Anonymous20231211 | | | 99.90 1 | 99.87 1 | 99.92 4 | 99.98 1 | 99.91 1 | 99.92 2 | 99.97 1 | 99.86 2 | 99.98 2 | 99.82 64 | 100.00 1 | 99.70 39 | 99.86 17 | 99.79 12 | 99.96 3 | 99.87 12 |
|
LTVRE_ROB | | 99.39 1 | 99.90 1 | 99.87 1 | 99.93 1 | 99.97 2 | 99.82 7 | 99.91 6 | 99.92 43 | 99.75 8 | 99.93 5 | 99.89 42 | 100.00 1 | 99.87 2 | 99.93 4 | 99.82 6 | 99.96 3 | 99.90 2 |
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 |
v748 | | | 99.89 3 | 99.87 1 | 99.92 4 | 99.96 9 | 99.80 12 | 99.91 6 | 99.95 29 | 99.77 6 | 99.92 9 | 99.96 5 | 99.93 38 | 99.81 9 | 99.92 7 | 99.82 6 | 99.96 3 | 99.90 2 |
|
v7n | | | 99.89 3 | 99.86 5 | 99.93 1 | 99.97 2 | 99.83 3 | 99.93 1 | 99.96 15 | 99.77 6 | 99.89 19 | 99.99 1 | 99.86 69 | 99.84 5 | 99.89 9 | 99.81 10 | 99.97 1 | 99.88 9 |
|
v52 | | | 99.89 3 | 99.85 7 | 99.92 4 | 99.97 2 | 99.80 12 | 99.92 2 | 99.97 1 | 99.78 4 | 99.90 15 | 99.96 5 | 99.85 75 | 99.82 7 | 99.88 12 | 99.82 6 | 99.96 3 | 99.89 5 |
|
V4 | | | 99.89 3 | 99.85 7 | 99.92 4 | 99.97 2 | 99.80 12 | 99.92 2 | 99.97 1 | 99.78 4 | 99.90 15 | 99.96 5 | 99.84 77 | 99.82 7 | 99.88 12 | 99.82 6 | 99.96 3 | 99.89 5 |
|
SixPastTwentyTwo | | | 99.89 3 | 99.85 7 | 99.93 1 | 99.97 2 | 99.88 2 | 99.92 2 | 99.97 1 | 99.66 15 | 99.94 4 | 99.94 15 | 99.74 94 | 99.81 9 | 99.97 2 | 99.89 1 | 99.96 3 | 99.89 5 |
|
pmmvs6 | | | 99.88 8 | 99.87 1 | 99.89 13 | 99.97 2 | 99.76 19 | 99.89 9 | 99.96 15 | 99.82 3 | 99.90 15 | 99.92 26 | 99.95 22 | 99.68 40 | 99.93 4 | 99.88 2 | 99.95 10 | 99.86 13 |
|
anonymousdsp | | | 99.87 9 | 99.86 5 | 99.88 16 | 99.95 12 | 99.75 23 | 99.90 8 | 99.96 15 | 99.69 11 | 99.83 54 | 99.96 5 | 99.99 4 | 99.74 26 | 99.95 3 | 99.83 3 | 99.91 25 | 99.88 9 |
|
FC-MVSNet-test | | | 99.84 10 | 99.80 10 | 99.89 13 | 99.96 9 | 99.83 3 | 99.84 18 | 99.95 29 | 99.37 58 | 99.77 76 | 99.95 10 | 99.96 14 | 99.85 3 | 99.93 4 | 99.83 3 | 99.95 10 | 99.72 46 |
|
TDRefinement | | | 99.81 11 | 99.76 12 | 99.86 19 | 99.83 98 | 99.53 66 | 99.89 9 | 99.91 48 | 99.73 9 | 99.88 24 | 99.83 62 | 99.96 14 | 99.76 19 | 99.91 8 | 99.81 10 | 99.86 53 | 99.59 70 |
|
WR-MVS | | | 99.79 12 | 99.68 17 | 99.91 9 | 99.95 12 | 99.83 3 | 99.87 13 | 99.96 15 | 99.39 57 | 99.93 5 | 99.87 50 | 99.29 152 | 99.77 17 | 99.83 21 | 99.72 21 | 99.97 1 | 99.82 17 |
|
MIMVSNet1 | | | 99.79 12 | 99.75 13 | 99.84 25 | 99.89 39 | 99.83 3 | 99.84 18 | 99.89 56 | 99.31 64 | 99.93 5 | 99.92 26 | 99.97 10 | 99.68 40 | 99.89 9 | 99.64 28 | 99.82 74 | 99.66 57 |
|
pm-mvs1 | | | 99.77 14 | 99.69 16 | 99.86 19 | 99.94 22 | 99.68 36 | 99.84 18 | 99.93 36 | 99.59 29 | 99.87 29 | 99.92 26 | 99.21 155 | 99.65 55 | 99.88 12 | 99.77 14 | 99.93 19 | 99.78 27 |
|
PEN-MVS | | | 99.77 14 | 99.65 19 | 99.91 9 | 99.95 12 | 99.80 12 | 99.86 14 | 99.97 1 | 99.08 94 | 99.89 19 | 99.69 81 | 99.68 102 | 99.84 5 | 99.81 25 | 99.64 28 | 99.95 10 | 99.81 20 |
|
EU-MVSNet | | | 99.76 16 | 99.74 14 | 99.78 49 | 99.82 103 | 99.81 10 | 99.88 11 | 99.87 62 | 99.31 64 | 99.75 86 | 99.91 35 | 99.76 93 | 99.78 15 | 99.84 20 | 99.74 18 | 99.56 149 | 99.81 20 |
|
Vis-MVSNet | | | 99.76 16 | 99.78 11 | 99.75 63 | 99.92 27 | 99.77 18 | 99.83 21 | 99.85 78 | 99.43 50 | 99.85 41 | 99.84 60 | 100.00 1 | 99.13 136 | 99.83 21 | 99.66 26 | 99.90 28 | 99.90 2 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
DTE-MVSNet | | | 99.75 18 | 99.61 27 | 99.92 4 | 99.95 12 | 99.81 10 | 99.86 14 | 99.96 15 | 99.18 81 | 99.92 9 | 99.66 83 | 99.45 136 | 99.85 3 | 99.80 26 | 99.56 34 | 99.96 3 | 99.79 24 |
|
tfpnnormal | | | 99.74 19 | 99.63 22 | 99.86 19 | 99.93 25 | 99.75 23 | 99.80 30 | 99.89 56 | 99.31 64 | 99.88 24 | 99.43 115 | 99.66 105 | 99.77 17 | 99.80 26 | 99.71 22 | 99.92 23 | 99.76 33 |
|
DeepC-MVS | | 99.05 5 | 99.74 19 | 99.64 20 | 99.84 25 | 99.90 34 | 99.39 99 | 99.79 32 | 99.81 118 | 99.69 11 | 99.90 15 | 99.87 50 | 99.98 5 | 99.81 9 | 99.62 49 | 99.32 62 | 99.83 70 | 99.65 61 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
v13 | | | 99.73 21 | 99.63 22 | 99.85 22 | 99.87 50 | 99.71 28 | 99.80 30 | 99.96 15 | 99.62 23 | 99.83 54 | 99.93 19 | 99.66 105 | 99.75 21 | 99.41 70 | 99.26 67 | 99.89 33 | 99.80 23 |
|
PS-CasMVS | | | 99.73 21 | 99.59 33 | 99.90 12 | 99.95 12 | 99.80 12 | 99.85 17 | 99.97 1 | 98.95 109 | 99.86 34 | 99.73 73 | 99.36 145 | 99.81 9 | 99.83 21 | 99.67 25 | 99.95 10 | 99.83 16 |
|
WR-MVS_H | | | 99.73 21 | 99.61 27 | 99.88 16 | 99.95 12 | 99.82 7 | 99.83 21 | 99.96 15 | 99.01 102 | 99.84 45 | 99.71 79 | 99.41 142 | 99.74 26 | 99.77 31 | 99.70 23 | 99.95 10 | 99.82 17 |
|
no-one | | | 99.73 21 | 99.70 15 | 99.76 57 | 99.77 126 | 99.58 53 | 99.76 40 | 99.90 55 | 99.08 94 | 99.86 34 | 99.90 39 | 99.98 5 | 99.66 52 | 99.98 1 | 99.73 19 | 99.59 143 | 99.67 55 |
|
v12 | | | 99.72 25 | 99.61 27 | 99.85 22 | 99.86 66 | 99.70 33 | 99.79 32 | 99.96 15 | 99.61 24 | 99.83 54 | 99.93 19 | 99.61 109 | 99.74 26 | 99.38 72 | 99.22 69 | 99.89 33 | 99.79 24 |
|
v11 | | | 99.72 25 | 99.62 25 | 99.85 22 | 99.87 50 | 99.71 28 | 99.81 27 | 99.96 15 | 99.63 20 | 99.83 54 | 99.97 4 | 99.58 116 | 99.75 21 | 99.33 83 | 99.33 60 | 99.87 47 | 99.79 24 |
|
TransMVSNet (Re) | | | 99.72 25 | 99.59 33 | 99.88 16 | 99.95 12 | 99.76 19 | 99.88 11 | 99.94 32 | 99.58 31 | 99.92 9 | 99.90 39 | 98.55 170 | 99.65 55 | 99.89 9 | 99.76 15 | 99.95 10 | 99.70 50 |
|
ACMH | | 99.11 4 | 99.72 25 | 99.63 22 | 99.84 25 | 99.87 50 | 99.59 51 | 99.83 21 | 99.88 60 | 99.46 47 | 99.87 29 | 99.66 83 | 99.95 22 | 99.76 19 | 99.73 36 | 99.47 49 | 99.84 60 | 99.52 100 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
V9 | | | 99.71 29 | 99.59 33 | 99.84 25 | 99.86 66 | 99.69 35 | 99.78 35 | 99.96 15 | 99.61 24 | 99.84 45 | 99.93 19 | 99.61 109 | 99.73 30 | 99.34 82 | 99.17 75 | 99.88 37 | 99.78 27 |
|
FC-MVSNet-train | | | 99.70 30 | 99.67 18 | 99.74 69 | 99.94 22 | 99.71 28 | 99.82 25 | 99.91 48 | 99.14 90 | 99.53 141 | 99.70 80 | 99.88 63 | 99.33 107 | 99.88 12 | 99.61 33 | 99.94 17 | 99.77 29 |
|
COLMAP_ROB | | 99.18 2 | 99.70 30 | 99.60 31 | 99.81 35 | 99.84 87 | 99.37 110 | 99.76 40 | 99.84 90 | 99.54 40 | 99.82 61 | 99.64 87 | 99.95 22 | 99.75 21 | 99.79 28 | 99.56 34 | 99.83 70 | 99.37 139 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
V14 | | | 99.69 32 | 99.56 38 | 99.84 25 | 99.86 66 | 99.68 36 | 99.78 35 | 99.96 15 | 99.60 28 | 99.83 54 | 99.93 19 | 99.58 116 | 99.72 34 | 99.28 96 | 99.11 86 | 99.88 37 | 99.77 29 |
|
ACMH+ | | 98.94 6 | 99.69 32 | 99.59 33 | 99.81 35 | 99.88 43 | 99.41 96 | 99.75 46 | 99.86 66 | 99.43 50 | 99.80 66 | 99.54 100 | 99.97 10 | 99.73 30 | 99.82 24 | 99.52 44 | 99.85 57 | 99.43 121 |
|
test20.03 | | | 99.68 34 | 99.60 31 | 99.76 57 | 99.91 31 | 99.70 33 | 99.68 71 | 99.87 62 | 99.05 99 | 99.88 24 | 99.92 26 | 99.88 63 | 99.50 85 | 99.77 31 | 99.42 55 | 99.75 92 | 99.49 106 |
|
CP-MVSNet | | | 99.68 34 | 99.51 45 | 99.89 13 | 99.95 12 | 99.76 19 | 99.83 21 | 99.96 15 | 98.83 125 | 99.84 45 | 99.65 86 | 99.09 158 | 99.80 13 | 99.78 29 | 99.62 32 | 99.95 10 | 99.82 17 |
|
v15 | | | 99.67 36 | 99.54 41 | 99.83 30 | 99.86 66 | 99.67 39 | 99.76 40 | 99.95 29 | 99.59 29 | 99.83 54 | 99.93 19 | 99.55 120 | 99.71 38 | 99.23 105 | 99.05 94 | 99.87 47 | 99.75 36 |
|
PVSNet_Blended_VisFu | | | 99.66 37 | 99.64 20 | 99.67 83 | 99.91 31 | 99.71 28 | 99.61 84 | 99.79 127 | 99.41 53 | 99.91 13 | 99.85 57 | 99.61 109 | 99.00 146 | 99.67 42 | 99.42 55 | 99.81 78 | 99.81 20 |
|
v10 | | | 99.65 38 | 99.51 45 | 99.81 35 | 99.83 98 | 99.61 46 | 99.75 46 | 99.94 32 | 99.56 36 | 99.76 79 | 99.94 15 | 99.60 113 | 99.73 30 | 99.11 133 | 99.01 101 | 99.85 57 | 99.74 39 |
|
CHOSEN 1792x2688 | | | 99.65 38 | 99.55 39 | 99.77 53 | 99.93 25 | 99.60 48 | 99.79 32 | 99.92 43 | 99.73 9 | 99.74 92 | 99.93 19 | 99.98 5 | 99.80 13 | 98.83 181 | 99.01 101 | 99.45 162 | 99.76 33 |
|
UA-Net | | | 99.64 40 | 99.62 25 | 99.66 84 | 99.97 2 | 99.82 7 | 99.14 174 | 99.96 15 | 98.95 109 | 99.52 147 | 99.38 123 | 99.86 69 | 99.55 72 | 99.72 37 | 99.66 26 | 99.80 81 | 99.94 1 |
|
v17 | | | 99.62 41 | 99.48 48 | 99.79 46 | 99.80 107 | 99.60 48 | 99.73 57 | 99.94 32 | 99.46 47 | 99.73 98 | 99.88 48 | 99.52 125 | 99.67 44 | 99.16 126 | 98.96 111 | 99.84 60 | 99.75 36 |
|
Baseline_NR-MVSNet | | | 99.62 41 | 99.48 48 | 99.78 49 | 99.85 79 | 99.76 19 | 99.59 89 | 99.82 106 | 98.84 123 | 99.88 24 | 99.91 35 | 99.04 159 | 99.61 63 | 99.46 61 | 99.78 13 | 99.94 17 | 99.60 69 |
|
pmmvs-eth3d | | | 99.61 43 | 99.48 48 | 99.75 63 | 99.87 50 | 99.30 129 | 99.75 46 | 99.89 56 | 99.23 71 | 99.85 41 | 99.88 48 | 99.97 10 | 99.49 89 | 99.46 61 | 99.01 101 | 99.68 110 | 99.52 100 |
|
v1144 | | | 99.61 43 | 99.43 59 | 99.82 31 | 99.88 43 | 99.41 96 | 99.76 40 | 99.86 66 | 99.64 18 | 99.84 45 | 99.95 10 | 99.49 132 | 99.74 26 | 99.00 149 | 98.93 116 | 99.84 60 | 99.58 79 |
|
v16 | | | 99.61 43 | 99.47 52 | 99.78 49 | 99.79 115 | 99.60 48 | 99.72 62 | 99.94 32 | 99.45 49 | 99.70 109 | 99.85 57 | 99.54 123 | 99.67 44 | 99.15 127 | 98.96 111 | 99.83 70 | 99.76 33 |
|
v8 | | | 99.61 43 | 99.45 56 | 99.79 46 | 99.80 107 | 99.59 51 | 99.73 57 | 99.93 36 | 99.48 45 | 99.77 76 | 99.90 39 | 99.48 134 | 99.67 44 | 99.11 133 | 98.89 120 | 99.84 60 | 99.73 42 |
|
v7 | | | 99.61 43 | 99.46 55 | 99.79 46 | 99.83 98 | 99.37 110 | 99.75 46 | 99.84 90 | 99.56 36 | 99.76 79 | 99.94 15 | 99.60 113 | 99.73 30 | 99.11 133 | 99.01 101 | 99.85 57 | 99.63 65 |
|
CSCG | | | 99.61 43 | 99.52 44 | 99.71 73 | 99.89 39 | 99.62 43 | 99.52 105 | 99.76 147 | 99.61 24 | 99.69 111 | 99.73 73 | 99.96 14 | 99.57 70 | 99.27 99 | 98.62 158 | 99.81 78 | 99.85 15 |
|
v1192 | | | 99.60 49 | 99.41 63 | 99.82 31 | 99.89 39 | 99.43 92 | 99.81 27 | 99.84 90 | 99.63 20 | 99.85 41 | 99.95 10 | 99.35 148 | 99.72 34 | 99.01 147 | 98.90 119 | 99.82 74 | 99.58 79 |
|
APDe-MVS | | | 99.60 49 | 99.48 48 | 99.73 71 | 99.85 79 | 99.51 79 | 99.75 46 | 99.85 78 | 99.17 82 | 99.81 64 | 99.56 98 | 99.94 32 | 99.44 97 | 99.42 69 | 99.22 69 | 99.67 112 | 99.54 90 |
|
v1921920 | | | 99.59 51 | 99.40 65 | 99.82 31 | 99.88 43 | 99.45 86 | 99.81 27 | 99.83 98 | 99.65 16 | 99.86 34 | 99.95 10 | 99.29 152 | 99.75 21 | 98.98 153 | 98.86 128 | 99.78 83 | 99.59 70 |
|
v18 | | | 99.59 51 | 99.44 58 | 99.76 57 | 99.78 120 | 99.57 55 | 99.70 69 | 99.93 36 | 99.43 50 | 99.69 111 | 99.85 57 | 99.51 127 | 99.65 55 | 99.08 144 | 98.87 125 | 99.82 74 | 99.74 39 |
|
TranMVSNet+NR-MVSNet | | | 99.59 51 | 99.42 62 | 99.80 39 | 99.87 50 | 99.55 62 | 99.64 76 | 99.86 66 | 99.05 99 | 99.88 24 | 99.72 76 | 99.33 150 | 99.64 58 | 99.47 59 | 99.14 80 | 99.91 25 | 99.67 55 |
|
EG-PatchMatch MVS | | | 99.59 51 | 99.49 47 | 99.70 76 | 99.82 103 | 99.26 137 | 99.39 132 | 99.83 98 | 98.99 104 | 99.93 5 | 99.54 100 | 99.92 47 | 99.51 81 | 99.78 29 | 99.50 45 | 99.73 100 | 99.41 126 |
|
pmmvs5 | | | 99.58 55 | 99.47 52 | 99.70 76 | 99.84 87 | 99.50 80 | 99.58 93 | 99.80 124 | 98.98 107 | 99.73 98 | 99.92 26 | 99.81 83 | 99.49 89 | 99.28 96 | 99.05 94 | 99.77 87 | 99.73 42 |
|
v144192 | | | 99.58 55 | 99.39 69 | 99.80 39 | 99.87 50 | 99.44 88 | 99.77 37 | 99.84 90 | 99.64 18 | 99.86 34 | 99.93 19 | 99.35 148 | 99.72 34 | 98.92 161 | 98.82 134 | 99.74 96 | 99.66 57 |
|
v148 | | | 99.58 55 | 99.43 59 | 99.76 57 | 99.87 50 | 99.40 98 | 99.76 40 | 99.85 78 | 99.48 45 | 99.83 54 | 99.82 64 | 99.83 80 | 99.51 81 | 99.20 114 | 98.82 134 | 99.75 92 | 99.45 114 |
|
v1141 | | | 99.58 55 | 99.39 69 | 99.80 39 | 99.87 50 | 99.39 99 | 99.74 54 | 99.85 78 | 99.58 31 | 99.84 45 | 99.92 26 | 99.49 132 | 99.68 40 | 98.98 153 | 98.83 131 | 99.84 60 | 99.52 100 |
|
v1240 | | | 99.58 55 | 99.38 74 | 99.82 31 | 99.89 39 | 99.49 82 | 99.82 25 | 99.83 98 | 99.63 20 | 99.86 34 | 99.96 5 | 98.92 164 | 99.75 21 | 99.15 127 | 98.96 111 | 99.76 89 | 99.56 84 |
|
divwei89l23v2f112 | | | 99.58 55 | 99.39 69 | 99.80 39 | 99.87 50 | 99.39 99 | 99.74 54 | 99.85 78 | 99.57 34 | 99.84 45 | 99.92 26 | 99.48 134 | 99.67 44 | 98.98 153 | 98.83 131 | 99.84 60 | 99.52 100 |
|
v1 | | | 99.58 55 | 99.39 69 | 99.80 39 | 99.87 50 | 99.39 99 | 99.74 54 | 99.85 78 | 99.58 31 | 99.84 45 | 99.92 26 | 99.51 127 | 99.67 44 | 98.98 153 | 98.82 134 | 99.84 60 | 99.52 100 |
|
v1neww | | | 99.57 62 | 99.40 65 | 99.77 53 | 99.80 107 | 99.34 119 | 99.72 62 | 99.82 106 | 99.49 42 | 99.76 79 | 99.89 42 | 99.50 129 | 99.67 44 | 99.10 141 | 98.89 120 | 99.84 60 | 99.59 70 |
|
v7new | | | 99.57 62 | 99.40 65 | 99.77 53 | 99.80 107 | 99.34 119 | 99.72 62 | 99.82 106 | 99.49 42 | 99.76 79 | 99.89 42 | 99.50 129 | 99.67 44 | 99.10 141 | 98.89 120 | 99.84 60 | 99.59 70 |
|
v6 | | | 99.57 62 | 99.40 65 | 99.77 53 | 99.80 107 | 99.34 119 | 99.72 62 | 99.82 106 | 99.49 42 | 99.76 79 | 99.89 42 | 99.52 125 | 99.67 44 | 99.10 141 | 98.89 120 | 99.84 60 | 99.59 70 |
|
V42 | | | 99.57 62 | 99.41 63 | 99.75 63 | 99.84 87 | 99.37 110 | 99.73 57 | 99.83 98 | 99.41 53 | 99.75 86 | 99.89 42 | 99.42 140 | 99.60 65 | 99.15 127 | 98.96 111 | 99.76 89 | 99.65 61 |
|
TSAR-MVS + MP. | | | 99.56 66 | 99.54 41 | 99.58 102 | 99.69 156 | 99.14 157 | 99.73 57 | 99.45 201 | 99.50 41 | 99.35 179 | 99.60 94 | 99.93 38 | 99.50 85 | 99.56 51 | 99.37 59 | 99.77 87 | 99.64 64 |
|
v2v482 | | | 99.56 66 | 99.35 76 | 99.81 35 | 99.87 50 | 99.35 117 | 99.75 46 | 99.85 78 | 99.56 36 | 99.87 29 | 99.95 10 | 99.44 138 | 99.66 52 | 98.91 164 | 98.76 143 | 99.86 53 | 99.45 114 |
|
Gipuma | | | 99.55 68 | 99.23 91 | 99.91 9 | 99.87 50 | 99.52 72 | 99.86 14 | 99.93 36 | 99.87 1 | 99.96 3 | 96.72 216 | 99.55 120 | 99.97 1 | 99.77 31 | 99.46 51 | 99.87 47 | 99.74 39 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
NR-MVSNet | | | 99.52 69 | 99.29 82 | 99.80 39 | 99.96 9 | 99.38 104 | 99.55 98 | 99.81 118 | 98.86 119 | 99.87 29 | 99.51 109 | 98.81 166 | 99.72 34 | 99.86 17 | 99.04 97 | 99.89 33 | 99.54 90 |
|
MPTG | | | 99.51 70 | 99.36 75 | 99.68 81 | 99.88 43 | 99.38 104 | 99.53 102 | 99.84 90 | 99.11 93 | 99.59 132 | 98.93 161 | 99.95 22 | 99.58 69 | 99.44 67 | 99.21 71 | 99.65 116 | 99.52 100 |
|
ACMMPR | | | 99.51 70 | 99.32 79 | 99.72 72 | 99.87 50 | 99.33 123 | 99.61 84 | 99.85 78 | 99.19 79 | 99.73 98 | 98.73 170 | 99.95 22 | 99.61 63 | 99.35 79 | 99.14 80 | 99.66 114 | 99.58 79 |
|
UniMVSNet (Re) | | | 99.50 72 | 99.29 82 | 99.75 63 | 99.86 66 | 99.47 84 | 99.51 108 | 99.82 106 | 98.90 115 | 99.89 19 | 99.64 87 | 99.00 160 | 99.55 72 | 99.32 85 | 99.08 89 | 99.90 28 | 99.59 70 |
|
FMVSNet1 | | | 99.50 72 | 99.57 37 | 99.42 131 | 99.67 164 | 99.65 41 | 99.60 88 | 99.91 48 | 99.40 55 | 99.39 170 | 99.83 62 | 99.27 154 | 98.14 178 | 99.68 39 | 99.50 45 | 99.81 78 | 99.68 52 |
|
HyFIR lowres test | | | 99.50 72 | 99.26 86 | 99.80 39 | 99.95 12 | 99.62 43 | 99.76 40 | 99.97 1 | 99.67 13 | 99.56 138 | 99.94 15 | 98.40 173 | 99.78 15 | 98.84 180 | 98.59 161 | 99.76 89 | 99.72 46 |
|
PM-MVS | | | 99.49 75 | 99.43 59 | 99.57 106 | 99.76 131 | 99.34 119 | 99.53 102 | 99.77 139 | 98.93 113 | 99.75 86 | 99.46 113 | 99.83 80 | 99.11 138 | 99.72 37 | 99.29 64 | 99.49 158 | 99.46 113 |
|
Anonymous20231206 | | | 99.48 76 | 99.31 80 | 99.69 80 | 99.79 115 | 99.57 55 | 99.63 78 | 99.79 127 | 98.88 117 | 99.91 13 | 99.72 76 | 99.93 38 | 99.59 66 | 99.24 102 | 98.63 157 | 99.43 167 | 99.18 159 |
|
DU-MVS | | | 99.48 76 | 99.26 86 | 99.75 63 | 99.85 79 | 99.38 104 | 99.50 112 | 99.81 118 | 98.86 119 | 99.89 19 | 99.51 109 | 98.98 161 | 99.59 66 | 99.46 61 | 98.97 109 | 99.87 47 | 99.63 65 |
|
RPSCF | | | 99.48 76 | 99.45 56 | 99.52 116 | 99.73 146 | 99.33 123 | 99.13 175 | 99.77 139 | 99.33 62 | 99.47 159 | 99.39 122 | 99.92 47 | 99.36 101 | 99.63 47 | 99.13 83 | 99.63 127 | 99.41 126 |
|
ACMMP_Plus | | | 99.47 79 | 99.33 78 | 99.63 92 | 99.85 79 | 99.28 134 | 99.56 96 | 99.83 98 | 98.75 131 | 99.48 156 | 99.03 155 | 99.95 22 | 99.47 96 | 99.48 56 | 99.19 72 | 99.57 146 | 99.59 70 |
|
SteuartSystems-ACMMP | | | 99.47 79 | 99.22 93 | 99.76 57 | 99.88 43 | 99.36 113 | 99.65 75 | 99.84 90 | 98.47 159 | 99.80 66 | 98.68 173 | 99.96 14 | 99.68 40 | 99.37 74 | 99.06 91 | 99.72 104 | 99.66 57 |
Skip Steuart: Steuart Systems R&D Blog. |
ACMM | | 98.37 12 | 99.47 79 | 99.23 91 | 99.74 69 | 99.86 66 | 99.19 152 | 99.68 71 | 99.86 66 | 99.16 86 | 99.71 107 | 98.52 180 | 99.95 22 | 99.62 62 | 99.35 79 | 99.02 99 | 99.74 96 | 99.42 125 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
HFP-MVS | | | 99.46 82 | 99.30 81 | 99.65 86 | 99.82 103 | 99.25 140 | 99.50 112 | 99.82 106 | 99.23 71 | 99.58 136 | 98.86 163 | 99.94 32 | 99.56 71 | 99.14 130 | 99.12 85 | 99.63 127 | 99.56 84 |
|
LGP-MVS_train | | | 99.46 82 | 99.18 104 | 99.78 49 | 99.87 50 | 99.25 140 | 99.71 68 | 99.87 62 | 98.02 188 | 99.79 69 | 98.90 162 | 99.96 14 | 99.66 52 | 99.49 55 | 99.17 75 | 99.79 82 | 99.49 106 |
|
ACMP | | 98.32 13 | 99.44 84 | 99.18 104 | 99.75 63 | 99.83 98 | 99.18 153 | 99.64 76 | 99.83 98 | 98.81 127 | 99.79 69 | 98.42 186 | 99.96 14 | 99.64 58 | 99.46 61 | 98.98 108 | 99.74 96 | 99.44 117 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
testgi | | | 99.43 85 | 99.47 52 | 99.38 139 | 99.90 34 | 99.67 39 | 99.30 153 | 99.73 156 | 98.64 145 | 99.53 141 | 99.52 107 | 99.90 55 | 98.08 181 | 99.65 45 | 99.40 58 | 99.75 92 | 99.55 89 |
|
DELS-MVS | | | 99.42 86 | 99.53 43 | 99.29 153 | 99.52 191 | 99.43 92 | 99.42 127 | 99.28 215 | 99.16 86 | 99.72 102 | 99.82 64 | 99.97 10 | 98.17 175 | 99.56 51 | 99.16 77 | 99.65 116 | 99.59 70 |
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 |
3Dnovator | | 99.16 3 | 99.42 86 | 99.22 93 | 99.65 86 | 99.78 120 | 99.13 160 | 99.50 112 | 99.85 78 | 99.40 55 | 99.80 66 | 98.59 176 | 99.79 90 | 99.30 114 | 99.20 114 | 99.06 91 | 99.71 106 | 99.35 142 |
|
UniMVSNet_NR-MVSNet | | | 99.41 88 | 99.12 116 | 99.76 57 | 99.86 66 | 99.48 83 | 99.50 112 | 99.81 118 | 98.84 123 | 99.89 19 | 99.45 114 | 98.32 176 | 99.59 66 | 99.22 108 | 98.89 120 | 99.90 28 | 99.63 65 |
|
CP-MVS | | | 99.41 88 | 99.20 98 | 99.65 86 | 99.80 107 | 99.23 147 | 99.44 125 | 99.75 155 | 98.60 150 | 99.74 92 | 98.66 174 | 99.93 38 | 99.48 93 | 99.33 83 | 99.16 77 | 99.73 100 | 99.48 109 |
|
QAPM | | | 99.41 88 | 99.21 97 | 99.64 91 | 99.78 120 | 99.16 154 | 99.51 108 | 99.85 78 | 99.20 76 | 99.72 102 | 99.43 115 | 99.81 83 | 99.25 119 | 98.87 170 | 98.71 148 | 99.71 106 | 99.30 148 |
|
UGNet | | | 99.40 91 | 99.61 27 | 99.16 175 | 99.88 43 | 99.64 42 | 99.61 84 | 99.77 139 | 99.31 64 | 99.63 124 | 99.33 126 | 99.93 38 | 96.46 214 | 99.63 47 | 99.53 43 | 99.63 127 | 99.89 5 |
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 |
Vis-MVSNet (Re-imp) | | | 99.40 91 | 99.28 84 | 99.55 110 | 99.92 27 | 99.68 36 | 99.31 148 | 99.87 62 | 98.69 137 | 99.16 189 | 99.08 150 | 98.64 169 | 99.20 123 | 99.65 45 | 99.46 51 | 99.83 70 | 99.72 46 |
|
OPM-MVS | | | 99.39 93 | 99.22 93 | 99.59 100 | 99.76 131 | 98.82 183 | 99.51 108 | 99.79 127 | 99.17 82 | 99.53 141 | 99.31 130 | 99.95 22 | 99.35 102 | 99.22 108 | 98.79 142 | 99.60 137 | 99.27 152 |
|
Fast-Effi-MVS+ | | | 99.39 93 | 99.18 104 | 99.63 92 | 99.86 66 | 99.28 134 | 99.45 124 | 99.91 48 | 98.47 159 | 99.61 126 | 99.50 111 | 99.57 118 | 99.17 124 | 99.24 102 | 98.66 154 | 99.78 83 | 99.59 70 |
|
testmv | | | 99.39 93 | 99.19 101 | 99.62 97 | 99.84 87 | 99.38 104 | 99.37 138 | 99.86 66 | 98.47 159 | 99.79 69 | 99.82 64 | 99.39 144 | 99.63 60 | 99.30 88 | 98.70 150 | 99.21 189 | 99.28 150 |
|
test1235678 | | | 99.39 93 | 99.20 98 | 99.62 97 | 99.84 87 | 99.38 104 | 99.38 136 | 99.86 66 | 98.47 159 | 99.79 69 | 99.82 64 | 99.41 142 | 99.63 60 | 99.30 88 | 98.71 148 | 99.21 189 | 99.28 150 |
|
LS3D | | | 99.39 93 | 99.28 84 | 99.52 116 | 99.77 126 | 99.39 99 | 99.55 98 | 99.82 106 | 98.93 113 | 99.64 122 | 98.52 180 | 99.67 104 | 98.58 167 | 99.74 35 | 99.63 30 | 99.75 92 | 99.06 174 |
|
CANet | | | 99.36 98 | 99.39 69 | 99.34 149 | 99.80 107 | 99.35 117 | 99.41 130 | 99.47 199 | 99.20 76 | 99.74 92 | 99.54 100 | 99.68 102 | 98.05 185 | 99.23 105 | 98.97 109 | 99.57 146 | 99.73 42 |
|
MVS_0304 | | | 99.36 98 | 99.35 76 | 99.37 142 | 99.85 79 | 99.36 113 | 99.39 132 | 99.56 185 | 99.36 60 | 99.75 86 | 99.23 136 | 99.90 55 | 97.97 188 | 99.00 149 | 98.83 131 | 99.69 109 | 99.77 29 |
|
ACMMP | | | 99.36 98 | 99.06 124 | 99.71 73 | 99.86 66 | 99.36 113 | 99.63 78 | 99.85 78 | 98.33 172 | 99.72 102 | 97.73 203 | 99.94 32 | 99.53 77 | 99.37 74 | 99.13 83 | 99.65 116 | 99.56 84 |
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 |
SD-MVS | | | 99.35 101 | 99.26 86 | 99.46 125 | 99.66 165 | 99.15 156 | 98.92 196 | 99.67 171 | 99.55 39 | 99.35 179 | 98.83 165 | 99.91 53 | 99.35 102 | 99.19 119 | 98.53 163 | 99.78 83 | 99.68 52 |
|
MP-MVS | | | 99.35 101 | 99.09 121 | 99.65 86 | 99.84 87 | 99.22 148 | 99.59 89 | 99.78 133 | 98.13 181 | 99.67 118 | 98.44 184 | 99.93 38 | 99.43 99 | 99.31 87 | 99.09 88 | 99.60 137 | 99.49 106 |
|
pmmvs4 | | | 99.34 103 | 99.15 111 | 99.57 106 | 99.77 126 | 98.90 177 | 99.51 108 | 99.77 139 | 99.07 97 | 99.73 98 | 99.72 76 | 99.84 77 | 99.07 140 | 98.85 176 | 98.39 172 | 99.55 153 | 99.27 152 |
|
EPP-MVSNet | | | 99.34 103 | 99.10 119 | 99.62 97 | 99.94 22 | 99.74 25 | 99.66 73 | 99.80 124 | 99.07 97 | 98.93 201 | 99.61 91 | 96.13 189 | 99.49 89 | 99.67 42 | 99.63 30 | 99.92 23 | 99.86 13 |
|
TSAR-MVS + GP. | | | 99.33 105 | 99.17 108 | 99.51 118 | 99.71 150 | 99.00 170 | 98.84 204 | 99.71 161 | 98.23 177 | 99.74 92 | 99.53 106 | 99.90 55 | 99.35 102 | 99.38 72 | 98.85 129 | 99.72 104 | 99.31 146 |
|
PHI-MVS | | | 99.33 105 | 99.19 101 | 99.49 123 | 99.69 156 | 99.25 140 | 99.27 157 | 99.59 183 | 98.44 165 | 99.78 75 | 99.15 141 | 99.92 47 | 98.95 155 | 99.39 71 | 99.04 97 | 99.64 125 | 99.18 159 |
|
PGM-MVS | | | 99.32 107 | 98.99 133 | 99.71 73 | 99.86 66 | 99.31 128 | 99.59 89 | 99.86 66 | 97.51 203 | 99.75 86 | 98.23 190 | 99.94 32 | 99.53 77 | 99.29 92 | 99.08 89 | 99.65 116 | 99.54 90 |
|
DeepC-MVS_fast | | 98.69 9 | 99.32 107 | 99.13 114 | 99.53 112 | 99.63 170 | 98.78 186 | 99.53 102 | 99.33 213 | 99.08 94 | 99.77 76 | 99.18 140 | 99.89 58 | 99.29 115 | 99.00 149 | 98.70 150 | 99.65 116 | 99.30 148 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MSDG | | | 99.32 107 | 99.09 121 | 99.58 102 | 99.75 135 | 98.74 190 | 99.36 140 | 99.54 188 | 99.14 90 | 99.72 102 | 99.24 134 | 99.89 58 | 99.51 81 | 99.30 88 | 98.76 143 | 99.62 133 | 98.54 193 |
|
TSAR-MVS + ACMM | | | 99.31 110 | 99.26 86 | 99.37 142 | 99.66 165 | 98.97 174 | 99.20 163 | 99.56 185 | 99.33 62 | 99.19 188 | 99.54 100 | 99.91 53 | 99.32 110 | 99.12 132 | 98.34 175 | 99.29 181 | 99.65 61 |
|
3Dnovator+ | | 98.92 7 | 99.31 110 | 99.03 128 | 99.63 92 | 99.77 126 | 98.90 177 | 99.52 105 | 99.81 118 | 99.37 58 | 99.72 102 | 98.03 198 | 99.73 97 | 99.32 110 | 98.99 152 | 98.81 139 | 99.67 112 | 99.36 140 |
|
X-MVS | | | 99.30 112 | 98.99 133 | 99.66 84 | 99.85 79 | 99.30 129 | 99.49 117 | 99.82 106 | 98.32 173 | 99.69 111 | 97.31 212 | 99.93 38 | 99.50 85 | 99.37 74 | 99.16 77 | 99.60 137 | 99.53 95 |
|
MVS_111021_HR | | | 99.30 112 | 99.14 112 | 99.48 124 | 99.58 187 | 99.25 140 | 99.27 157 | 99.61 177 | 98.74 132 | 99.66 120 | 99.02 156 | 99.84 77 | 99.33 107 | 99.20 114 | 98.76 143 | 99.44 164 | 99.18 159 |
|
TAPA-MVS | | 98.54 10 | 99.30 112 | 99.24 90 | 99.36 148 | 99.44 205 | 98.77 188 | 99.00 188 | 99.41 205 | 99.23 71 | 99.60 130 | 99.50 111 | 99.86 69 | 99.15 132 | 99.29 92 | 98.95 115 | 99.56 149 | 99.08 171 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CLD-MVS | | | 99.30 112 | 99.01 132 | 99.63 92 | 99.75 135 | 98.89 180 | 99.35 143 | 99.60 179 | 98.53 156 | 99.86 34 | 99.57 97 | 99.94 32 | 99.52 80 | 98.96 157 | 98.10 188 | 99.70 108 | 99.08 171 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
USDC | | | 99.29 116 | 98.98 135 | 99.65 86 | 99.72 147 | 98.87 181 | 99.47 121 | 99.66 175 | 99.35 61 | 99.87 29 | 99.58 96 | 99.87 68 | 99.51 81 | 98.85 176 | 97.93 195 | 99.65 116 | 98.38 197 |
|
HSP-MVS | | | 99.27 117 | 99.07 123 | 99.50 120 | 99.76 131 | 99.54 64 | 99.73 57 | 99.72 158 | 98.94 111 | 99.23 185 | 98.96 157 | 99.96 14 | 98.91 156 | 98.72 190 | 97.71 200 | 99.63 127 | 99.66 57 |
|
PMVS | | 94.32 17 | 99.27 117 | 99.55 39 | 98.94 192 | 99.60 180 | 99.43 92 | 99.39 132 | 99.54 188 | 98.99 104 | 99.69 111 | 99.60 94 | 99.81 83 | 95.68 222 | 99.88 12 | 99.83 3 | 99.73 100 | 99.31 146 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
MVS_111021_LR | | | 99.25 119 | 99.13 114 | 99.39 136 | 99.50 198 | 99.14 157 | 99.23 161 | 99.50 196 | 98.67 139 | 99.61 126 | 99.12 145 | 99.81 83 | 99.16 128 | 99.28 96 | 98.67 153 | 99.35 177 | 99.21 157 |
|
HPM-MVS++ | | | 99.23 120 | 98.98 135 | 99.53 112 | 99.75 135 | 99.02 169 | 99.44 125 | 99.77 139 | 98.65 141 | 99.52 147 | 98.72 171 | 99.92 47 | 99.33 107 | 98.77 188 | 98.40 171 | 99.40 171 | 99.36 140 |
|
PMMVS2 | | | 99.23 120 | 99.22 93 | 99.24 161 | 99.80 107 | 99.14 157 | 99.50 112 | 99.82 106 | 99.12 92 | 98.41 225 | 99.91 35 | 99.98 5 | 98.51 168 | 99.48 56 | 98.76 143 | 99.38 173 | 98.14 205 |
|
ESAPD | | | 99.21 122 | 99.14 112 | 99.29 153 | 99.79 115 | 99.44 88 | 99.02 186 | 99.79 127 | 97.96 191 | 99.12 193 | 99.22 137 | 99.95 22 | 98.50 169 | 99.21 111 | 98.84 130 | 99.56 149 | 99.34 143 |
|
CPTT-MVS | | | 99.21 122 | 98.89 143 | 99.58 102 | 99.72 147 | 99.12 163 | 99.30 153 | 99.76 147 | 98.62 146 | 99.66 120 | 97.51 206 | 99.89 58 | 99.48 93 | 99.01 147 | 98.64 156 | 99.58 145 | 99.40 133 |
|
TinyColmap | | | 99.21 122 | 98.89 143 | 99.59 100 | 99.61 176 | 98.61 199 | 99.47 121 | 99.67 171 | 99.02 101 | 99.82 61 | 99.15 141 | 99.74 94 | 99.35 102 | 99.17 124 | 98.33 176 | 99.63 127 | 98.22 203 |
|
Effi-MVS+ | | | 99.20 125 | 98.93 138 | 99.50 120 | 99.79 115 | 99.26 137 | 98.82 207 | 99.96 15 | 98.37 171 | 99.60 130 | 99.12 145 | 98.36 174 | 99.05 143 | 98.93 159 | 98.82 134 | 99.78 83 | 99.68 52 |
|
PVSNet_BlendedMVS | | | 99.20 125 | 99.17 108 | 99.23 162 | 99.69 156 | 99.33 123 | 99.04 181 | 99.13 218 | 98.41 168 | 99.79 69 | 99.33 126 | 99.36 145 | 98.10 179 | 99.29 92 | 98.87 125 | 99.65 116 | 99.56 84 |
|
PVSNet_Blended | | | 99.20 125 | 99.17 108 | 99.23 162 | 99.69 156 | 99.33 123 | 99.04 181 | 99.13 218 | 98.41 168 | 99.79 69 | 99.33 126 | 99.36 145 | 98.10 179 | 99.29 92 | 98.87 125 | 99.65 116 | 99.56 84 |
|
MCST-MVS | | | 99.17 128 | 98.82 151 | 99.57 106 | 99.75 135 | 98.70 194 | 99.25 160 | 99.69 165 | 98.62 146 | 99.59 132 | 98.54 178 | 99.79 90 | 99.53 77 | 98.48 198 | 98.15 184 | 99.64 125 | 99.43 121 |
|
APD-MVS | | | 99.17 128 | 98.92 139 | 99.46 125 | 99.78 120 | 99.24 145 | 99.34 144 | 99.78 133 | 97.79 196 | 99.48 156 | 98.25 189 | 99.88 63 | 98.77 162 | 99.18 122 | 98.92 117 | 99.63 127 | 99.18 159 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
OpenMVS | | 98.82 8 | 99.17 128 | 98.85 147 | 99.53 112 | 99.75 135 | 99.06 166 | 99.36 140 | 99.82 106 | 98.28 175 | 99.76 79 | 98.47 182 | 99.61 109 | 98.91 156 | 98.80 184 | 98.70 150 | 99.60 137 | 99.04 179 |
|
IterMVS-LS | | | 99.16 131 | 98.82 151 | 99.57 106 | 99.87 50 | 99.71 28 | 99.58 93 | 99.92 43 | 99.24 70 | 99.71 107 | 99.73 73 | 95.79 190 | 98.91 156 | 98.82 182 | 98.66 154 | 99.43 167 | 99.77 29 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DeepPCF-MVS | | 98.38 11 | 99.16 131 | 99.20 98 | 99.12 179 | 99.20 223 | 98.71 193 | 98.85 203 | 99.06 220 | 99.17 82 | 98.96 200 | 99.61 91 | 99.86 69 | 99.29 115 | 99.17 124 | 98.72 147 | 99.36 175 | 99.15 167 |
|
CDS-MVSNet | | | 99.15 133 | 99.10 119 | 99.21 169 | 99.59 184 | 99.22 148 | 99.48 119 | 99.47 199 | 98.89 116 | 99.41 168 | 99.84 60 | 98.11 179 | 97.76 190 | 99.26 101 | 99.01 101 | 99.57 146 | 99.38 135 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
IS_MVSNet | | | 99.15 133 | 99.12 116 | 99.19 172 | 99.92 27 | 99.73 27 | 99.55 98 | 99.86 66 | 98.45 164 | 96.91 236 | 98.74 169 | 98.33 175 | 99.02 145 | 99.54 53 | 99.47 49 | 99.88 37 | 99.61 68 |
|
test12356 | | | 99.12 135 | 99.03 128 | 99.23 162 | 99.78 120 | 98.95 175 | 99.10 178 | 99.72 158 | 98.26 176 | 99.81 64 | 99.87 50 | 99.20 156 | 98.06 183 | 99.47 59 | 98.80 140 | 98.91 201 | 98.67 190 |
|
MDA-MVSNet-bldmvs | | | 99.11 136 | 99.11 118 | 99.12 179 | 99.91 31 | 99.38 104 | 99.77 37 | 98.72 223 | 99.31 64 | 99.85 41 | 99.43 115 | 98.26 177 | 99.48 93 | 99.85 19 | 98.47 166 | 96.99 218 | 99.08 171 |
|
OMC-MVS | | | 99.11 136 | 98.95 137 | 99.29 153 | 99.37 212 | 98.57 201 | 99.19 164 | 99.20 217 | 98.87 118 | 99.58 136 | 99.13 143 | 99.88 63 | 99.00 146 | 99.19 119 | 98.46 167 | 99.43 167 | 98.57 191 |
|
MVS_Test | | | 99.09 138 | 98.92 139 | 99.29 153 | 99.61 176 | 99.07 165 | 99.04 181 | 99.81 118 | 98.58 152 | 99.37 173 | 99.74 71 | 98.87 165 | 98.41 172 | 98.61 193 | 98.01 193 | 99.50 157 | 99.57 83 |
|
tfpn_n400 | | | 99.08 139 | 98.56 162 | 99.70 76 | 99.85 79 | 99.56 60 | 99.63 78 | 99.86 66 | 99.21 74 | 99.37 173 | 98.95 158 | 94.24 196 | 99.55 72 | 99.20 114 | 99.29 64 | 99.93 19 | 99.44 117 |
|
tfpnconf | | | 99.08 139 | 98.56 162 | 99.70 76 | 99.85 79 | 99.56 60 | 99.63 78 | 99.86 66 | 99.21 74 | 99.37 173 | 98.95 158 | 94.24 196 | 99.55 72 | 99.20 114 | 99.29 64 | 99.93 19 | 99.44 117 |
|
CNVR-MVS | | | 99.08 139 | 98.83 148 | 99.37 142 | 99.61 176 | 98.74 190 | 99.15 172 | 99.54 188 | 98.59 151 | 99.37 173 | 98.15 194 | 99.88 63 | 99.08 139 | 98.91 164 | 98.46 167 | 99.48 159 | 99.06 174 |
|
IterMVS | | | 99.08 139 | 98.90 142 | 99.29 153 | 99.87 50 | 99.53 66 | 99.52 105 | 99.77 139 | 98.94 111 | 99.75 86 | 99.91 35 | 97.52 185 | 98.72 164 | 98.86 174 | 98.14 185 | 98.09 211 | 99.43 121 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
FMVSNet2 | | | 99.07 143 | 99.19 101 | 98.93 194 | 99.02 228 | 99.53 66 | 99.31 148 | 99.84 90 | 98.86 119 | 98.88 204 | 99.64 87 | 98.44 172 | 96.92 208 | 99.35 79 | 99.00 106 | 99.61 134 | 99.53 95 |
|
CVMVSNet | | | 99.06 144 | 98.88 146 | 99.28 158 | 99.52 191 | 99.53 66 | 99.42 127 | 99.69 165 | 98.74 132 | 98.27 228 | 99.89 42 | 95.48 194 | 99.44 97 | 99.46 61 | 99.33 60 | 99.32 180 | 99.75 36 |
|
CDPH-MVS | | | 99.05 145 | 98.63 158 | 99.54 111 | 99.75 135 | 98.78 186 | 99.59 89 | 99.68 169 | 97.79 196 | 99.37 173 | 98.20 193 | 99.86 69 | 99.14 134 | 98.58 194 | 98.01 193 | 99.68 110 | 99.16 165 |
|
TAMVS | | | 99.05 145 | 99.02 131 | 99.08 184 | 99.69 156 | 99.22 148 | 99.33 145 | 99.32 214 | 99.16 86 | 98.97 199 | 99.87 50 | 97.36 186 | 97.76 190 | 99.21 111 | 99.00 106 | 99.44 164 | 99.33 144 |
|
tfpnview11 | | | 99.04 147 | 98.49 171 | 99.68 81 | 99.84 87 | 99.58 53 | 99.56 96 | 99.86 66 | 98.86 119 | 99.37 173 | 98.95 158 | 94.24 196 | 99.54 76 | 98.87 170 | 99.54 42 | 99.91 25 | 99.39 134 |
|
CANet_DTU | | | 99.03 148 | 99.18 104 | 98.87 197 | 99.58 187 | 99.03 167 | 99.18 165 | 99.41 205 | 98.65 141 | 99.74 92 | 99.55 99 | 99.71 99 | 96.13 220 | 99.19 119 | 98.92 117 | 99.17 192 | 99.18 159 |
|
Effi-MVS+-dtu | | | 99.01 149 | 99.05 125 | 98.98 188 | 99.60 180 | 99.13 160 | 99.03 185 | 99.61 177 | 98.52 158 | 99.01 196 | 98.53 179 | 99.83 80 | 96.95 207 | 99.48 56 | 98.59 161 | 99.66 114 | 99.25 156 |
|
canonicalmvs | | | 99.00 150 | 98.68 157 | 99.37 142 | 99.68 163 | 99.42 95 | 98.94 195 | 99.89 56 | 99.00 103 | 98.99 197 | 98.43 185 | 95.69 191 | 98.96 154 | 99.18 122 | 99.18 73 | 99.74 96 | 99.88 9 |
|
MIMVSNet | | | 99.00 150 | 99.03 128 | 98.97 190 | 99.32 217 | 99.32 127 | 99.39 132 | 99.91 48 | 98.41 168 | 98.76 208 | 99.24 134 | 99.17 157 | 97.13 201 | 99.30 88 | 98.80 140 | 99.29 181 | 99.01 180 |
|
CHOSEN 280x420 | | | 98.99 152 | 98.91 141 | 99.07 185 | 99.77 126 | 99.26 137 | 99.55 98 | 99.92 43 | 98.62 146 | 98.67 213 | 99.62 90 | 97.20 187 | 98.44 171 | 99.50 54 | 99.18 73 | 98.08 212 | 98.99 183 |
|
GBi-Net | | | 98.96 153 | 99.05 125 | 98.85 198 | 99.02 228 | 99.53 66 | 99.31 148 | 99.78 133 | 98.13 181 | 98.48 221 | 99.43 115 | 97.58 182 | 96.92 208 | 99.68 39 | 99.50 45 | 99.61 134 | 99.53 95 |
|
test1 | | | 98.96 153 | 99.05 125 | 98.85 198 | 99.02 228 | 99.53 66 | 99.31 148 | 99.78 133 | 98.13 181 | 98.48 221 | 99.43 115 | 97.58 182 | 96.92 208 | 99.68 39 | 99.50 45 | 99.61 134 | 99.53 95 |
|
PCF-MVS | | 97.86 15 | 98.95 155 | 98.53 165 | 99.44 129 | 99.70 154 | 98.80 185 | 98.96 191 | 99.69 165 | 98.65 141 | 99.59 132 | 99.33 126 | 99.94 32 | 99.12 137 | 98.01 210 | 97.11 206 | 99.59 143 | 97.83 209 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MS-PatchMatch | | | 98.94 156 | 98.71 156 | 99.21 169 | 99.52 191 | 98.22 219 | 98.97 190 | 99.53 193 | 98.76 129 | 99.50 154 | 98.59 176 | 99.56 119 | 98.68 165 | 98.63 192 | 98.45 169 | 99.05 197 | 98.73 187 |
|
AdaColmap | | | 98.93 157 | 98.53 165 | 99.39 136 | 99.52 191 | 98.65 197 | 99.11 177 | 99.59 183 | 98.08 185 | 99.44 162 | 97.46 209 | 99.45 136 | 99.24 120 | 98.92 161 | 98.44 170 | 99.44 164 | 98.73 187 |
|
MSLP-MVS++ | | | 98.92 158 | 98.73 155 | 99.14 176 | 99.44 205 | 99.00 170 | 98.36 222 | 99.35 210 | 98.82 126 | 99.38 172 | 96.06 218 | 99.79 90 | 99.07 140 | 98.88 169 | 99.05 94 | 99.27 183 | 99.53 95 |
|
new_pmnet | | | 98.91 159 | 98.89 143 | 98.94 192 | 99.51 196 | 98.27 215 | 99.15 172 | 98.66 224 | 99.17 82 | 99.48 156 | 99.79 69 | 99.80 88 | 98.49 170 | 99.23 105 | 98.20 181 | 98.34 209 | 97.74 213 |
|
train_agg | | | 98.89 160 | 98.48 172 | 99.38 139 | 99.69 156 | 98.76 189 | 99.31 148 | 99.60 179 | 97.71 198 | 98.98 198 | 97.89 200 | 99.89 58 | 99.29 115 | 98.32 200 | 97.59 203 | 99.42 170 | 99.16 165 |
|
NCCC | | | 98.88 161 | 98.42 173 | 99.42 131 | 99.62 171 | 98.81 184 | 99.10 178 | 99.54 188 | 98.76 129 | 99.53 141 | 95.97 219 | 99.80 88 | 99.16 128 | 98.49 196 | 98.06 191 | 99.55 153 | 99.05 176 |
|
PLC | | 97.83 16 | 98.88 161 | 98.52 167 | 99.30 152 | 99.45 203 | 98.60 200 | 98.65 214 | 99.49 197 | 98.66 140 | 99.59 132 | 96.33 217 | 99.59 115 | 99.17 124 | 98.87 170 | 98.53 163 | 99.46 160 | 99.05 176 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
pmmvs3 | | | 98.85 163 | 98.60 159 | 99.13 177 | 99.66 165 | 98.72 192 | 99.37 138 | 99.06 220 | 98.44 165 | 99.76 79 | 99.74 71 | 99.55 120 | 99.15 132 | 99.04 145 | 96.00 214 | 97.80 213 | 98.72 189 |
|
diffmvs | | | 98.83 164 | 98.51 170 | 99.19 172 | 99.62 171 | 98.98 173 | 99.18 165 | 99.82 106 | 99.15 89 | 99.51 151 | 99.66 83 | 95.37 195 | 98.07 182 | 98.49 196 | 98.22 180 | 98.96 199 | 99.73 42 |
|
Fast-Effi-MVS+-dtu | | | 98.82 165 | 98.80 153 | 98.84 200 | 99.51 196 | 98.90 177 | 98.96 191 | 99.91 48 | 98.29 174 | 99.11 194 | 98.47 182 | 99.63 108 | 96.03 221 | 99.21 111 | 98.12 186 | 99.52 155 | 99.01 180 |
|
CNLPA | | | 98.82 165 | 98.52 167 | 99.18 174 | 99.21 222 | 98.50 205 | 98.73 212 | 99.34 212 | 98.73 134 | 99.56 138 | 97.55 205 | 99.42 140 | 99.06 142 | 98.93 159 | 98.10 188 | 99.21 189 | 98.38 197 |
|
PatchMatch-RL | | | 98.80 167 | 98.52 167 | 99.12 179 | 99.38 211 | 98.70 194 | 98.56 217 | 99.55 187 | 97.81 195 | 99.34 182 | 97.57 204 | 99.31 151 | 98.67 166 | 99.27 99 | 98.62 158 | 99.22 188 | 98.35 199 |
|
DI_MVS_plusplus_trai | | | 98.74 168 | 98.08 185 | 99.51 118 | 99.79 115 | 99.29 133 | 99.61 84 | 99.60 179 | 99.20 76 | 99.46 160 | 99.09 149 | 92.93 203 | 98.97 153 | 98.27 204 | 98.35 174 | 99.65 116 | 99.45 114 |
|
TSAR-MVS + COLMAP | | | 98.74 168 | 98.58 161 | 98.93 194 | 99.29 219 | 98.23 216 | 99.04 181 | 99.24 216 | 98.79 128 | 98.80 207 | 99.37 124 | 99.71 99 | 98.06 183 | 98.02 209 | 97.46 205 | 99.16 193 | 98.48 195 |
|
testus | | | 98.74 168 | 98.33 175 | 99.23 162 | 99.71 150 | 99.03 167 | 98.17 228 | 99.60 179 | 97.18 210 | 99.52 147 | 98.07 196 | 98.45 171 | 99.21 122 | 98.30 201 | 98.06 191 | 99.14 195 | 99.21 157 |
|
tfpn1000 | | | 98.73 171 | 98.07 186 | 99.50 120 | 99.84 87 | 99.61 46 | 99.48 119 | 99.84 90 | 98.71 136 | 98.74 209 | 98.71 172 | 91.70 210 | 99.17 124 | 98.81 183 | 99.55 40 | 99.90 28 | 99.43 121 |
|
MDTV_nov1_ep13_2view | | | 98.73 171 | 98.31 176 | 99.22 167 | 99.75 135 | 99.24 145 | 99.75 46 | 99.93 36 | 99.31 64 | 99.84 45 | 99.86 56 | 99.81 83 | 99.31 112 | 97.40 217 | 94.77 215 | 96.73 220 | 97.81 210 |
|
PMMVS | | | 98.71 173 | 98.55 164 | 98.90 196 | 99.28 220 | 98.45 207 | 98.53 220 | 99.45 201 | 97.67 200 | 99.15 192 | 98.76 168 | 99.54 123 | 97.79 189 | 98.77 188 | 98.23 178 | 99.16 193 | 98.46 196 |
|
HQP-MVS | | | 98.70 174 | 98.19 181 | 99.28 158 | 99.61 176 | 98.52 203 | 98.71 213 | 99.35 210 | 97.97 190 | 99.53 141 | 97.38 210 | 99.85 75 | 99.14 134 | 97.53 214 | 96.85 211 | 99.36 175 | 99.26 155 |
|
tfpn_ndepth | | | 98.67 175 | 98.03 187 | 99.42 131 | 99.65 168 | 99.50 80 | 99.29 155 | 99.78 133 | 98.17 180 | 99.04 195 | 98.36 187 | 93.29 201 | 98.88 159 | 98.46 199 | 99.26 67 | 99.88 37 | 99.14 168 |
|
N_pmnet | | | 98.64 176 | 98.23 180 | 99.11 182 | 99.78 120 | 99.25 140 | 99.75 46 | 99.39 209 | 99.65 16 | 99.70 109 | 99.78 70 | 99.89 58 | 98.81 161 | 97.60 213 | 94.28 216 | 97.24 217 | 97.15 217 |
|
CMPMVS | | 76.62 19 | 98.64 176 | 98.60 159 | 98.68 205 | 99.33 215 | 97.07 231 | 98.11 232 | 98.50 226 | 97.69 199 | 99.26 184 | 98.35 188 | 99.66 105 | 97.62 193 | 99.43 68 | 99.02 99 | 99.24 186 | 99.01 180 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
FMVSNet3 | | | 98.63 178 | 98.75 154 | 98.49 209 | 98.10 235 | 99.44 88 | 99.02 186 | 99.78 133 | 98.13 181 | 98.48 221 | 99.43 115 | 97.58 182 | 96.16 219 | 98.85 176 | 98.39 172 | 99.40 171 | 99.41 126 |
|
GA-MVS | | | 98.59 179 | 98.15 182 | 99.09 183 | 99.59 184 | 99.13 160 | 98.84 204 | 99.52 194 | 98.61 149 | 99.35 179 | 99.67 82 | 93.03 202 | 97.73 192 | 98.90 168 | 98.26 177 | 99.51 156 | 99.48 109 |
|
MAR-MVS | | | 98.54 180 | 98.15 182 | 98.98 188 | 99.37 212 | 98.09 222 | 98.56 217 | 99.65 176 | 96.11 229 | 99.27 183 | 97.16 215 | 99.50 129 | 98.03 187 | 98.87 170 | 98.23 178 | 99.01 198 | 99.13 169 |
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 |
new-patchmatchnet | | | 98.49 181 | 97.60 189 | 99.53 112 | 99.90 34 | 99.55 62 | 99.77 37 | 99.48 198 | 99.67 13 | 99.86 34 | 99.98 3 | 99.98 5 | 99.50 85 | 96.90 220 | 91.52 221 | 98.67 206 | 95.62 222 |
|
FPMVS | | | 98.48 182 | 98.83 148 | 98.07 221 | 99.09 226 | 97.98 225 | 99.07 180 | 98.04 232 | 98.99 104 | 99.22 187 | 98.85 164 | 99.43 139 | 93.79 229 | 99.66 44 | 99.11 86 | 99.24 186 | 97.76 211 |
|
MVS-HIRNet | | | 98.45 183 | 98.25 178 | 98.69 204 | 99.12 224 | 97.81 229 | 98.55 219 | 99.85 78 | 98.58 152 | 99.67 118 | 99.61 91 | 99.86 69 | 97.46 196 | 97.95 211 | 96.37 213 | 97.49 215 | 97.56 214 |
|
test0.0.03 1 | | | 98.41 184 | 98.41 174 | 98.40 213 | 99.62 171 | 99.16 154 | 98.87 201 | 99.41 205 | 97.15 211 | 96.60 238 | 99.31 130 | 97.00 188 | 96.55 213 | 98.91 164 | 98.51 165 | 99.37 174 | 98.82 186 |
|
gg-mvs-nofinetune | | | 98.40 185 | 98.26 177 | 98.57 207 | 99.83 98 | 98.86 182 | 98.77 210 | 99.97 1 | 99.57 34 | 99.99 1 | 99.99 1 | 93.81 199 | 93.50 230 | 98.91 164 | 98.20 181 | 99.33 179 | 98.52 194 |
|
conf0.05thres1000 | | | 98.36 186 | 97.28 195 | 99.63 92 | 99.92 27 | 99.74 25 | 99.66 73 | 99.88 60 | 98.68 138 | 98.92 202 | 97.30 213 | 86.02 228 | 99.49 89 | 99.77 31 | 99.73 19 | 99.93 19 | 99.69 51 |
|
tfpn111 | | | 98.25 187 | 97.29 194 | 99.37 142 | 99.74 142 | 99.52 72 | 99.17 167 | 99.76 147 | 96.10 230 | 98.65 215 | 98.23 190 | 89.10 216 | 99.00 146 | 99.11 133 | 99.56 34 | 99.88 37 | 99.41 126 |
|
PatchT | | | 98.11 188 | 97.12 196 | 99.26 160 | 99.65 168 | 98.34 212 | 99.57 95 | 99.97 1 | 97.48 205 | 99.43 164 | 99.04 154 | 90.84 212 | 98.15 176 | 98.04 207 | 97.78 196 | 98.82 203 | 98.30 200 |
|
thresconf0.02 | | | 98.10 189 | 96.83 199 | 99.58 102 | 99.71 150 | 99.28 134 | 99.40 131 | 99.72 158 | 98.65 141 | 99.39 170 | 98.23 190 | 86.73 226 | 99.43 99 | 97.73 212 | 98.17 183 | 99.86 53 | 99.05 176 |
|
EPNet_dtu | | | 98.09 190 | 98.25 178 | 97.91 223 | 99.58 187 | 98.02 224 | 98.19 227 | 99.67 171 | 97.94 192 | 99.74 92 | 99.07 152 | 98.71 168 | 93.40 231 | 97.50 215 | 97.09 207 | 96.89 219 | 99.44 117 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPNet | | | 98.06 191 | 98.11 184 | 98.00 222 | 99.60 180 | 98.99 172 | 98.38 221 | 99.68 169 | 98.18 179 | 98.85 206 | 97.89 200 | 95.60 193 | 92.72 232 | 98.30 201 | 98.10 188 | 98.76 204 | 99.72 46 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CR-MVSNet | | | 97.91 192 | 96.80 200 | 99.22 167 | 99.60 180 | 98.23 216 | 98.91 197 | 99.97 1 | 96.89 220 | 99.43 164 | 99.10 148 | 89.24 215 | 98.15 176 | 98.04 207 | 97.78 196 | 99.26 184 | 98.30 200 |
|
view800 | | | 97.89 193 | 96.56 202 | 99.45 127 | 99.86 66 | 99.57 55 | 99.42 127 | 99.80 124 | 97.50 204 | 98.40 226 | 93.78 225 | 86.63 227 | 99.31 112 | 99.24 102 | 99.68 24 | 99.89 33 | 99.54 90 |
|
view600 | | | 97.88 194 | 96.55 204 | 99.44 129 | 99.84 87 | 99.52 72 | 99.38 136 | 99.76 147 | 97.36 207 | 98.50 220 | 93.29 226 | 87.31 223 | 99.26 118 | 99.13 131 | 99.76 15 | 99.88 37 | 99.48 109 |
|
thres200 | | | 97.87 195 | 96.56 202 | 99.39 136 | 99.76 131 | 99.52 72 | 99.13 175 | 99.76 147 | 96.88 222 | 98.66 214 | 92.87 231 | 88.77 220 | 99.16 128 | 99.11 133 | 99.42 55 | 99.88 37 | 99.33 144 |
|
thres600view7 | | | 97.86 196 | 96.53 208 | 99.41 134 | 99.84 87 | 99.52 72 | 99.36 140 | 99.76 147 | 97.32 208 | 98.38 227 | 93.24 227 | 87.25 224 | 99.23 121 | 99.11 133 | 99.75 17 | 99.88 37 | 99.48 109 |
|
conf200view11 | | | 97.85 197 | 96.54 205 | 99.37 142 | 99.74 142 | 99.52 72 | 99.17 167 | 99.76 147 | 96.10 230 | 98.65 215 | 92.99 228 | 89.10 216 | 99.00 146 | 99.11 133 | 99.56 34 | 99.88 37 | 99.41 126 |
|
tfpn200view9 | | | 97.85 197 | 96.54 205 | 99.38 139 | 99.74 142 | 99.52 72 | 99.17 167 | 99.76 147 | 96.10 230 | 98.70 211 | 92.99 228 | 89.10 216 | 99.00 146 | 99.11 133 | 99.56 34 | 99.88 37 | 99.41 126 |
|
thres400 | | | 97.82 199 | 96.47 209 | 99.40 135 | 99.81 106 | 99.44 88 | 99.29 155 | 99.69 165 | 97.15 211 | 98.57 217 | 92.82 232 | 87.96 221 | 99.16 128 | 98.96 157 | 99.55 40 | 99.86 53 | 99.41 126 |
|
IB-MVS | | 98.10 14 | 97.76 200 | 97.40 193 | 98.18 216 | 99.62 171 | 99.11 164 | 98.24 225 | 98.35 228 | 96.56 225 | 99.44 162 | 91.28 235 | 98.96 163 | 93.84 228 | 98.09 206 | 98.62 158 | 99.56 149 | 99.18 159 |
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 |
test-LLR | | | 97.74 201 | 97.46 191 | 98.08 219 | 99.62 171 | 98.37 210 | 98.26 223 | 99.41 205 | 97.03 215 | 97.38 234 | 99.54 100 | 92.89 204 | 95.12 225 | 98.78 186 | 97.68 201 | 98.65 207 | 97.90 207 |
|
RPMNet | | | 97.70 202 | 96.54 205 | 99.06 186 | 99.57 190 | 98.23 216 | 98.95 194 | 99.97 1 | 96.89 220 | 99.49 155 | 99.13 143 | 89.63 214 | 97.09 203 | 96.68 221 | 97.02 208 | 99.26 184 | 98.19 204 |
|
thres100view900 | | | 97.69 203 | 96.37 210 | 99.23 162 | 99.74 142 | 99.21 151 | 98.81 208 | 99.43 204 | 96.10 230 | 98.70 211 | 92.99 228 | 89.10 216 | 98.88 159 | 98.58 194 | 99.31 63 | 99.82 74 | 99.27 152 |
|
FMVSNet5 | | | 97.69 203 | 96.98 197 | 98.53 208 | 98.53 233 | 99.36 113 | 98.90 199 | 99.54 188 | 96.38 226 | 98.44 224 | 95.38 221 | 90.08 213 | 97.05 206 | 99.46 61 | 99.06 91 | 98.73 205 | 99.12 170 |
|
MVE | | 91.08 18 | 97.68 205 | 97.65 188 | 97.71 229 | 98.46 234 | 91.62 238 | 97.92 234 | 98.86 222 | 98.73 134 | 97.99 232 | 98.64 175 | 99.96 14 | 99.17 124 | 99.59 50 | 97.75 198 | 93.87 235 | 97.27 215 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test-mter | | | 97.65 206 | 97.57 190 | 97.75 227 | 98.90 231 | 98.56 202 | 98.15 229 | 98.45 227 | 96.92 219 | 96.84 237 | 99.52 107 | 92.53 209 | 95.24 224 | 99.04 145 | 98.12 186 | 98.90 202 | 98.29 202 |
|
TESTMET0.1,1 | | | 97.62 207 | 97.46 191 | 97.81 225 | 99.07 227 | 98.37 210 | 98.26 223 | 98.35 228 | 97.03 215 | 97.38 234 | 99.54 100 | 92.89 204 | 95.12 225 | 98.78 186 | 97.68 201 | 98.65 207 | 97.90 207 |
|
MVSTER | | | 97.55 208 | 96.75 201 | 98.48 210 | 99.46 202 | 99.54 64 | 98.24 225 | 99.77 139 | 97.56 202 | 99.41 168 | 99.31 130 | 84.86 229 | 94.66 227 | 98.86 174 | 97.75 198 | 99.34 178 | 99.38 135 |
|
LP | | | 97.43 209 | 96.28 211 | 98.77 201 | 99.69 156 | 98.92 176 | 99.49 117 | 99.70 163 | 98.53 156 | 99.82 61 | 99.12 145 | 95.67 192 | 97.30 199 | 94.65 224 | 91.76 219 | 96.65 222 | 95.34 224 |
|
MDTV_nov1_ep13 | | | 97.41 210 | 96.26 212 | 98.76 202 | 99.47 201 | 98.43 208 | 99.26 159 | 99.82 106 | 98.06 187 | 99.23 185 | 99.22 137 | 92.86 206 | 98.05 185 | 95.33 223 | 93.66 218 | 96.73 220 | 96.26 219 |
|
ADS-MVSNet | | | 97.29 211 | 96.17 213 | 98.59 206 | 99.59 184 | 98.70 194 | 99.32 146 | 99.86 66 | 98.47 159 | 99.56 138 | 99.08 150 | 98.16 178 | 97.34 198 | 92.92 225 | 91.17 222 | 95.91 224 | 94.72 226 |
|
1111 | | | 96.83 212 | 95.02 218 | 98.95 191 | 99.90 34 | 99.57 55 | 99.62 82 | 99.97 1 | 98.58 152 | 98.06 230 | 99.87 50 | 69.04 239 | 96.43 216 | 99.36 77 | 99.14 80 | 99.73 100 | 99.54 90 |
|
gm-plane-assit | | | 96.82 213 | 94.84 219 | 99.13 177 | 99.95 12 | 99.78 17 | 99.69 70 | 99.92 43 | 99.19 79 | 99.84 45 | 99.92 26 | 72.93 236 | 96.44 215 | 98.21 205 | 97.01 209 | 98.92 200 | 96.87 218 |
|
PatchmatchNet | | | 96.81 214 | 95.41 215 | 98.43 212 | 99.43 207 | 98.30 213 | 99.23 161 | 99.93 36 | 98.19 178 | 99.64 122 | 98.81 167 | 93.50 200 | 97.43 197 | 92.89 227 | 90.78 224 | 94.94 230 | 95.41 223 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tfpn | | | 96.77 215 | 94.47 221 | 99.45 127 | 99.88 43 | 99.62 43 | 99.46 123 | 99.83 98 | 97.61 201 | 98.27 228 | 94.22 224 | 71.45 238 | 99.34 106 | 99.32 85 | 99.46 51 | 99.90 28 | 99.58 79 |
|
EPMVS | | | 96.76 216 | 95.30 217 | 98.46 211 | 99.42 208 | 98.47 206 | 99.32 146 | 99.91 48 | 98.42 167 | 99.51 151 | 99.07 152 | 92.81 207 | 97.12 202 | 92.39 228 | 91.71 220 | 95.51 226 | 94.20 228 |
|
E-PMN | | | 96.72 217 | 95.78 214 | 97.81 225 | 99.45 203 | 95.46 234 | 98.14 231 | 98.33 230 | 97.99 189 | 98.73 210 | 98.09 195 | 98.97 162 | 97.54 195 | 97.45 216 | 91.09 223 | 94.70 232 | 91.40 232 |
|
conf0.01 | | | 96.70 218 | 94.44 223 | 99.34 149 | 99.71 150 | 99.46 85 | 99.17 167 | 99.73 156 | 96.10 230 | 98.53 218 | 91.96 233 | 75.75 234 | 99.00 146 | 98.85 176 | 99.56 34 | 99.87 47 | 99.38 135 |
|
tpm | | | 96.56 219 | 94.68 220 | 98.74 203 | 99.12 224 | 97.90 226 | 98.79 209 | 99.93 36 | 96.79 223 | 99.69 111 | 99.19 139 | 81.48 231 | 97.56 194 | 95.46 222 | 93.97 217 | 97.37 216 | 97.99 206 |
|
EMVS | | | 96.47 220 | 95.38 216 | 97.74 228 | 99.42 208 | 95.37 235 | 98.07 233 | 98.27 231 | 97.85 194 | 98.90 203 | 97.48 207 | 98.73 167 | 97.20 200 | 97.21 218 | 90.39 225 | 94.59 234 | 90.65 233 |
|
conf0.002 | | | 96.39 221 | 93.87 225 | 99.33 151 | 99.70 154 | 99.45 86 | 99.17 167 | 99.71 161 | 96.10 230 | 98.51 219 | 91.88 234 | 72.65 237 | 99.00 146 | 98.80 184 | 98.82 134 | 99.87 47 | 99.38 135 |
|
test2356 | | | 96.34 222 | 94.05 224 | 99.00 187 | 99.39 210 | 98.28 214 | 98.15 229 | 99.51 195 | 96.23 227 | 99.16 189 | 97.95 199 | 73.39 235 | 98.75 163 | 97.07 219 | 96.86 210 | 99.06 196 | 98.57 191 |
|
tpmrst | | | 96.18 223 | 94.47 221 | 98.18 216 | 99.52 191 | 97.89 227 | 98.96 191 | 99.79 127 | 98.07 186 | 99.16 189 | 99.30 133 | 92.69 208 | 96.69 211 | 90.76 230 | 88.85 229 | 94.96 229 | 93.69 230 |
|
CostFormer | | | 95.61 224 | 93.35 228 | 98.24 215 | 99.48 200 | 98.03 223 | 98.65 214 | 99.83 98 | 96.93 218 | 99.42 167 | 98.83 165 | 83.65 230 | 97.08 204 | 90.39 231 | 89.54 228 | 94.94 230 | 96.11 221 |
|
dps | | | 95.59 225 | 93.46 227 | 98.08 219 | 99.33 215 | 98.22 219 | 98.87 201 | 99.70 163 | 96.17 228 | 98.87 205 | 97.75 202 | 86.85 225 | 96.60 212 | 91.24 229 | 89.62 227 | 95.10 228 | 94.34 227 |
|
tpm cat1 | | | 95.52 226 | 93.49 226 | 97.88 224 | 99.28 220 | 97.87 228 | 98.65 214 | 99.77 139 | 97.27 209 | 99.46 160 | 98.04 197 | 90.99 211 | 95.46 223 | 88.57 234 | 88.14 232 | 94.64 233 | 93.54 231 |
|
tpmp4_e23 | | | 95.42 227 | 92.99 229 | 98.27 214 | 99.32 217 | 97.77 230 | 98.74 211 | 99.79 127 | 97.11 213 | 99.61 126 | 97.47 208 | 80.64 232 | 96.36 218 | 92.92 225 | 88.79 230 | 95.80 225 | 96.19 220 |
|
DWT-MVSNet_training | | | 94.92 228 | 92.14 230 | 98.15 218 | 99.37 212 | 98.43 208 | 98.99 189 | 98.51 225 | 96.76 224 | 99.52 147 | 97.35 211 | 77.20 233 | 97.08 204 | 89.76 232 | 90.38 226 | 95.43 227 | 95.13 225 |
|
testpf | | | 93.65 229 | 91.79 231 | 95.82 230 | 98.71 232 | 93.25 236 | 96.38 236 | 99.67 171 | 95.38 236 | 97.83 233 | 94.48 223 | 87.69 222 | 89.61 234 | 88.96 233 | 88.79 230 | 92.71 236 | 93.97 229 |
|
.test1245 | | | 79.44 230 | 75.07 232 | 84.53 232 | 99.90 34 | 99.57 55 | 99.62 82 | 99.97 1 | 98.58 152 | 98.06 230 | 99.87 50 | 69.04 239 | 96.43 216 | 99.36 77 | 24.74 233 | 13.21 237 | 34.30 234 |
|
GG-mvs-BLEND | | | 70.44 231 | 96.91 198 | 39.57 233 | 3.32 239 | 96.51 232 | 91.01 238 | 4.05 237 | 97.03 215 | 33.20 240 | 94.67 222 | 97.75 181 | 7.59 237 | 98.28 203 | 96.85 211 | 98.24 210 | 97.26 216 |
|
testmvs | | | 22.33 232 | 29.66 233 | 13.79 234 | 8.97 237 | 10.35 239 | 15.53 241 | 8.09 236 | 32.51 237 | 19.87 241 | 45.18 236 | 30.56 242 | 17.05 236 | 29.96 235 | 24.74 233 | 13.21 237 | 34.30 234 |
|
test123 | | | 21.52 233 | 28.47 234 | 13.42 235 | 7.29 238 | 10.12 240 | 15.70 240 | 8.31 235 | 31.54 238 | 19.34 242 | 36.33 237 | 37.40 241 | 17.14 235 | 27.45 236 | 23.17 235 | 12.73 239 | 33.30 236 |
|
sosnet-low-res | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 240 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 243 | 0.00 238 | 0.00 243 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
sosnet | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 240 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 243 | 0.00 238 | 0.00 243 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
ambc | | | | 98.83 148 | | 99.72 147 | 98.52 203 | 98.84 204 | | 98.96 108 | 99.92 9 | 99.34 125 | 99.74 94 | 99.04 144 | 98.68 191 | 97.57 204 | 99.46 160 | 98.99 183 |
|
MTAPA | | | | | | | | | | | 99.62 125 | | 99.95 22 | | | | | |
|
MTMP | | | | | | | | | | | 99.53 141 | | 99.92 47 | | | | | |
|
Patchmatch-RL test | | | | | | | | 65.75 239 | | | | | | | | | | |
|
tmp_tt | | | | | 88.14 231 | 96.68 236 | 91.91 237 | 93.70 237 | 61.38 234 | 99.61 24 | 90.51 239 | 99.40 121 | 99.71 99 | 90.32 233 | 99.22 108 | 99.44 54 | 96.25 223 | |
|
XVS | | | | | | 99.86 66 | 99.30 129 | 99.72 62 | | | 99.69 111 | | 99.93 38 | | | | 99.60 137 | |
|
X-MVStestdata | | | | | | 99.86 66 | 99.30 129 | 99.72 62 | | | 99.69 111 | | 99.93 38 | | | | 99.60 137 | |
|
abl_6 | | | | | 99.21 169 | 99.49 199 | 98.62 198 | 98.90 199 | 99.44 203 | 97.08 214 | 99.61 126 | 97.19 214 | 99.73 97 | 98.35 173 | | | 99.45 162 | 98.84 185 |
|
mPP-MVS | | | | | | 99.84 87 | | | | | | | 99.92 47 | | | | | |
|
NP-MVS | | | | | | | | | | 97.37 206 | | | | | | | | |
|
Patchmtry | | | | | | | 98.19 221 | 98.91 197 | 99.97 1 | | 99.43 164 | | | | | | | |
|
DeepMVS_CX | | | | | | | 96.39 233 | 97.15 235 | 88.89 233 | 97.94 192 | 99.51 151 | 95.71 220 | 97.88 180 | 98.19 174 | 98.92 161 | | 97.73 214 | 97.75 212 |
|