 97.66 172 | 94.91 158 | 89.91 135 | 96.85 180 | 80.58 167 | 89.66 175 | 83.43 175 | 95.37 169 | 95.03 200 | 94.90 190 | 99.59 146 | 99.78 41 |
|
v8 | | | 92.87 165 | 93.87 179 | 91.72 165 | 92.05 167 | 97.50 185 | 94.79 170 | 88.20 157 | 96.85 180 | 80.11 176 | 90.01 168 | 82.86 185 | 95.48 157 | 95.15 194 | 94.90 190 | 99.66 101 | 99.80 32 |
|
v16 | | | 92.66 177 | 93.80 180 | 91.32 172 | 92.13 163 | 95.62 208 | 94.89 159 | 85.12 183 | 97.20 163 | 80.66 165 | 89.96 172 | 83.93 170 | 95.49 156 | 95.17 186 | 95.04 174 | 99.63 124 | 99.68 100 |
|
v11 | | | 92.43 183 | 93.77 181 | 90.85 187 | 91.72 187 | 95.58 213 | 94.87 163 | 84.07 199 | 96.98 172 | 79.28 188 | 88.03 198 | 84.22 169 | 95.53 155 | 96.55 154 | 95.36 163 | 99.65 107 | 99.70 87 |
|
test20.03 | | | 90.65 207 | 93.71 182 | 87.09 209 | 90.44 212 | 96.24 203 | 89.74 215 | 85.46 180 | 95.59 213 | 72.99 215 | 90.68 162 | 85.33 158 | 84.41 221 | 95.94 172 | 95.10 172 | 99.52 164 | 97.06 211 |
|
v18 | | | 92.63 178 | 93.67 183 | 91.43 168 | 92.13 163 | 95.65 206 | 95.09 153 | 85.44 181 | 97.06 169 | 80.78 164 | 90.06 166 | 83.06 178 | 95.47 162 | 95.16 190 | 95.01 179 | 99.64 118 | 99.67 105 |
|
v17 | | | 92.55 179 | 93.65 184 | 91.27 174 | 92.11 165 | 95.63 207 | 94.89 159 | 85.15 182 | 97.12 168 | 80.39 173 | 90.02 167 | 83.02 179 | 95.45 164 | 95.17 186 | 94.92 189 | 99.66 101 | 99.68 100 |
|
v1192 | | | 92.43 183 | 93.61 185 | 91.05 178 | 91.53 196 | 97.43 189 | 94.61 184 | 87.99 160 | 96.60 189 | 76.72 202 | 87.11 205 | 82.74 186 | 95.85 143 | 96.35 161 | 95.30 166 | 99.60 140 | 99.74 65 |
|
v1141 | | | 92.79 171 | 93.61 185 | 91.84 162 | 91.75 183 | 97.71 163 | 94.74 176 | 90.33 123 | 96.58 191 | 79.21 190 | 88.59 188 | 82.53 190 | 95.36 170 | 95.16 190 | 94.96 186 | 99.63 124 | 99.72 81 |
|
divwei89l23v2f112 | | | 92.80 169 | 93.60 187 | 91.86 161 | 91.75 183 | 97.71 163 | 94.75 175 | 90.32 124 | 96.54 193 | 79.35 187 | 88.59 188 | 82.55 189 | 95.35 171 | 95.15 194 | 94.96 186 | 99.63 124 | 99.72 81 |
|
v1921920 | | | 92.36 188 | 93.57 188 | 90.94 183 | 91.39 200 | 97.39 191 | 94.70 179 | 87.63 164 | 96.60 189 | 76.63 203 | 86.98 206 | 82.89 184 | 95.75 144 | 96.26 166 | 95.14 171 | 99.55 158 | 99.73 69 |
|
v1 | | | 92.81 167 | 93.57 188 | 91.94 156 | 91.79 180 | 97.70 166 | 94.80 169 | 90.32 124 | 96.52 194 | 79.75 180 | 88.47 191 | 82.46 191 | 95.32 173 | 95.14 196 | 94.96 186 | 99.63 124 | 99.73 69 |
|
TDRefinement | | | 93.04 163 | 93.57 188 | 92.41 143 | 96.58 81 | 98.77 113 | 97.78 97 | 91.96 98 | 98.12 124 | 80.84 163 | 89.13 180 | 79.87 211 | 87.78 214 | 96.44 156 | 94.50 199 | 99.54 162 | 98.15 195 |
|
v144192 | | | 92.38 186 | 93.55 191 | 91.00 181 | 91.44 198 | 97.47 188 | 94.27 190 | 87.41 165 | 96.52 194 | 78.03 195 | 87.50 202 | 82.65 187 | 95.32 173 | 95.82 174 | 95.15 170 | 99.55 158 | 99.78 41 |
|
v2v482 | | | 92.77 173 | 93.52 192 | 91.90 159 | 91.59 195 | 97.63 174 | 94.57 187 | 90.31 126 | 96.80 182 | 79.22 189 | 88.74 185 | 81.55 202 | 96.04 140 | 95.26 179 | 94.97 185 | 99.66 101 | 99.69 93 |
|
V14 | | | 92.31 190 | 93.41 193 | 91.03 180 | 91.80 179 | 95.59 211 | 94.79 170 | 84.70 188 | 96.58 191 | 79.83 178 | 88.79 184 | 82.98 182 | 95.41 166 | 95.22 180 | 95.02 178 | 99.65 107 | 99.67 105 |
|
PS-CasMVS | | | 92.72 174 | 93.36 194 | 91.98 155 | 91.62 194 | 97.52 183 | 94.13 193 | 88.98 146 | 95.94 207 | 81.51 161 | 87.35 203 | 79.95 210 | 95.91 142 | 96.37 159 | 96.49 135 | 99.70 78 | 99.89 7 |
|
v1240 | | | 91.99 197 | 93.33 195 | 90.44 193 | 91.29 202 | 97.30 195 | 94.25 191 | 86.79 170 | 96.43 198 | 75.49 208 | 86.34 211 | 81.85 200 | 95.29 176 | 96.42 158 | 95.22 168 | 99.52 164 | 99.73 69 |
|
v15 | | | 92.27 191 | 93.33 195 | 91.04 179 | 91.83 176 | 95.60 209 | 94.79 170 | 84.88 187 | 96.66 186 | 79.66 183 | 88.72 186 | 82.45 192 | 95.40 167 | 95.19 185 | 95.00 183 | 99.65 107 | 99.67 105 |
|
V9 | | | 92.24 192 | 93.32 197 | 90.98 182 | 91.76 182 | 95.58 213 | 94.83 168 | 84.50 192 | 96.68 185 | 79.73 181 | 88.66 187 | 82.39 193 | 95.39 168 | 95.22 180 | 95.03 176 | 99.65 107 | 99.67 105 |
|
v12 | | | 92.18 194 | 93.29 198 | 90.88 185 | 91.70 188 | 95.59 211 | 94.61 184 | 84.36 194 | 96.65 187 | 79.59 184 | 88.85 182 | 82.03 197 | 95.35 171 | 95.22 180 | 95.04 174 | 99.65 107 | 99.68 100 |
|
v13 | | | 92.16 195 | 93.28 199 | 90.85 187 | 91.75 183 | 95.58 213 | 94.65 183 | 84.23 197 | 96.49 197 | 79.51 186 | 88.40 193 | 82.58 188 | 95.31 175 | 95.21 183 | 95.03 176 | 99.66 101 | 99.68 100 |
|
PEN-MVS | | | 92.72 174 | 93.20 200 | 92.15 148 | 91.29 202 | 97.31 194 | 94.67 181 | 89.81 137 | 96.19 200 | 81.83 159 | 88.58 190 | 79.06 215 | 95.61 151 | 95.21 183 | 96.27 140 | 99.72 60 | 99.82 25 |
|
v52 | | | 91.94 198 | 93.10 201 | 90.57 190 | 90.62 209 | 97.50 185 | 93.98 194 | 87.02 167 | 95.86 209 | 77.67 198 | 86.93 207 | 82.16 196 | 94.53 186 | 94.71 203 | 94.70 196 | 99.61 133 | 99.85 18 |
|
V4 | | | 91.92 199 | 93.10 201 | 90.55 191 | 90.64 208 | 97.51 184 | 93.93 195 | 87.02 167 | 95.81 211 | 77.61 199 | 86.93 207 | 82.19 195 | 94.50 187 | 94.72 202 | 94.68 197 | 99.62 130 | 99.85 18 |
|
v7n | | | 91.61 202 | 92.95 203 | 90.04 197 | 90.56 211 | 97.69 170 | 93.74 196 | 85.59 179 | 95.89 208 | 76.95 201 | 86.60 210 | 78.60 217 | 93.76 200 | 97.01 145 | 94.99 184 | 99.65 107 | 99.87 13 |
|
v148 | | | 92.36 188 | 92.88 204 | 91.75 163 | 91.63 193 | 97.66 172 | 92.64 202 | 90.55 122 | 96.09 202 | 83.34 148 | 88.19 194 | 80.00 209 | 92.74 205 | 93.98 207 | 94.58 198 | 99.58 150 | 99.69 93 |
|
test2356 | | | 88.81 212 | 92.86 205 | 84.09 219 | 87.85 217 | 93.46 223 | 87.07 221 | 83.60 201 | 96.50 196 | 62.08 230 | 97.06 88 | 75.04 221 | 85.17 219 | 95.08 199 | 95.42 161 | 98.75 196 | 97.46 204 |
|
DTE-MVSNet | | | 92.42 185 | 92.85 206 | 91.91 158 | 90.87 207 | 96.97 198 | 94.53 188 | 89.81 137 | 95.86 209 | 81.59 160 | 88.83 183 | 77.88 218 | 95.01 182 | 94.34 206 | 96.35 138 | 99.64 118 | 99.73 69 |
|
new_pmnet | | | 90.45 208 | 92.84 207 | 87.66 208 | 88.96 215 | 96.16 204 | 88.71 217 | 84.66 189 | 97.56 150 | 71.91 218 | 85.60 214 | 86.58 147 | 93.28 202 | 96.07 170 | 93.54 206 | 98.46 199 | 94.39 222 |
|
gm-plane-assit | | | 89.44 211 | 92.82 208 | 85.49 214 | 91.37 201 | 95.34 217 | 79.55 228 | 82.12 202 | 91.68 224 | 64.79 227 | 87.98 199 | 80.26 208 | 95.66 147 | 98.51 72 | 97.56 106 | 99.45 171 | 98.41 191 |
|
testus | | | 88.77 213 | 92.77 209 | 84.10 218 | 88.24 216 | 93.95 221 | 87.16 220 | 84.24 195 | 97.37 153 | 61.54 231 | 95.70 127 | 73.10 223 | 84.90 220 | 95.56 176 | 95.82 153 | 98.51 197 | 97.88 201 |
|
pmmvs6 | | | 91.90 200 | 92.53 210 | 91.17 176 | 91.81 178 | 97.63 174 | 93.23 197 | 88.37 155 | 93.43 221 | 80.61 166 | 77.32 225 | 87.47 133 | 94.12 193 | 96.58 152 | 95.72 156 | 98.88 195 | 99.53 139 |
|
v748 | | | 91.12 203 | 91.95 211 | 90.16 196 | 90.60 210 | 97.35 193 | 91.11 206 | 87.92 161 | 94.75 215 | 80.54 168 | 86.26 212 | 75.97 220 | 91.13 211 | 94.63 204 | 94.81 193 | 99.65 107 | 99.90 3 |
|
pmmvs3 | | | 88.19 215 | 91.27 212 | 84.60 216 | 85.60 221 | 93.66 222 | 85.68 223 | 81.13 203 | 92.36 223 | 63.66 229 | 89.51 176 | 77.10 219 | 93.22 203 | 96.37 159 | 92.40 216 | 98.30 203 | 97.46 204 |
|
CMPMVS | | 70.31 18 | 90.74 205 | 91.06 213 | 90.36 195 | 97.32 69 | 97.43 189 | 92.97 200 | 87.82 163 | 93.50 220 | 75.34 209 | 83.27 218 | 84.90 163 | 92.19 208 | 92.64 215 | 91.21 224 | 96.50 226 | 94.46 221 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MIMVSNet1 | | | 88.61 214 | 90.68 214 | 86.19 213 | 81.56 230 | 95.30 218 | 87.78 218 | 85.98 178 | 94.19 219 | 72.30 217 | 78.84 224 | 78.90 216 | 90.06 212 | 96.59 151 | 95.47 159 | 99.46 170 | 95.49 220 |
|
PM-MVS | | | 89.55 210 | 90.30 215 | 88.67 206 | 87.06 218 | 95.60 209 | 90.88 209 | 84.51 191 | 96.14 201 | 75.75 205 | 86.89 209 | 63.47 230 | 94.64 184 | 96.85 148 | 93.89 203 | 99.17 190 | 99.29 160 |
|
pmmvs-eth3d | | | 89.81 209 | 89.65 216 | 90.00 198 | 86.94 219 | 95.38 216 | 91.08 207 | 86.39 175 | 94.57 217 | 82.27 157 | 83.03 219 | 64.94 227 | 93.96 196 | 96.57 153 | 93.82 204 | 99.35 181 | 99.24 164 |
|
MDA-MVSNet-bldmvs | | | 87.84 216 | 89.22 217 | 86.23 212 | 81.74 229 | 96.77 201 | 83.74 224 | 89.57 141 | 94.50 218 | 72.83 216 | 96.64 100 | 64.47 229 | 92.71 206 | 81.43 230 | 92.28 218 | 96.81 225 | 98.47 190 |
|
new-patchmatchnet | | | 86.12 217 | 87.30 218 | 84.74 215 | 86.92 220 | 95.19 220 | 83.57 225 | 84.42 193 | 92.67 222 | 65.66 224 | 80.32 222 | 64.72 228 | 89.41 213 | 92.33 219 | 89.21 225 | 98.43 200 | 96.69 216 |
|
testmv | | | 81.83 221 | 86.26 219 | 76.66 224 | 84.10 223 | 89.42 229 | 74.29 232 | 79.65 210 | 90.61 225 | 51.85 236 | 82.11 220 | 63.06 232 | 72.61 228 | 91.94 221 | 92.75 210 | 97.49 216 | 93.94 224 |
|
test1235678 | | | 81.83 221 | 86.26 219 | 76.66 224 | 84.10 223 | 89.41 230 | 74.29 232 | 79.64 211 | 90.60 226 | 51.84 237 | 82.11 220 | 63.07 231 | 72.61 228 | 91.94 221 | 92.75 210 | 97.49 216 | 93.94 224 |
|
1111 | | | 82.87 220 | 85.67 221 | 79.62 223 | 81.86 227 | 89.62 227 | 74.44 230 | 68.81 233 | 87.44 229 | 66.59 222 | 76.83 226 | 70.33 225 | 87.71 215 | 92.65 213 | 93.37 207 | 98.28 204 | 89.42 228 |
|
test12356 | | | 80.53 224 | 84.80 222 | 75.54 226 | 82.31 226 | 88.05 233 | 75.99 229 | 79.31 215 | 88.53 227 | 53.24 235 | 83.30 217 | 56.38 234 | 65.16 234 | 90.87 225 | 93.10 209 | 97.25 222 | 93.34 227 |
|
FPMVS | | | 83.82 219 | 84.61 223 | 82.90 220 | 90.39 213 | 90.71 226 | 90.85 210 | 84.10 198 | 95.47 214 | 65.15 225 | 83.44 216 | 74.46 222 | 75.48 225 | 81.63 229 | 79.42 231 | 91.42 232 | 87.14 230 |
|
Anonymous20231211 | | | 83.86 218 | 83.39 224 | 84.40 217 | 85.29 222 | 93.44 224 | 86.29 222 | 84.24 195 | 85.55 231 | 68.63 220 | 61.25 231 | 59.57 233 | 84.33 222 | 92.50 216 | 92.52 214 | 97.65 213 | 98.89 182 |
|
Gipuma | | | 81.40 223 | 81.78 225 | 80.96 222 | 83.21 225 | 85.61 234 | 79.73 227 | 76.25 228 | 97.33 160 | 64.21 228 | 55.32 232 | 55.55 235 | 86.04 217 | 92.43 218 | 92.20 219 | 96.32 227 | 93.99 223 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
ambc | | | | 80.99 226 | | 80.04 232 | 90.84 225 | 90.91 208 | | 96.09 202 | 74.18 211 | 62.81 230 | 30.59 241 | 82.44 224 | 96.25 167 | 91.77 221 | 95.91 229 | 98.56 187 |
|
PMMVS2 | | | 77.26 225 | 79.47 227 | 74.70 228 | 76.00 233 | 88.37 232 | 74.22 234 | 76.34 226 | 78.31 233 | 54.13 233 | 69.96 229 | 52.50 236 | 70.14 231 | 84.83 228 | 88.71 226 | 97.35 218 | 93.58 226 |
|
PMVS | | 72.60 17 | 76.39 226 | 77.66 228 | 74.92 227 | 81.04 231 | 69.37 239 | 68.47 235 | 80.54 207 | 85.39 232 | 65.07 226 | 73.52 228 | 72.91 224 | 65.67 233 | 80.35 231 | 76.81 232 | 88.71 234 | 85.25 234 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
.test1245 | | | 69.67 227 | 72.22 229 | 66.70 231 | 81.86 227 | 89.62 227 | 74.44 230 | 68.81 233 | 87.44 229 | 66.59 222 | 76.83 226 | 70.33 225 | 87.71 215 | 92.65 213 | 37.65 234 | 20.79 238 | 51.04 235 |
|
E-PMN | | | 68.30 229 | 68.43 230 | 68.15 229 | 74.70 235 | 71.56 238 | 55.64 237 | 77.24 224 | 77.48 235 | 39.46 239 | 51.95 235 | 41.68 239 | 73.28 227 | 70.65 233 | 79.51 230 | 88.61 235 | 86.20 233 |
|
EMVS | | | 68.12 230 | 68.11 231 | 68.14 230 | 75.51 234 | 71.76 237 | 55.38 238 | 77.20 225 | 77.78 234 | 37.79 240 | 53.59 233 | 43.61 237 | 74.72 226 | 67.05 235 | 76.70 233 | 88.27 236 | 86.24 232 |
|
no-one | | | 66.79 231 | 67.62 232 | 65.81 232 | 73.06 236 | 81.79 235 | 51.90 240 | 76.20 229 | 61.07 237 | 54.05 234 | 51.62 236 | 41.72 238 | 49.18 235 | 67.26 234 | 82.83 229 | 90.47 233 | 87.07 231 |
|
MVE | | 67.97 19 | 65.53 232 | 67.43 233 | 63.31 233 | 59.33 237 | 74.20 236 | 53.09 239 | 70.43 232 | 66.27 236 | 43.13 238 | 45.98 237 | 30.62 240 | 70.65 230 | 79.34 232 | 86.30 227 | 83.25 237 | 89.33 229 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
testmvs | | | 31.24 233 | 40.15 234 | 20.86 235 | 12.61 238 | 17.99 240 | 25.16 241 | 13.30 236 | 48.42 238 | 24.82 241 | 53.07 234 | 30.13 242 | 28.47 236 | 42.73 236 | 37.65 234 | 20.79 238 | 51.04 235 |
|
test123 | | | 26.75 234 | 34.25 235 | 18.01 236 | 7.93 239 | 17.18 241 | 24.85 242 | 12.36 237 | 44.83 239 | 16.52 242 | 41.80 238 | 18.10 243 | 28.29 237 | 33.08 237 | 34.79 236 | 18.10 240 | 49.95 237 |
|
sosnet-low-res | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
sosnet | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
MTAPA | | | | | | | | | | | 98.09 10 | | 99.97 4 | | | | | |
|
MTMP | | | | | | | | | | | 98.46 7 | | 99.96 10 | | | | | |
|
Patchmatch-RL test | | | | | | | | 66.86 236 | | | | | | | | | | |
|
tmp_tt | | | | | 82.25 221 | 97.73 63 | 88.71 231 | 80.18 226 | 68.65 235 | 99.15 51 | 86.98 129 | 99.47 7 | 85.31 159 | 68.35 232 | 87.51 227 | 83.81 228 | 91.64 231 | |
|
XVS | | | | | | 97.42 67 | 99.62 34 | 98.59 58 | | | 93.81 80 | | 99.95 15 | | | | 99.69 80 | |
|
X-MVStestdata | | | | | | 97.42 67 | 99.62 34 | 98.59 58 | | | 93.81 80 | | 99.95 15 | | | | 99.69 80 | |
|
abl_6 | | | | | 98.09 36 | 99.33 37 | 99.22 91 | 98.79 53 | 94.96 48 | 98.52 111 | 97.00 27 | 97.30 81 | 99.86 33 | 98.76 63 | | | 99.69 80 | 99.41 155 |
|
mPP-MVS | | | | | | 99.53 25 | | | | | | | 99.89 30 | | | | | |
|
NP-MVS | | | | | | | | | | 98.57 106 | | | | | | | | |
|
Patchmtry | | | | | | | 98.59 127 | 97.15 115 | 79.14 216 | | 80.42 170 | | | | | | | |
|
DeepMVS_CX | | | | | | | 96.85 199 | 87.43 219 | 89.27 143 | 98.30 117 | 75.55 207 | 95.05 133 | 79.47 212 | 92.62 207 | 89.48 226 | | 95.18 230 | 95.96 219 |
|