Anonymous20231211 | | | 99.83 1 | 99.80 1 | 99.86 1 | 99.97 1 | 99.87 1 | 99.90 1 | 99.92 1 | 99.76 1 | 99.82 2 | 99.79 37 | 99.98 1 | 99.63 13 | 99.84 3 | 99.78 3 | 99.94 1 | 99.61 6 |
|
LTVRE_ROB | | 98.82 1 | 99.76 2 | 99.75 2 | 99.77 8 | 99.87 18 | 99.71 9 | 99.77 12 | 99.76 23 | 99.52 3 | 99.80 3 | 99.79 37 | 99.91 2 | 99.56 19 | 99.83 4 | 99.75 4 | 99.86 9 | 99.75 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 |
pmmvs6 | | | 99.74 3 | 99.75 2 | 99.73 15 | 99.92 5 | 99.67 16 | 99.76 15 | 99.84 11 | 99.59 2 | 99.52 27 | 99.87 18 | 99.91 2 | 99.43 40 | 99.87 1 | 99.81 2 | 99.89 6 | 99.52 10 |
|
SixPastTwentyTwo | | | 99.70 4 | 99.59 7 | 99.82 3 | 99.93 3 | 99.80 2 | 99.86 3 | 99.87 7 | 98.87 14 | 99.79 5 | 99.85 27 | 99.33 64 | 99.74 7 | 99.85 2 | 99.82 1 | 99.74 23 | 99.63 4 |
|
v7n | | | 99.68 5 | 99.61 4 | 99.76 9 | 99.89 14 | 99.74 8 | 99.87 2 | 99.82 14 | 99.20 6 | 99.71 6 | 99.96 1 | 99.73 12 | 99.76 5 | 99.58 18 | 99.59 16 | 99.52 44 | 99.46 17 |
|
v748 | | | 99.67 6 | 99.61 4 | 99.75 13 | 99.87 18 | 99.68 14 | 99.84 6 | 99.79 16 | 99.14 7 | 99.64 17 | 99.89 12 | 99.88 5 | 99.72 8 | 99.58 18 | 99.57 18 | 99.62 31 | 99.50 13 |
|
v52 | | | 99.67 6 | 99.59 7 | 99.76 9 | 99.91 9 | 99.69 12 | 99.85 4 | 99.79 16 | 99.12 9 | 99.68 12 | 99.95 2 | 99.72 14 | 99.77 2 | 99.58 18 | 99.61 12 | 99.54 39 | 99.50 13 |
|
V4 | | | 99.67 6 | 99.60 6 | 99.76 9 | 99.91 9 | 99.69 12 | 99.85 4 | 99.79 16 | 99.13 8 | 99.68 12 | 99.95 2 | 99.72 14 | 99.77 2 | 99.58 18 | 99.61 12 | 99.54 39 | 99.50 13 |
|
anonymousdsp | | | 99.64 9 | 99.55 9 | 99.74 14 | 99.87 18 | 99.56 23 | 99.82 7 | 99.73 29 | 98.54 19 | 99.71 6 | 99.92 6 | 99.84 7 | 99.61 14 | 99.70 6 | 99.63 7 | 99.69 27 | 99.64 2 |
|
WR-MVS | | | 99.61 10 | 99.44 11 | 99.82 3 | 99.92 5 | 99.80 2 | 99.80 8 | 99.89 2 | 98.54 19 | 99.66 15 | 99.78 40 | 99.16 87 | 99.68 10 | 99.70 6 | 99.63 7 | 99.94 1 | 99.49 16 |
|
PEN-MVS | | | 99.54 11 | 99.30 19 | 99.83 2 | 99.92 5 | 99.76 5 | 99.80 8 | 99.88 4 | 97.60 67 | 99.71 6 | 99.59 56 | 99.52 43 | 99.75 6 | 99.64 13 | 99.51 20 | 99.90 3 | 99.46 17 |
|
TDRefinement | | | 99.54 11 | 99.50 10 | 99.60 20 | 99.70 63 | 99.35 46 | 99.77 12 | 99.58 56 | 99.40 5 | 99.28 57 | 99.66 45 | 99.41 52 | 99.55 21 | 99.74 5 | 99.65 6 | 99.70 24 | 99.25 28 |
|
Anonymous20240521 | | | 99.52 13 | 99.38 12 | 99.69 16 | 99.88 16 | 99.71 9 | 99.77 12 | 99.78 19 | 98.23 26 | 99.21 62 | 99.60 53 | 99.42 51 | 99.64 12 | 99.68 9 | 99.67 5 | 99.85 10 | 99.38 21 |
|
DTE-MVSNet | | | 99.52 13 | 99.27 20 | 99.82 3 | 99.93 3 | 99.77 4 | 99.79 10 | 99.87 7 | 97.89 46 | 99.70 11 | 99.55 63 | 99.21 79 | 99.77 2 | 99.65 11 | 99.43 24 | 99.90 3 | 99.36 22 |
|
PS-CasMVS | | | 99.50 15 | 99.23 22 | 99.82 3 | 99.92 5 | 99.75 7 | 99.78 11 | 99.89 2 | 97.30 86 | 99.71 6 | 99.60 53 | 99.23 75 | 99.71 9 | 99.65 11 | 99.55 19 | 99.90 3 | 99.56 8 |
|
WR-MVS_H | | | 99.48 16 | 99.23 22 | 99.76 9 | 99.91 9 | 99.76 5 | 99.75 16 | 99.88 4 | 97.27 89 | 99.58 20 | 99.56 60 | 99.24 73 | 99.56 19 | 99.60 16 | 99.60 15 | 99.88 8 | 99.58 7 |
|
pm-mvs1 | | | 99.47 17 | 99.38 12 | 99.57 23 | 99.82 26 | 99.49 33 | 99.63 30 | 99.65 44 | 98.88 13 | 99.31 47 | 99.85 27 | 99.02 113 | 99.23 66 | 99.60 16 | 99.58 17 | 99.80 16 | 99.22 32 |
|
MIMVSNet1 | | | 99.46 18 | 99.34 14 | 99.60 20 | 99.83 24 | 99.68 14 | 99.74 19 | 99.71 34 | 98.20 28 | 99.41 35 | 99.86 22 | 99.66 27 | 99.41 43 | 99.50 24 | 99.39 26 | 99.50 50 | 99.10 42 |
|
TransMVSNet (Re) | | | 99.45 19 | 99.32 17 | 99.61 18 | 99.88 16 | 99.60 19 | 99.75 16 | 99.63 48 | 99.11 10 | 99.28 57 | 99.83 31 | 98.35 141 | 99.27 63 | 99.70 6 | 99.62 11 | 99.84 11 | 99.03 49 |
|
ACMH | | 97.81 6 | 99.44 20 | 99.33 15 | 99.56 24 | 99.81 29 | 99.42 39 | 99.73 20 | 99.58 56 | 99.02 11 | 99.10 82 | 99.41 74 | 99.69 19 | 99.60 15 | 99.45 28 | 99.26 36 | 99.55 38 | 99.05 46 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CP-MVSNet | | | 99.39 21 | 99.04 30 | 99.80 7 | 99.91 9 | 99.70 11 | 99.75 16 | 99.88 4 | 96.82 108 | 99.68 12 | 99.32 77 | 98.86 121 | 99.68 10 | 99.57 22 | 99.47 22 | 99.89 6 | 99.52 10 |
|
COLMAP_ROB | | 98.29 2 | 99.37 22 | 99.25 21 | 99.51 31 | 99.74 51 | 99.12 81 | 99.56 41 | 99.39 94 | 98.96 12 | 99.17 69 | 99.44 71 | 99.63 33 | 99.58 16 | 99.48 26 | 99.27 34 | 99.60 35 | 98.81 75 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
DeepC-MVS | | 97.88 4 | 99.33 23 | 99.15 26 | 99.53 30 | 99.73 56 | 99.05 90 | 99.49 57 | 99.40 92 | 98.42 22 | 99.55 24 | 99.71 43 | 99.89 4 | 99.49 30 | 99.14 39 | 98.81 64 | 99.54 39 | 99.02 52 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
FC-MVSNet-test | | | 99.32 24 | 99.33 15 | 99.31 66 | 99.87 18 | 99.65 18 | 99.63 30 | 99.75 26 | 97.76 51 | 97.29 203 | 99.87 18 | 99.63 33 | 99.52 25 | 99.66 10 | 99.63 7 | 99.77 20 | 99.12 38 |
|
UA-Net | | | 99.30 25 | 99.22 24 | 99.39 47 | 99.94 2 | 99.66 17 | 98.91 124 | 99.86 9 | 97.74 56 | 98.74 124 | 99.00 103 | 99.60 38 | 99.17 72 | 99.50 24 | 99.39 26 | 99.70 24 | 99.64 2 |
|
ACMH+ | | 97.53 7 | 99.29 26 | 99.20 25 | 99.40 46 | 99.81 29 | 99.22 63 | 99.59 37 | 99.50 77 | 98.64 18 | 98.29 155 | 99.21 86 | 99.69 19 | 99.57 17 | 99.53 23 | 99.33 31 | 99.66 29 | 98.81 75 |
|
Vis-MVSNet | | | 99.25 27 | 99.32 17 | 99.17 80 | 99.65 80 | 99.55 27 | 99.63 30 | 99.33 120 | 98.16 29 | 99.29 52 | 99.65 49 | 99.77 8 | 97.56 154 | 99.44 29 | 99.14 40 | 99.58 36 | 99.51 12 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
TranMVSNet+NR-MVSNet | | | 99.23 28 | 98.91 39 | 99.61 18 | 99.81 29 | 99.45 37 | 99.47 59 | 99.68 38 | 97.28 88 | 99.39 36 | 99.54 64 | 99.08 108 | 99.45 35 | 99.09 44 | 98.84 62 | 99.83 12 | 99.04 47 |
|
CSCG | | | 99.23 28 | 99.15 26 | 99.32 65 | 99.83 24 | 99.45 37 | 98.97 116 | 99.21 140 | 98.83 15 | 99.04 94 | 99.43 72 | 99.64 31 | 99.26 64 | 98.85 66 | 98.20 104 | 99.62 31 | 99.62 5 |
|
v13 | | | 99.22 30 | 98.99 32 | 99.49 32 | 99.68 67 | 99.58 21 | 99.67 21 | 99.77 22 | 98.10 30 | 99.36 38 | 99.88 13 | 99.37 58 | 99.54 23 | 98.50 83 | 98.51 90 | 98.92 122 | 99.03 49 |
|
Gipuma | | | 99.22 30 | 98.86 43 | 99.64 17 | 99.70 63 | 99.24 58 | 99.17 97 | 99.63 48 | 99.52 3 | 99.89 1 | 96.54 179 | 99.14 93 | 99.93 1 | 99.42 30 | 99.15 39 | 99.52 44 | 99.04 47 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
tfpnnormal | | | 99.19 32 | 98.90 40 | 99.54 27 | 99.81 29 | 99.55 27 | 99.60 36 | 99.54 67 | 98.53 21 | 99.23 61 | 98.40 121 | 98.23 144 | 99.40 44 | 99.29 34 | 99.36 29 | 99.63 30 | 98.95 62 |
|
v12 | | | 99.19 32 | 98.95 33 | 99.48 33 | 99.67 70 | 99.56 23 | 99.66 23 | 99.76 23 | 98.06 32 | 99.33 43 | 99.88 13 | 99.34 63 | 99.53 24 | 98.42 90 | 98.43 95 | 98.91 125 | 98.97 56 |
|
v11 | | | 99.19 32 | 98.95 33 | 99.47 34 | 99.66 74 | 99.54 29 | 99.65 24 | 99.73 29 | 98.06 32 | 99.38 37 | 99.92 6 | 99.40 55 | 99.55 21 | 98.29 103 | 98.50 91 | 98.88 130 | 98.92 65 |
|
Baseline_NR-MVSNet | | | 99.18 35 | 98.87 42 | 99.54 27 | 99.74 51 | 99.56 23 | 99.36 72 | 99.62 52 | 96.53 129 | 99.29 52 | 99.85 27 | 98.64 135 | 99.40 44 | 99.03 54 | 99.63 7 | 99.83 12 | 98.86 70 |
|
V9 | | | 99.16 36 | 98.90 40 | 99.46 35 | 99.66 74 | 99.54 29 | 99.65 24 | 99.75 26 | 98.01 35 | 99.31 47 | 99.87 18 | 99.31 67 | 99.51 26 | 98.34 97 | 98.34 98 | 98.90 127 | 98.91 66 |
|
APDe-MVS | | | 99.15 37 | 98.95 33 | 99.39 47 | 99.77 38 | 99.28 55 | 99.52 52 | 99.54 67 | 97.22 94 | 99.06 89 | 99.20 87 | 99.64 31 | 99.05 83 | 99.14 39 | 99.02 53 | 99.39 70 | 99.17 36 |
|
FC-MVSNet-train | | | 99.13 38 | 99.05 29 | 99.21 75 | 99.87 18 | 99.57 22 | 99.67 21 | 99.60 55 | 96.75 115 | 98.28 156 | 99.48 68 | 99.52 43 | 98.10 136 | 99.47 27 | 99.37 28 | 99.76 22 | 99.21 33 |
|
V14 | | | 99.13 38 | 98.85 45 | 99.45 36 | 99.65 80 | 99.52 31 | 99.63 30 | 99.74 28 | 97.97 37 | 99.30 50 | 99.87 18 | 99.27 71 | 99.49 30 | 98.23 109 | 98.24 101 | 98.88 130 | 98.83 71 |
|
NR-MVSNet | | | 99.10 40 | 98.68 57 | 99.58 22 | 99.89 14 | 99.23 60 | 99.35 73 | 99.63 48 | 96.58 123 | 99.36 38 | 99.05 97 | 98.67 133 | 99.46 33 | 99.63 14 | 98.73 74 | 99.80 16 | 98.88 69 |
|
v15 | | | 99.09 41 | 98.79 48 | 99.43 40 | 99.64 88 | 99.50 32 | 99.61 34 | 99.73 29 | 97.92 41 | 99.28 57 | 99.86 22 | 99.24 73 | 99.47 32 | 98.12 120 | 98.14 106 | 98.87 132 | 98.76 82 |
|
UniMVSNet (Re) | | | 99.08 42 | 98.69 56 | 99.54 27 | 99.75 47 | 99.33 49 | 99.29 80 | 99.64 47 | 96.75 115 | 99.48 31 | 99.30 79 | 98.69 129 | 99.26 64 | 98.94 60 | 98.76 70 | 99.78 19 | 99.02 52 |
|
ACMMPR | | | 99.05 43 | 98.72 52 | 99.44 37 | 99.79 34 | 99.12 81 | 99.35 73 | 99.56 59 | 97.74 56 | 99.21 62 | 97.72 145 | 99.55 41 | 99.29 61 | 98.90 65 | 98.81 64 | 99.41 65 | 99.19 34 |
|
DU-MVS | | | 99.04 44 | 98.59 62 | 99.56 24 | 99.74 51 | 99.23 60 | 99.29 80 | 99.63 48 | 96.58 123 | 99.55 24 | 99.05 97 | 98.68 131 | 99.36 56 | 99.03 54 | 98.60 84 | 99.77 20 | 98.97 56 |
|
TSAR-MVS + MP. | | | 99.02 45 | 98.95 33 | 99.11 88 | 99.23 169 | 98.79 130 | 99.51 53 | 98.73 180 | 97.50 72 | 98.56 132 | 99.03 100 | 99.59 39 | 99.16 74 | 99.29 34 | 99.17 38 | 99.50 50 | 99.24 31 |
|
v10 | | | 99.01 46 | 98.66 58 | 99.41 43 | 99.52 123 | 99.39 42 | 99.57 39 | 99.66 42 | 97.59 68 | 99.32 45 | 99.88 13 | 99.23 75 | 99.50 28 | 97.77 148 | 97.98 116 | 98.92 122 | 98.78 80 |
|
EG-PatchMatch MVS | | | 99.01 46 | 98.77 50 | 99.28 74 | 99.64 88 | 98.90 125 | 98.81 136 | 99.27 131 | 96.55 127 | 99.71 6 | 99.31 78 | 99.66 27 | 99.17 72 | 99.28 36 | 99.11 43 | 99.10 98 | 98.57 98 |
|
no-one | | | 99.01 46 | 98.94 37 | 99.09 92 | 98.97 192 | 98.55 151 | 99.37 70 | 99.04 162 | 97.59 68 | 99.36 38 | 99.66 45 | 99.75 9 | 99.57 17 | 98.47 84 | 99.27 34 | 98.21 182 | 99.30 26 |
|
PVSNet_Blended_VisFu | | | 98.98 49 | 98.79 48 | 99.21 75 | 99.76 44 | 99.34 47 | 99.35 73 | 99.35 117 | 97.12 100 | 99.46 32 | 99.56 60 | 98.89 119 | 98.08 139 | 99.05 49 | 98.58 86 | 99.27 88 | 98.98 55 |
|
HFP-MVS | | | 98.97 50 | 98.70 54 | 99.29 70 | 99.67 70 | 98.98 106 | 99.13 101 | 99.53 71 | 97.76 51 | 98.90 110 | 98.07 135 | 99.50 48 | 99.14 78 | 98.64 78 | 98.78 68 | 99.37 72 | 99.18 35 |
|
UniMVSNet_NR-MVSNet | | | 98.97 50 | 98.46 74 | 99.56 24 | 99.76 44 | 99.34 47 | 99.29 80 | 99.61 53 | 96.55 127 | 99.55 24 | 99.05 97 | 97.96 155 | 99.36 56 | 98.84 67 | 98.50 91 | 99.81 15 | 98.97 56 |
|
v17 | | | 98.96 52 | 98.63 59 | 99.35 61 | 99.54 111 | 99.41 40 | 99.55 44 | 99.70 35 | 97.40 81 | 99.10 82 | 99.79 37 | 99.10 102 | 99.40 44 | 97.96 127 | 97.99 114 | 98.80 146 | 98.77 81 |
|
v16 | | | 98.95 53 | 98.62 60 | 99.34 63 | 99.53 118 | 99.41 40 | 99.54 48 | 99.70 35 | 97.34 85 | 99.07 88 | 99.76 41 | 99.10 102 | 99.40 44 | 97.96 127 | 98.00 113 | 98.79 148 | 98.76 82 |
|
SMA-MVS | | | 98.94 54 | 98.80 47 | 99.11 88 | 99.73 56 | 99.09 83 | 98.78 138 | 99.18 145 | 96.32 138 | 98.89 113 | 99.19 89 | 99.72 14 | 98.75 98 | 99.09 44 | 98.89 57 | 99.31 82 | 99.27 27 |
|
ACMMP_Plus | | | 98.94 54 | 98.72 52 | 99.21 75 | 99.67 70 | 99.08 85 | 99.26 85 | 99.39 94 | 96.84 105 | 98.88 115 | 98.22 128 | 99.68 22 | 98.82 93 | 99.06 48 | 98.90 56 | 99.25 90 | 99.25 28 |
|
zzz-MVS | | | 98.94 54 | 98.57 65 | 99.37 54 | 99.77 38 | 99.15 77 | 99.24 88 | 99.55 61 | 97.38 83 | 99.16 72 | 96.64 175 | 99.69 19 | 99.15 76 | 99.09 44 | 98.92 55 | 99.37 72 | 99.11 39 |
|
v1144 | | | 98.94 54 | 98.53 68 | 99.42 42 | 99.62 95 | 99.03 99 | 99.58 38 | 99.36 112 | 97.99 36 | 99.49 30 | 99.91 11 | 99.20 81 | 99.51 26 | 97.61 160 | 97.85 128 | 98.95 117 | 98.10 139 |
|
v8 | | | 98.94 54 | 98.60 61 | 99.35 61 | 99.54 111 | 99.39 42 | 99.55 44 | 99.67 41 | 97.48 74 | 99.13 77 | 99.81 32 | 99.10 102 | 99.39 54 | 97.86 137 | 97.89 122 | 98.81 141 | 98.66 92 |
|
SteuartSystems-ACMMP | | | 98.94 54 | 98.52 70 | 99.43 40 | 99.79 34 | 99.13 79 | 99.33 77 | 99.55 61 | 96.17 143 | 99.04 94 | 97.53 151 | 99.65 30 | 99.46 33 | 99.04 53 | 98.76 70 | 99.44 57 | 99.35 23 |
Skip Steuart: Steuart Systems R&D Blog. |
v1192 | | | 98.91 60 | 98.48 73 | 99.41 43 | 99.61 98 | 99.03 99 | 99.64 27 | 99.25 135 | 97.91 43 | 99.58 20 | 99.92 6 | 99.07 110 | 99.45 35 | 97.55 164 | 97.68 145 | 98.93 119 | 98.23 126 |
|
v7 | | | 98.91 60 | 98.53 68 | 99.36 56 | 99.53 118 | 98.99 105 | 99.57 39 | 99.36 112 | 97.58 70 | 99.32 45 | 99.88 13 | 99.23 75 | 99.50 28 | 97.77 148 | 97.98 116 | 98.91 125 | 98.26 123 |
|
FMVSNet1 | | | 98.90 62 | 99.10 28 | 98.67 138 | 99.54 111 | 99.48 34 | 99.22 91 | 99.66 42 | 98.39 25 | 97.50 191 | 99.66 45 | 99.04 111 | 96.58 174 | 99.05 49 | 99.03 50 | 99.52 44 | 99.08 44 |
|
ACMM | | 96.66 11 | 98.90 62 | 98.44 84 | 99.44 37 | 99.74 51 | 98.95 115 | 99.47 59 | 99.55 61 | 97.66 63 | 99.09 86 | 96.43 180 | 99.41 52 | 99.35 59 | 98.95 59 | 98.67 79 | 99.45 55 | 99.03 49 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v1921920 | | | 98.89 64 | 98.46 74 | 99.39 47 | 99.58 101 | 99.04 94 | 99.64 27 | 99.17 147 | 97.91 43 | 99.64 17 | 99.92 6 | 98.99 117 | 99.44 38 | 97.44 171 | 97.57 155 | 98.84 139 | 98.35 114 |
|
v18 | | | 98.89 64 | 98.54 66 | 99.30 67 | 99.50 126 | 99.37 45 | 99.51 53 | 99.68 38 | 97.25 93 | 99.00 97 | 99.76 41 | 99.04 111 | 99.36 56 | 97.81 144 | 97.86 127 | 98.77 151 | 98.68 91 |
|
v144192 | | | 98.88 66 | 98.46 74 | 99.37 54 | 99.56 106 | 99.03 99 | 99.61 34 | 99.26 132 | 97.79 50 | 99.58 20 | 99.88 13 | 99.11 101 | 99.43 40 | 97.38 175 | 97.61 151 | 98.80 146 | 98.43 109 |
|
v1141 | | | 98.87 67 | 98.45 78 | 99.36 56 | 99.65 80 | 99.04 94 | 99.56 41 | 99.38 101 | 97.83 47 | 99.29 52 | 99.86 22 | 99.16 87 | 99.40 44 | 97.68 154 | 97.78 131 | 98.86 135 | 97.82 151 |
|
divwei89l23v2f112 | | | 98.87 67 | 98.45 78 | 99.36 56 | 99.65 80 | 99.04 94 | 99.56 41 | 99.38 101 | 97.83 47 | 99.29 52 | 99.86 22 | 99.15 91 | 99.40 44 | 97.68 154 | 97.78 131 | 98.86 135 | 97.82 151 |
|
v1 | | | 98.87 67 | 98.45 78 | 99.36 56 | 99.65 80 | 99.04 94 | 99.55 44 | 99.38 101 | 97.83 47 | 99.30 50 | 99.86 22 | 99.17 84 | 99.40 44 | 97.68 154 | 97.77 138 | 98.86 135 | 97.82 151 |
|
ACMP | | 96.54 13 | 98.87 67 | 98.40 89 | 99.41 43 | 99.74 51 | 98.88 126 | 99.29 80 | 99.50 77 | 96.85 104 | 98.96 101 | 97.05 164 | 99.66 27 | 99.43 40 | 98.98 58 | 98.60 84 | 99.52 44 | 98.81 75 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
v1240 | | | 98.86 71 | 98.41 87 | 99.38 52 | 99.59 99 | 99.05 90 | 99.65 24 | 99.14 151 | 97.68 62 | 99.66 15 | 99.93 5 | 98.72 128 | 99.45 35 | 97.38 175 | 97.72 143 | 98.79 148 | 98.35 114 |
|
CP-MVS | | | 98.86 71 | 98.43 86 | 99.36 56 | 99.68 67 | 98.97 113 | 99.19 96 | 99.46 86 | 96.60 122 | 99.20 64 | 97.11 163 | 99.51 46 | 99.15 76 | 98.92 63 | 98.82 63 | 99.45 55 | 99.08 44 |
|
v2v482 | | | 98.85 73 | 98.40 89 | 99.38 52 | 99.65 80 | 98.98 106 | 99.55 44 | 99.39 94 | 97.92 41 | 99.35 41 | 99.85 27 | 99.14 93 | 99.39 54 | 97.50 166 | 97.78 131 | 98.98 114 | 97.60 158 |
|
OPM-MVS | | | 98.84 74 | 98.59 62 | 99.12 86 | 99.52 123 | 98.50 156 | 99.13 101 | 99.22 138 | 97.76 51 | 98.76 121 | 98.70 112 | 99.61 36 | 98.90 88 | 98.67 76 | 98.37 97 | 99.19 94 | 98.57 98 |
|
v1neww | | | 98.84 74 | 98.45 78 | 99.29 70 | 99.54 111 | 98.98 106 | 99.54 48 | 99.37 109 | 97.48 74 | 99.10 82 | 99.80 35 | 99.12 97 | 99.40 44 | 97.85 140 | 97.89 122 | 98.81 141 | 98.04 142 |
|
v7new | | | 98.84 74 | 98.45 78 | 99.29 70 | 99.54 111 | 98.98 106 | 99.54 48 | 99.37 109 | 97.48 74 | 99.10 82 | 99.80 35 | 99.12 97 | 99.40 44 | 97.85 140 | 97.89 122 | 98.81 141 | 98.04 142 |
|
v6 | | | 98.84 74 | 98.46 74 | 99.30 67 | 99.54 111 | 98.98 106 | 99.54 48 | 99.37 109 | 97.49 73 | 99.11 81 | 99.81 32 | 99.13 96 | 99.40 44 | 97.86 137 | 97.89 122 | 98.81 141 | 98.04 142 |
|
test20.03 | | | 98.84 74 | 98.74 51 | 98.95 108 | 99.77 38 | 99.33 49 | 99.21 93 | 99.46 86 | 97.29 87 | 98.88 115 | 99.65 49 | 99.10 102 | 97.07 168 | 99.11 41 | 98.76 70 | 99.32 81 | 97.98 147 |
|
LGP-MVS_train | | | 98.84 74 | 98.33 95 | 99.44 37 | 99.78 36 | 98.98 106 | 99.39 68 | 99.55 61 | 95.41 158 | 98.90 110 | 97.51 152 | 99.68 22 | 99.44 38 | 99.03 54 | 98.81 64 | 99.57 37 | 98.91 66 |
|
RPSCF | | | 98.84 74 | 98.81 46 | 98.89 113 | 99.37 141 | 98.95 115 | 98.51 165 | 98.85 173 | 97.73 58 | 98.33 152 | 98.97 105 | 99.14 93 | 98.95 86 | 99.18 38 | 98.68 78 | 99.31 82 | 98.99 54 |
|
ACMMP | | | 98.82 81 | 98.33 95 | 99.39 47 | 99.77 38 | 99.14 78 | 99.37 70 | 99.54 67 | 96.47 133 | 99.03 96 | 96.26 184 | 99.52 43 | 99.28 62 | 98.92 63 | 98.80 67 | 99.37 72 | 99.16 37 |
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 |
V42 | | | 98.81 82 | 98.49 72 | 99.18 79 | 99.52 123 | 98.92 121 | 99.50 56 | 99.29 128 | 97.43 79 | 98.97 99 | 99.81 32 | 99.00 116 | 99.30 60 | 97.93 130 | 98.01 112 | 98.51 171 | 98.34 118 |
|
LS3D | | | 98.79 83 | 98.52 70 | 99.12 86 | 99.64 88 | 99.09 83 | 99.24 88 | 99.46 86 | 97.75 54 | 98.93 107 | 97.47 153 | 98.23 144 | 97.98 142 | 99.36 31 | 99.30 33 | 99.46 54 | 98.42 110 |
|
MP-MVS | | | 98.78 84 | 98.30 97 | 99.34 63 | 99.75 47 | 98.95 115 | 99.26 85 | 99.46 86 | 95.78 154 | 99.17 69 | 96.98 168 | 99.72 14 | 99.06 82 | 98.84 67 | 98.74 73 | 99.33 78 | 99.11 39 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
v148 | | | 98.77 85 | 98.45 78 | 99.15 82 | 99.68 67 | 98.94 119 | 99.49 57 | 99.31 127 | 97.95 39 | 98.91 109 | 99.65 49 | 99.62 35 | 99.18 69 | 97.99 126 | 97.64 149 | 98.33 176 | 97.38 167 |
|
SD-MVS | | | 98.73 86 | 98.54 66 | 98.95 108 | 99.14 178 | 98.76 132 | 98.46 168 | 99.14 151 | 97.71 60 | 98.56 132 | 98.06 137 | 99.61 36 | 98.85 92 | 98.56 80 | 97.74 140 | 99.54 39 | 99.32 24 |
|
PGM-MVS | | | 98.69 87 | 98.09 111 | 99.39 47 | 99.76 44 | 99.07 86 | 99.30 79 | 99.51 74 | 94.76 174 | 99.18 68 | 96.70 173 | 99.51 46 | 99.20 67 | 98.79 72 | 98.71 77 | 99.39 70 | 99.11 39 |
|
pmmvs-eth3d | | | 98.68 88 | 98.14 107 | 99.29 70 | 99.49 129 | 98.45 159 | 99.45 63 | 99.38 101 | 97.21 95 | 99.50 29 | 99.65 49 | 99.21 79 | 99.16 74 | 97.11 183 | 97.56 156 | 98.79 148 | 97.82 151 |
|
EU-MVSNet | | | 98.68 88 | 98.94 37 | 98.37 159 | 99.14 178 | 98.74 136 | 99.64 27 | 98.20 205 | 98.21 27 | 99.17 69 | 99.66 45 | 99.18 83 | 99.08 80 | 99.11 41 | 98.86 58 | 95.00 214 | 98.83 71 |
|
PMVS | | 92.51 17 | 98.66 90 | 98.86 43 | 98.43 154 | 99.26 163 | 98.98 106 | 98.60 158 | 98.59 189 | 97.73 58 | 99.45 33 | 99.38 75 | 98.54 138 | 95.24 195 | 99.62 15 | 99.61 12 | 99.42 62 | 98.17 135 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
DeepC-MVS_fast | | 97.38 8 | 98.65 91 | 98.34 94 | 99.02 101 | 99.33 149 | 98.29 164 | 98.99 113 | 98.71 182 | 97.40 81 | 99.31 47 | 98.20 129 | 99.40 55 | 98.54 113 | 98.33 100 | 98.18 105 | 99.23 93 | 98.58 96 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
3Dnovator | | 98.16 3 | 98.65 91 | 98.35 93 | 99.00 104 | 99.59 99 | 98.70 138 | 98.90 127 | 99.36 112 | 97.97 37 | 99.09 86 | 96.55 178 | 99.09 106 | 97.97 143 | 98.70 75 | 98.65 82 | 99.12 97 | 98.81 75 |
|
TSAR-MVS + ACMM | | | 98.64 93 | 98.58 64 | 98.72 131 | 99.17 175 | 98.63 144 | 98.69 143 | 99.10 158 | 97.69 61 | 98.30 154 | 99.12 93 | 99.38 57 | 98.70 101 | 98.45 85 | 97.51 158 | 98.35 175 | 99.25 28 |
|
DELS-MVS | | | 98.63 94 | 98.70 54 | 98.55 149 | 99.24 168 | 99.04 94 | 98.96 117 | 98.52 192 | 96.83 107 | 98.38 148 | 99.58 58 | 99.68 22 | 97.06 169 | 98.74 74 | 98.44 94 | 99.10 98 | 98.59 95 |
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 |
QAPM | | | 98.62 95 | 98.40 89 | 98.89 113 | 99.57 105 | 98.80 129 | 98.63 152 | 99.35 117 | 96.82 108 | 98.60 129 | 98.85 110 | 99.08 108 | 98.09 138 | 98.31 101 | 98.21 102 | 99.08 103 | 98.72 86 |
|
EPP-MVSNet | | | 98.61 96 | 98.19 105 | 99.11 88 | 99.86 23 | 99.60 19 | 99.44 64 | 99.53 71 | 97.37 84 | 96.85 211 | 98.69 113 | 93.75 187 | 99.18 69 | 99.22 37 | 99.35 30 | 99.82 14 | 99.32 24 |
|
3Dnovator+ | | 97.85 5 | 98.61 96 | 98.14 107 | 99.15 82 | 99.62 95 | 98.37 162 | 99.10 105 | 99.51 74 | 98.04 34 | 98.98 98 | 96.07 188 | 98.75 127 | 98.55 111 | 98.51 82 | 98.40 96 | 99.17 95 | 98.82 73 |
|
ESAPD | | | 98.60 98 | 98.41 87 | 98.83 120 | 99.56 106 | 99.21 64 | 98.66 151 | 99.47 83 | 95.22 161 | 98.35 150 | 98.48 119 | 99.67 26 | 97.84 149 | 98.80 71 | 98.57 88 | 99.10 98 | 98.93 64 |
|
X-MVS | | | 98.59 99 | 97.99 118 | 99.30 67 | 99.75 47 | 99.07 86 | 99.17 97 | 99.50 77 | 96.62 120 | 98.95 103 | 93.95 207 | 99.37 58 | 99.11 79 | 98.94 60 | 98.86 58 | 99.35 76 | 99.09 43 |
|
MVS_111021_HR | | | 98.58 100 | 98.26 100 | 98.96 107 | 99.32 152 | 98.81 128 | 98.48 166 | 98.99 167 | 96.81 110 | 99.16 72 | 98.07 135 | 99.23 75 | 98.89 90 | 98.43 89 | 98.27 100 | 98.90 127 | 98.24 125 |
|
MVS_0304 | | | 98.57 101 | 98.36 92 | 98.82 123 | 99.72 59 | 98.94 119 | 98.92 122 | 99.14 151 | 96.76 113 | 99.33 43 | 98.30 125 | 99.73 12 | 96.74 171 | 98.05 123 | 97.79 130 | 99.08 103 | 98.97 56 |
|
PM-MVS | | | 98.57 101 | 98.24 102 | 98.95 108 | 99.26 163 | 98.59 147 | 99.03 108 | 98.74 179 | 96.84 105 | 99.44 34 | 99.13 91 | 98.31 143 | 98.75 98 | 98.03 124 | 98.21 102 | 98.48 172 | 98.58 96 |
|
PHI-MVS | | | 98.57 101 | 98.20 104 | 99.00 104 | 99.48 130 | 98.91 122 | 98.68 144 | 99.17 147 | 94.97 169 | 99.27 60 | 98.33 123 | 99.33 64 | 98.05 140 | 98.82 69 | 98.62 83 | 99.34 77 | 98.38 112 |
|
HPM-MVS++ | | | 98.56 104 | 98.08 112 | 99.11 88 | 99.53 118 | 98.61 146 | 99.02 112 | 99.32 125 | 96.29 140 | 99.06 89 | 97.23 158 | 99.50 48 | 98.77 96 | 98.15 116 | 97.90 120 | 98.96 115 | 98.90 68 |
|
TSAR-MVS + GP. | | | 98.54 105 | 98.29 99 | 98.82 123 | 99.28 161 | 98.59 147 | 97.73 204 | 99.24 137 | 95.93 150 | 98.59 130 | 99.07 96 | 99.17 84 | 98.86 91 | 98.44 86 | 98.10 108 | 99.26 89 | 98.72 86 |
|
UGNet | | | 98.52 106 | 99.00 31 | 97.96 181 | 99.58 101 | 99.26 56 | 99.27 84 | 99.40 92 | 98.07 31 | 98.28 156 | 98.76 111 | 99.71 18 | 92.24 224 | 98.94 60 | 98.85 60 | 99.00 113 | 99.43 19 |
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 |
HSP-MVS | | | 98.50 107 | 98.05 114 | 99.03 98 | 99.67 70 | 99.33 49 | 99.51 53 | 99.26 132 | 95.28 160 | 98.51 137 | 98.19 130 | 99.74 11 | 98.29 126 | 97.69 153 | 96.70 180 | 98.96 115 | 99.41 20 |
|
Anonymous20231206 | | | 98.50 107 | 98.03 115 | 99.05 96 | 99.50 126 | 99.01 103 | 99.15 99 | 99.26 132 | 96.38 135 | 99.12 79 | 99.50 67 | 99.12 97 | 98.60 106 | 97.68 154 | 97.24 169 | 98.66 157 | 97.30 169 |
|
CLD-MVS | | | 98.48 109 | 98.15 106 | 98.86 118 | 99.53 118 | 98.35 163 | 98.55 163 | 97.83 215 | 96.02 148 | 98.97 99 | 99.08 94 | 99.75 9 | 99.03 84 | 98.10 122 | 97.33 165 | 99.28 87 | 98.44 108 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
CANet | | | 98.47 110 | 98.30 97 | 98.67 138 | 99.65 80 | 98.87 127 | 98.82 135 | 99.01 165 | 96.14 144 | 99.29 52 | 98.86 108 | 99.01 114 | 96.54 175 | 98.36 95 | 98.08 109 | 98.72 154 | 98.80 79 |
|
APD-MVS | | | 98.47 110 | 97.97 119 | 99.05 96 | 99.64 88 | 98.91 122 | 98.94 119 | 99.45 90 | 94.40 184 | 98.77 120 | 97.26 157 | 99.41 52 | 98.21 133 | 98.67 76 | 98.57 88 | 99.31 82 | 98.57 98 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
Vis-MVSNet (Re-imp) | | | 98.46 112 | 98.23 103 | 98.73 130 | 99.81 29 | 99.29 54 | 98.79 137 | 99.50 77 | 96.20 142 | 96.03 216 | 98.29 126 | 96.98 169 | 98.54 113 | 99.11 41 | 99.08 44 | 99.70 24 | 98.62 94 |
|
Fast-Effi-MVS+ | | | 98.42 113 | 97.79 125 | 99.15 82 | 99.69 66 | 98.66 142 | 98.94 119 | 99.68 38 | 94.49 178 | 99.05 91 | 98.06 137 | 98.86 121 | 98.48 116 | 98.18 112 | 97.78 131 | 99.05 109 | 98.54 102 |
|
MVS_111021_LR | | | 98.39 114 | 98.11 109 | 98.71 133 | 99.08 185 | 98.54 154 | 98.23 186 | 98.56 191 | 96.57 125 | 99.13 77 | 98.41 120 | 98.86 121 | 98.65 104 | 98.23 109 | 97.87 126 | 98.65 159 | 98.28 120 |
|
pmmvs5 | | | 98.37 115 | 97.81 124 | 99.03 98 | 99.46 131 | 98.97 113 | 99.03 108 | 98.96 169 | 95.85 152 | 99.05 91 | 99.45 70 | 98.66 134 | 98.79 95 | 96.02 200 | 97.52 157 | 98.87 132 | 98.21 129 |
|
OMC-MVS | | | 98.35 116 | 98.10 110 | 98.64 142 | 98.85 196 | 97.99 182 | 98.56 162 | 98.21 203 | 97.26 91 | 98.87 117 | 98.54 118 | 99.27 71 | 98.43 118 | 98.34 97 | 97.66 146 | 98.92 122 | 97.65 157 |
|
canonicalmvs | | | 98.34 117 | 97.92 121 | 98.83 120 | 99.45 132 | 99.21 64 | 98.37 175 | 99.53 71 | 97.06 102 | 97.74 182 | 96.95 170 | 95.05 183 | 98.36 122 | 98.77 73 | 98.85 60 | 99.51 49 | 99.53 9 |
|
CHOSEN 1792x2688 | | | 98.31 118 | 98.02 116 | 98.66 140 | 99.55 108 | 98.57 150 | 99.38 69 | 99.25 135 | 98.42 22 | 98.48 143 | 99.58 58 | 99.85 6 | 98.31 125 | 95.75 203 | 95.71 196 | 96.96 203 | 98.27 122 |
|
CPTT-MVS | | | 98.28 119 | 97.51 137 | 99.16 81 | 99.54 111 | 98.78 131 | 98.96 117 | 99.36 112 | 96.30 139 | 98.89 113 | 93.10 212 | 99.30 68 | 99.20 67 | 98.35 96 | 97.96 119 | 99.03 111 | 98.82 73 |
|
TinyColmap | | | 98.27 120 | 97.62 134 | 99.03 98 | 99.29 158 | 97.79 191 | 98.92 122 | 98.95 170 | 97.48 74 | 99.52 27 | 98.65 115 | 97.86 157 | 98.90 88 | 98.34 97 | 97.27 167 | 98.64 160 | 95.97 194 |
|
USDC | | | 98.26 121 | 97.57 135 | 99.06 93 | 99.42 138 | 97.98 184 | 98.83 132 | 98.85 173 | 97.57 71 | 99.59 19 | 99.15 90 | 98.59 136 | 98.99 85 | 97.42 172 | 96.08 195 | 98.69 156 | 96.23 191 |
|
MCST-MVS | | | 98.25 122 | 97.57 135 | 99.06 93 | 99.53 118 | 98.24 170 | 98.63 152 | 99.17 147 | 95.88 151 | 98.58 131 | 96.11 186 | 99.09 106 | 99.18 69 | 97.58 163 | 97.31 166 | 99.25 90 | 98.75 84 |
|
IterMVS-LS | | | 98.23 123 | 97.66 130 | 98.90 111 | 99.63 93 | 99.38 44 | 99.07 106 | 99.48 82 | 97.75 54 | 98.81 119 | 99.37 76 | 94.57 185 | 97.88 146 | 96.54 193 | 97.04 174 | 98.53 168 | 98.97 56 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
TAPA-MVS | | 96.65 12 | 98.23 123 | 97.96 120 | 98.55 149 | 98.81 198 | 98.16 174 | 98.40 172 | 97.94 212 | 96.68 118 | 98.49 141 | 98.61 116 | 98.89 119 | 98.57 109 | 97.45 169 | 97.59 153 | 99.09 102 | 98.35 114 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
CNVR-MVS | | | 98.22 125 | 97.76 126 | 98.76 128 | 99.33 149 | 98.26 168 | 98.48 166 | 98.88 172 | 96.22 141 | 98.47 145 | 95.79 190 | 99.33 64 | 98.35 123 | 98.37 93 | 97.99 114 | 99.03 111 | 98.38 112 |
|
IS_MVSNet | | | 98.20 126 | 98.00 117 | 98.44 153 | 99.82 26 | 99.48 34 | 99.25 87 | 99.56 59 | 95.58 156 | 93.93 233 | 97.56 150 | 96.52 173 | 98.27 128 | 99.08 47 | 99.20 37 | 99.80 16 | 98.56 101 |
|
DeepPCF-MVS | | 96.68 10 | 98.20 126 | 98.26 100 | 98.12 173 | 97.03 236 | 98.11 176 | 98.44 170 | 97.70 216 | 96.77 112 | 98.52 136 | 98.91 106 | 99.17 84 | 98.58 108 | 98.41 91 | 98.02 111 | 98.46 173 | 98.46 105 |
|
MSDG | | | 98.20 126 | 97.88 123 | 98.56 148 | 99.33 149 | 97.74 194 | 98.27 183 | 98.10 206 | 97.20 97 | 98.06 166 | 98.59 117 | 99.16 87 | 98.76 97 | 98.39 92 | 97.71 144 | 98.86 135 | 96.38 188 |
|
testgi | | | 98.18 129 | 98.44 84 | 97.89 182 | 99.78 36 | 99.23 60 | 98.78 138 | 99.21 140 | 97.26 91 | 97.41 193 | 97.39 155 | 99.36 62 | 92.85 220 | 98.82 69 | 98.66 81 | 99.31 82 | 98.35 114 |
|
Effi-MVS+ | | | 98.11 130 | 97.29 142 | 99.06 93 | 99.62 95 | 98.55 151 | 98.16 188 | 99.80 15 | 94.64 175 | 99.15 75 | 96.59 176 | 97.43 162 | 98.44 117 | 97.46 168 | 97.90 120 | 99.17 95 | 98.45 107 |
|
HyFIR lowres test | | | 98.08 131 | 97.16 150 | 99.14 85 | 99.72 59 | 98.91 122 | 99.41 65 | 99.58 56 | 97.93 40 | 98.82 118 | 99.24 81 | 95.81 180 | 98.73 100 | 95.16 212 | 95.13 205 | 98.60 163 | 97.94 148 |
|
train_agg | | | 97.99 132 | 97.26 143 | 98.83 120 | 99.43 137 | 98.22 172 | 98.91 124 | 99.07 159 | 94.43 182 | 97.96 173 | 96.42 181 | 99.30 68 | 98.81 94 | 97.39 173 | 96.62 183 | 98.82 140 | 98.47 104 |
|
MSLP-MVS++ | | | 97.99 132 | 97.64 133 | 98.40 156 | 98.91 194 | 98.47 158 | 97.12 223 | 98.78 177 | 96.49 130 | 98.48 143 | 93.57 210 | 99.12 97 | 98.51 115 | 98.31 101 | 98.58 86 | 98.58 165 | 98.95 62 |
|
CDPH-MVS | | | 97.99 132 | 97.23 146 | 98.87 115 | 99.58 101 | 98.29 164 | 98.83 132 | 99.20 143 | 93.76 196 | 98.11 164 | 96.11 186 | 99.16 87 | 98.23 132 | 97.80 145 | 97.22 170 | 99.29 86 | 98.28 120 |
|
FMVSNet2 | | | 97.94 135 | 98.08 112 | 97.77 187 | 98.71 201 | 99.21 64 | 98.62 154 | 99.47 83 | 96.62 120 | 96.37 215 | 99.20 87 | 97.70 159 | 94.39 206 | 97.39 173 | 97.75 139 | 99.08 103 | 98.70 88 |
|
PVSNet_BlendedMVS | | | 97.93 136 | 97.66 130 | 98.25 164 | 99.30 155 | 98.67 140 | 98.31 180 | 97.95 210 | 94.30 187 | 98.75 122 | 97.63 147 | 98.76 125 | 96.30 182 | 98.29 103 | 97.78 131 | 98.93 119 | 98.18 133 |
|
PVSNet_Blended | | | 97.93 136 | 97.66 130 | 98.25 164 | 99.30 155 | 98.67 140 | 98.31 180 | 97.95 210 | 94.30 187 | 98.75 122 | 97.63 147 | 98.76 125 | 96.30 182 | 98.29 103 | 97.78 131 | 98.93 119 | 98.18 133 |
|
OpenMVS | | 97.26 9 | 97.88 138 | 97.17 149 | 98.70 134 | 99.50 126 | 98.55 151 | 98.34 179 | 99.11 156 | 93.92 194 | 98.90 110 | 95.04 197 | 98.23 144 | 97.38 162 | 98.11 121 | 98.12 107 | 98.95 117 | 98.23 126 |
|
pmmvs4 | | | 97.87 139 | 97.02 154 | 98.86 118 | 99.20 170 | 97.68 197 | 98.89 128 | 99.03 163 | 96.57 125 | 99.12 79 | 99.03 100 | 97.26 166 | 98.42 119 | 95.16 212 | 96.34 187 | 98.53 168 | 97.10 179 |
|
NCCC | | | 97.84 140 | 96.96 156 | 98.87 115 | 99.39 140 | 98.27 167 | 98.46 168 | 99.02 164 | 96.78 111 | 98.73 125 | 91.12 218 | 98.91 118 | 98.57 109 | 97.83 143 | 97.49 159 | 99.04 110 | 98.33 119 |
|
Effi-MVS+-dtu | | | 97.78 141 | 97.37 140 | 98.26 163 | 99.25 166 | 98.50 156 | 97.89 198 | 99.19 144 | 94.51 177 | 98.16 161 | 95.93 189 | 98.80 124 | 95.97 186 | 98.27 108 | 97.38 162 | 99.10 98 | 98.23 126 |
|
MDA-MVSNet-bldmvs | | | 97.75 142 | 97.26 143 | 98.33 160 | 99.35 148 | 98.45 159 | 99.32 78 | 97.21 221 | 97.90 45 | 99.05 91 | 99.01 102 | 96.86 171 | 99.08 80 | 99.36 31 | 92.97 215 | 95.97 211 | 96.25 190 |
|
CDS-MVSNet | | | 97.75 142 | 97.68 129 | 97.83 185 | 99.08 185 | 98.20 173 | 98.68 144 | 98.61 188 | 95.63 155 | 97.80 177 | 99.24 81 | 96.93 170 | 94.09 211 | 97.96 127 | 97.82 129 | 98.71 155 | 97.99 145 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
CNLPA | | | 97.75 142 | 97.26 143 | 98.32 162 | 98.58 210 | 97.86 187 | 97.80 200 | 98.09 207 | 96.49 130 | 98.49 141 | 96.15 185 | 98.08 149 | 98.35 123 | 98.00 125 | 97.03 175 | 98.61 162 | 97.21 176 |
|
PLC | | 95.63 15 | 97.73 145 | 97.01 155 | 98.57 147 | 99.10 182 | 97.80 190 | 97.72 205 | 98.77 178 | 96.34 136 | 98.38 148 | 93.46 211 | 98.06 150 | 98.66 103 | 97.90 133 | 97.65 148 | 98.77 151 | 97.90 149 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MVS_Test | | | 97.69 146 | 97.15 151 | 98.33 160 | 99.27 162 | 98.43 161 | 98.25 184 | 99.29 128 | 95.00 168 | 97.39 196 | 98.86 108 | 98.00 153 | 97.14 166 | 95.38 208 | 96.22 189 | 98.62 161 | 98.15 137 |
|
GBi-Net | | | 97.69 146 | 97.75 127 | 97.62 188 | 98.71 201 | 99.21 64 | 98.62 154 | 99.33 120 | 94.09 190 | 95.60 222 | 98.17 132 | 95.97 177 | 94.39 206 | 99.05 49 | 99.03 50 | 99.08 103 | 98.70 88 |
|
test1 | | | 97.69 146 | 97.75 127 | 97.62 188 | 98.71 201 | 99.21 64 | 98.62 154 | 99.33 120 | 94.09 190 | 95.60 222 | 98.17 132 | 95.97 177 | 94.39 206 | 99.05 49 | 99.03 50 | 99.08 103 | 98.70 88 |
|
CANet_DTU | | | 97.65 149 | 97.50 138 | 97.82 186 | 99.19 173 | 98.08 177 | 98.41 171 | 98.67 184 | 94.40 184 | 99.16 72 | 98.32 124 | 98.69 129 | 93.96 213 | 97.87 136 | 97.61 151 | 97.51 194 | 97.56 161 |
|
TSAR-MVS + COLMAP | | | 97.62 150 | 97.31 141 | 97.98 179 | 98.47 216 | 97.39 201 | 98.29 182 | 98.25 201 | 96.68 118 | 97.54 190 | 98.87 107 | 98.04 152 | 97.08 167 | 96.78 188 | 96.26 188 | 98.26 179 | 97.12 178 |
|
MS-PatchMatch | | | 97.60 151 | 97.22 147 | 98.04 177 | 98.67 206 | 97.18 203 | 97.91 196 | 98.28 200 | 95.82 153 | 98.34 151 | 97.66 146 | 98.38 140 | 97.77 150 | 97.10 184 | 97.25 168 | 97.27 198 | 97.18 177 |
|
PCF-MVS | | 95.58 16 | 97.60 151 | 96.67 160 | 98.69 136 | 99.44 135 | 98.23 171 | 98.37 175 | 98.81 176 | 93.01 206 | 98.22 158 | 97.97 141 | 99.59 39 | 98.20 134 | 95.72 205 | 95.08 206 | 99.08 103 | 97.09 181 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
tfpn_n400 | | | 97.59 153 | 96.36 171 | 99.01 102 | 99.66 74 | 99.19 69 | 99.21 93 | 99.55 61 | 97.62 64 | 97.77 178 | 94.60 201 | 87.78 200 | 98.27 128 | 98.44 86 | 98.72 75 | 99.62 31 | 98.21 129 |
|
tfpnconf | | | 97.59 153 | 96.36 171 | 99.01 102 | 99.66 74 | 99.19 69 | 99.21 93 | 99.55 61 | 97.62 64 | 97.77 178 | 94.60 201 | 87.78 200 | 98.27 128 | 98.44 86 | 98.72 75 | 99.62 31 | 98.21 129 |
|
HQP-MVS | | | 97.58 155 | 96.65 164 | 98.66 140 | 99.30 155 | 97.99 182 | 97.88 199 | 98.65 185 | 94.58 176 | 98.66 126 | 94.65 200 | 99.15 91 | 98.59 107 | 96.10 198 | 95.59 198 | 98.90 127 | 98.50 103 |
|
DI_MVS_plusplus_trai | | | 97.57 156 | 96.55 166 | 98.77 127 | 99.55 108 | 98.76 132 | 99.22 91 | 99.00 166 | 97.08 101 | 97.95 174 | 97.78 144 | 91.35 194 | 98.02 141 | 96.20 196 | 96.81 179 | 98.87 132 | 97.87 150 |
|
AdaColmap | | | 97.57 156 | 96.57 165 | 98.74 129 | 99.25 166 | 98.01 180 | 98.36 178 | 98.98 168 | 94.44 181 | 98.47 145 | 92.44 216 | 97.91 156 | 98.62 105 | 98.19 111 | 97.74 140 | 98.73 153 | 97.28 170 |
|
tfpnview11 | | | 97.49 158 | 96.22 175 | 98.97 106 | 99.63 93 | 99.24 58 | 99.12 103 | 99.54 67 | 96.76 113 | 97.77 178 | 94.60 201 | 87.78 200 | 98.25 131 | 97.93 130 | 99.14 40 | 99.52 44 | 98.08 141 |
|
test1235678 | | | 97.49 158 | 96.84 158 | 98.24 167 | 99.37 141 | 97.79 191 | 98.59 159 | 99.07 159 | 92.41 208 | 97.59 186 | 99.24 81 | 98.15 147 | 97.66 151 | 97.64 158 | 97.12 171 | 97.17 199 | 95.55 198 |
|
testmv | | | 97.48 160 | 96.83 159 | 98.24 167 | 99.37 141 | 97.79 191 | 98.59 159 | 99.07 159 | 92.40 209 | 97.59 186 | 99.24 81 | 98.11 148 | 97.66 151 | 97.64 158 | 97.11 172 | 97.17 199 | 95.54 199 |
|
conf0.05thres1000 | | | 97.44 161 | 95.93 182 | 99.20 78 | 99.82 26 | 99.56 23 | 99.41 65 | 99.61 53 | 97.42 80 | 98.01 171 | 94.34 206 | 82.73 221 | 98.68 102 | 99.33 33 | 99.42 25 | 99.67 28 | 98.74 85 |
|
IterMVS | | | 97.40 162 | 96.67 160 | 98.25 164 | 99.45 132 | 98.66 142 | 98.87 130 | 98.73 180 | 96.40 134 | 98.94 106 | 99.56 60 | 95.26 182 | 97.58 153 | 95.38 208 | 94.70 209 | 95.90 212 | 96.72 184 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
CVMVSNet | | | 97.38 163 | 97.39 139 | 97.37 193 | 98.58 210 | 97.72 195 | 98.70 142 | 97.42 218 | 97.21 95 | 95.95 219 | 99.46 69 | 93.31 190 | 97.38 162 | 97.60 161 | 97.78 131 | 96.18 208 | 98.66 92 |
|
diffmvs | | | 97.29 164 | 96.67 160 | 98.01 178 | 99.00 190 | 97.82 188 | 98.37 175 | 99.18 145 | 96.73 117 | 97.74 182 | 99.08 94 | 94.26 186 | 96.50 176 | 94.86 216 | 95.67 197 | 97.29 197 | 98.25 124 |
|
new-patchmatchnet | | | 97.26 165 | 96.12 177 | 98.58 146 | 99.55 108 | 98.63 144 | 99.14 100 | 97.04 223 | 98.80 16 | 99.19 66 | 99.92 6 | 99.19 82 | 98.92 87 | 95.51 207 | 87.04 223 | 97.66 191 | 93.73 212 |
|
MIMVSNet | | | 97.24 166 | 97.15 151 | 97.36 194 | 99.03 188 | 98.52 155 | 98.55 163 | 99.73 29 | 94.94 171 | 94.94 230 | 97.98 140 | 97.37 164 | 93.66 215 | 97.60 161 | 97.34 164 | 98.23 181 | 96.29 189 |
|
PatchMatch-RL | | | 97.24 166 | 96.45 169 | 98.17 170 | 98.70 204 | 97.57 199 | 97.31 219 | 98.48 195 | 94.42 183 | 98.39 147 | 95.74 191 | 96.35 176 | 97.88 146 | 97.75 150 | 97.48 160 | 98.24 180 | 95.87 195 |
|
MDTV_nov1_ep13_2view | | | 97.12 168 | 96.19 176 | 98.22 169 | 99.13 180 | 98.05 178 | 99.24 88 | 99.47 83 | 97.61 66 | 99.15 75 | 99.59 56 | 99.01 114 | 98.40 120 | 94.87 214 | 90.14 218 | 93.91 219 | 94.04 211 |
|
MAR-MVS | | | 97.12 168 | 96.28 174 | 98.11 174 | 98.94 193 | 97.22 202 | 97.65 209 | 99.38 101 | 90.93 230 | 98.15 162 | 95.17 195 | 97.13 167 | 96.48 178 | 97.71 152 | 97.40 161 | 98.06 185 | 98.40 111 |
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 |
tfpn1000 | | | 97.10 170 | 95.97 180 | 98.41 155 | 99.64 88 | 99.30 53 | 98.89 128 | 99.49 81 | 96.49 130 | 95.97 218 | 95.31 194 | 85.62 214 | 96.92 170 | 97.86 137 | 99.13 42 | 99.53 43 | 98.11 138 |
|
Fast-Effi-MVS+-dtu | | | 96.99 171 | 96.46 168 | 97.61 190 | 98.98 191 | 97.89 185 | 97.54 214 | 99.76 23 | 93.43 200 | 96.55 214 | 94.93 198 | 98.06 150 | 94.32 209 | 96.93 186 | 96.50 186 | 98.53 168 | 97.47 162 |
|
FPMVS | | | 96.97 172 | 97.20 148 | 96.70 212 | 97.75 229 | 96.11 217 | 97.72 205 | 95.47 227 | 97.13 99 | 98.02 168 | 97.57 149 | 96.67 172 | 92.97 219 | 99.00 57 | 98.34 98 | 98.28 178 | 95.58 197 |
|
TAMVS | | | 96.95 173 | 96.94 157 | 96.97 206 | 99.07 187 | 97.67 198 | 97.98 194 | 97.12 222 | 95.04 165 | 95.41 225 | 99.27 80 | 95.57 181 | 94.09 211 | 97.32 177 | 97.11 172 | 98.16 184 | 96.59 186 |
|
FMVSNet3 | | | 96.85 174 | 96.67 160 | 97.06 200 | 97.56 232 | 99.01 103 | 97.99 193 | 99.33 120 | 94.09 190 | 95.60 222 | 98.17 132 | 95.97 177 | 93.26 218 | 94.76 217 | 96.22 189 | 98.59 164 | 98.46 105 |
|
GA-MVS | | | 96.84 175 | 95.86 184 | 97.98 179 | 99.16 177 | 98.29 164 | 97.91 196 | 98.64 187 | 95.14 163 | 97.71 184 | 98.04 139 | 88.90 197 | 96.50 176 | 96.41 194 | 96.61 184 | 97.97 188 | 97.60 158 |
|
CHOSEN 280x420 | | | 96.80 176 | 96.30 173 | 97.39 192 | 99.09 183 | 96.52 208 | 98.76 140 | 99.29 128 | 93.88 195 | 97.65 185 | 98.34 122 | 93.66 188 | 96.29 184 | 98.28 106 | 97.73 142 | 93.27 223 | 95.70 196 |
|
gg-mvs-nofinetune | | | 96.77 177 | 96.52 167 | 97.06 200 | 99.66 74 | 97.82 188 | 97.54 214 | 99.86 9 | 98.69 17 | 98.61 128 | 99.94 4 | 89.62 195 | 88.37 234 | 97.55 164 | 96.67 182 | 98.30 177 | 95.35 200 |
|
tfpn_ndepth | | | 96.69 178 | 95.49 189 | 98.09 175 | 99.17 175 | 99.13 79 | 98.61 157 | 99.38 101 | 94.90 172 | 95.85 220 | 92.85 214 | 88.19 199 | 96.07 185 | 97.28 180 | 98.67 79 | 99.49 52 | 97.44 163 |
|
N_pmnet | | | 96.68 179 | 95.70 187 | 97.84 184 | 99.42 138 | 98.00 181 | 99.35 73 | 98.21 203 | 98.40 24 | 98.13 163 | 99.42 73 | 99.30 68 | 97.44 161 | 94.00 222 | 88.79 220 | 94.47 218 | 91.96 220 |
|
new_pmnet | | | 96.59 180 | 96.40 170 | 96.81 209 | 98.24 225 | 95.46 227 | 97.71 207 | 94.75 231 | 96.92 103 | 96.80 213 | 99.23 85 | 97.81 158 | 96.69 172 | 96.58 192 | 95.16 204 | 96.69 204 | 93.64 213 |
|
tfpn111 | | | 96.48 181 | 94.67 192 | 98.59 144 | 99.37 141 | 99.18 71 | 98.68 144 | 99.39 94 | 92.02 215 | 97.21 205 | 90.63 219 | 86.34 208 | 97.45 156 | 98.15 116 | 99.08 44 | 99.43 59 | 97.28 170 |
|
view800 | | | 96.48 181 | 94.42 193 | 98.87 115 | 99.70 63 | 99.26 56 | 99.05 107 | 99.45 90 | 94.77 173 | 97.32 200 | 88.21 223 | 83.40 219 | 98.28 127 | 98.37 93 | 99.33 31 | 99.44 57 | 97.58 160 |
|
PMMVS | | | 96.47 183 | 95.81 185 | 97.23 196 | 97.38 234 | 95.96 221 | 97.31 219 | 96.91 224 | 93.21 203 | 97.93 175 | 97.14 161 | 97.64 160 | 95.70 189 | 95.24 210 | 96.18 192 | 98.17 183 | 95.33 201 |
|
EPNet | | | 96.44 184 | 96.08 178 | 96.86 207 | 99.32 152 | 97.15 204 | 97.69 208 | 99.32 125 | 93.67 197 | 98.11 164 | 95.64 192 | 93.44 189 | 89.07 232 | 96.86 187 | 96.83 178 | 97.67 190 | 98.97 56 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
view600 | | | 96.39 185 | 94.30 194 | 98.82 123 | 99.65 80 | 99.16 76 | 98.98 114 | 99.36 112 | 94.46 180 | 97.39 196 | 87.28 224 | 84.16 217 | 98.16 135 | 98.16 113 | 99.48 21 | 99.40 67 | 97.42 165 |
|
thres600view7 | | | 96.35 186 | 94.27 195 | 98.79 126 | 99.66 74 | 99.18 71 | 98.94 119 | 99.38 101 | 94.37 186 | 97.21 205 | 87.19 226 | 84.10 218 | 98.10 136 | 98.16 113 | 99.47 22 | 99.42 62 | 97.43 164 |
|
EPNet_dtu | | | 96.31 187 | 95.96 181 | 96.72 211 | 99.18 174 | 95.39 228 | 97.03 225 | 99.13 155 | 93.02 205 | 99.35 41 | 97.23 158 | 97.07 168 | 90.70 229 | 95.74 204 | 95.08 206 | 94.94 215 | 98.16 136 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
pmmvs3 | | | 96.30 188 | 95.87 183 | 96.80 210 | 97.66 231 | 96.48 209 | 97.93 195 | 93.80 232 | 93.40 201 | 98.54 135 | 98.27 127 | 97.50 161 | 97.37 164 | 97.49 167 | 93.11 214 | 95.52 213 | 94.85 205 |
|
PMMVS2 | | | 96.29 189 | 97.05 153 | 95.40 226 | 98.32 222 | 96.16 214 | 98.18 187 | 97.46 217 | 97.20 97 | 84.51 240 | 99.60 53 | 98.68 131 | 96.37 179 | 98.59 79 | 97.38 162 | 97.58 193 | 91.76 222 |
|
thres200 | | | 96.23 190 | 94.13 196 | 98.69 136 | 99.44 135 | 99.18 71 | 98.58 161 | 99.38 101 | 93.52 199 | 97.35 198 | 86.33 233 | 85.83 213 | 97.93 144 | 98.16 113 | 98.78 68 | 99.42 62 | 97.10 179 |
|
thres400 | | | 96.22 191 | 94.08 198 | 98.72 131 | 99.58 101 | 99.05 90 | 98.83 132 | 99.22 138 | 94.01 193 | 97.40 194 | 86.34 232 | 84.91 216 | 97.93 144 | 97.85 140 | 99.08 44 | 99.37 72 | 97.28 170 |
|
tfpn200view9 | | | 96.17 192 | 94.08 198 | 98.60 143 | 99.37 141 | 99.18 71 | 98.68 144 | 99.39 94 | 92.02 215 | 97.30 201 | 86.53 229 | 86.34 208 | 97.45 156 | 98.15 116 | 99.08 44 | 99.43 59 | 97.28 170 |
|
CMPMVS | | 74.71 19 | 96.17 192 | 96.06 179 | 96.30 219 | 97.41 233 | 94.52 233 | 94.83 235 | 95.46 228 | 91.57 224 | 97.26 204 | 94.45 205 | 98.33 142 | 94.98 198 | 98.28 106 | 97.59 153 | 97.86 189 | 97.68 156 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
conf200view11 | | | 96.16 194 | 94.08 198 | 98.59 144 | 99.37 141 | 99.18 71 | 98.68 144 | 99.39 94 | 92.02 215 | 97.21 205 | 86.53 229 | 86.34 208 | 97.45 156 | 98.15 116 | 99.08 44 | 99.43 59 | 97.28 170 |
|
testus | | | 96.13 195 | 95.13 190 | 97.28 195 | 99.13 180 | 97.00 205 | 96.84 227 | 97.89 214 | 90.48 231 | 97.40 194 | 93.60 209 | 96.47 174 | 95.39 193 | 96.21 195 | 96.19 191 | 97.05 201 | 95.99 193 |
|
IB-MVS | | 95.85 14 | 95.87 196 | 94.88 191 | 97.02 203 | 99.09 183 | 98.25 169 | 97.16 221 | 97.38 219 | 91.97 222 | 97.77 178 | 83.61 237 | 97.29 165 | 92.03 227 | 97.16 182 | 97.66 146 | 98.66 157 | 98.20 132 |
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 |
test0.0.03 1 | | | 95.81 197 | 95.77 186 | 95.85 225 | 99.20 170 | 98.15 175 | 97.49 218 | 98.50 193 | 92.24 210 | 92.74 238 | 96.82 172 | 92.70 191 | 88.60 233 | 97.31 179 | 97.01 177 | 98.57 166 | 96.19 192 |
|
thres100view900 | | | 95.74 198 | 93.66 206 | 98.17 170 | 99.37 141 | 98.59 147 | 98.10 189 | 98.33 199 | 92.02 215 | 97.30 201 | 86.53 229 | 86.34 208 | 96.69 172 | 96.77 189 | 98.47 93 | 99.24 92 | 96.89 182 |
|
test12356 | | | 95.71 199 | 95.55 188 | 95.89 224 | 98.27 224 | 96.48 209 | 96.90 226 | 97.35 220 | 92.13 213 | 95.64 221 | 99.13 91 | 97.97 154 | 92.34 223 | 96.94 185 | 96.55 185 | 94.87 216 | 89.61 229 |
|
thresconf0.02 | | | 95.49 200 | 92.74 211 | 98.70 134 | 99.32 152 | 98.70 138 | 98.87 130 | 99.21 140 | 95.95 149 | 97.57 188 | 90.63 219 | 73.55 233 | 97.86 148 | 96.09 199 | 97.03 175 | 99.40 67 | 97.22 175 |
|
PatchT | | | 95.49 200 | 93.29 208 | 98.06 176 | 98.65 207 | 96.20 213 | 98.91 124 | 99.73 29 | 92.00 221 | 98.50 138 | 96.67 174 | 83.25 220 | 96.34 180 | 94.40 218 | 95.50 199 | 96.21 207 | 95.04 203 |
|
CR-MVSNet | | | 95.38 202 | 93.01 209 | 98.16 172 | 98.63 208 | 95.85 223 | 97.64 210 | 99.78 19 | 91.27 226 | 98.50 138 | 96.84 171 | 82.16 222 | 96.34 180 | 94.40 218 | 95.50 199 | 98.05 186 | 95.04 203 |
|
MVSTER | | | 95.38 202 | 93.99 202 | 97.01 204 | 98.83 197 | 98.95 115 | 96.62 228 | 99.14 151 | 92.17 212 | 97.44 192 | 97.29 156 | 77.88 228 | 91.63 228 | 97.45 169 | 96.18 192 | 98.41 174 | 97.99 145 |
|
LP | | | 95.33 204 | 93.45 207 | 97.54 191 | 98.68 205 | 97.40 200 | 98.73 141 | 98.41 197 | 96.33 137 | 98.92 108 | 97.84 143 | 88.30 198 | 95.92 187 | 92.98 223 | 89.38 219 | 94.56 217 | 91.90 221 |
|
tfpn | | | 94.97 205 | 91.60 217 | 98.90 111 | 99.73 56 | 99.33 49 | 99.11 104 | 99.51 74 | 95.05 164 | 97.19 208 | 89.03 222 | 62.62 239 | 98.37 121 | 98.53 81 | 98.97 54 | 99.48 53 | 97.70 155 |
|
MVS-HIRNet | | | 94.86 206 | 93.83 203 | 96.07 220 | 97.07 235 | 94.00 234 | 94.31 236 | 99.17 147 | 91.23 229 | 98.17 160 | 98.69 113 | 97.43 162 | 95.66 190 | 94.05 221 | 91.92 216 | 92.04 230 | 89.46 230 |
|
test-LLR | | | 94.79 207 | 93.71 204 | 96.06 221 | 99.20 170 | 96.16 214 | 96.31 229 | 98.50 193 | 89.98 232 | 94.08 231 | 97.01 165 | 86.43 206 | 92.20 225 | 96.76 190 | 95.31 201 | 96.05 209 | 94.31 208 |
|
RPMNet | | | 94.72 208 | 92.01 216 | 97.88 183 | 98.56 212 | 95.85 223 | 97.78 201 | 99.70 35 | 91.27 226 | 98.33 152 | 93.69 208 | 81.88 223 | 94.91 200 | 92.60 225 | 94.34 211 | 98.01 187 | 94.46 207 |
|
gm-plane-assit | | | 94.62 209 | 91.39 218 | 98.39 157 | 99.90 13 | 99.47 36 | 99.40 67 | 99.65 44 | 97.44 78 | 99.56 23 | 99.68 44 | 59.40 242 | 94.23 210 | 96.17 197 | 94.77 208 | 97.61 192 | 92.79 217 |
|
test-mter | | | 94.62 209 | 94.02 201 | 95.32 227 | 97.72 230 | 96.75 206 | 96.23 231 | 95.67 226 | 89.83 235 | 93.23 237 | 96.99 167 | 85.94 212 | 92.66 222 | 97.32 177 | 96.11 194 | 96.44 205 | 95.22 202 |
|
FMVSNet5 | | | 94.57 211 | 92.77 210 | 96.67 213 | 97.88 227 | 98.72 137 | 97.54 214 | 98.70 183 | 88.64 236 | 95.11 228 | 86.90 227 | 81.77 224 | 93.27 217 | 97.92 132 | 98.07 110 | 97.50 195 | 97.34 168 |
|
conf0.01 | | | 94.53 212 | 91.09 220 | 98.53 151 | 99.29 158 | 99.05 90 | 98.68 144 | 99.35 117 | 92.02 215 | 97.04 209 | 84.45 235 | 68.52 235 | 97.45 156 | 97.79 147 | 99.08 44 | 99.41 65 | 96.70 185 |
|
MDTV_nov1_ep13 | | | 94.47 213 | 92.15 214 | 97.17 197 | 98.54 214 | 96.42 211 | 98.10 189 | 98.89 171 | 94.49 178 | 98.02 168 | 97.41 154 | 86.49 205 | 95.56 191 | 90.85 226 | 87.95 221 | 93.91 219 | 91.45 224 |
|
TESTMET0.1,1 | | | 94.44 214 | 93.71 204 | 95.30 228 | 97.84 228 | 96.16 214 | 96.31 229 | 95.32 229 | 89.98 232 | 94.08 231 | 97.01 165 | 86.43 206 | 92.20 225 | 96.76 190 | 95.31 201 | 96.05 209 | 94.31 208 |
|
ADS-MVSNet | | | 94.41 215 | 92.13 215 | 97.07 199 | 98.86 195 | 96.60 207 | 98.38 174 | 98.47 196 | 96.13 146 | 98.02 168 | 96.98 168 | 87.50 204 | 95.87 188 | 89.89 227 | 87.58 222 | 92.79 227 | 90.27 226 |
|
1111 | | | 94.22 216 | 92.26 213 | 96.51 217 | 99.71 61 | 98.75 134 | 99.03 108 | 99.83 12 | 95.01 166 | 93.39 235 | 99.54 64 | 60.23 240 | 89.58 230 | 97.90 133 | 97.62 150 | 97.50 195 | 96.75 183 |
|
conf0.002 | | | 93.97 217 | 90.06 224 | 98.52 152 | 99.26 163 | 99.02 102 | 98.68 144 | 99.33 120 | 92.02 215 | 97.01 210 | 83.82 236 | 63.41 238 | 97.45 156 | 97.73 151 | 97.98 116 | 99.40 67 | 96.47 187 |
|
tpm | | | 93.89 218 | 91.21 219 | 97.03 202 | 98.36 220 | 96.07 218 | 97.53 217 | 99.65 44 | 92.24 210 | 98.64 127 | 97.23 158 | 74.67 232 | 94.64 204 | 92.68 224 | 90.73 217 | 93.37 222 | 94.82 206 |
|
PatchmatchNet | | | 93.88 219 | 91.08 221 | 97.14 198 | 98.75 200 | 96.01 220 | 98.25 184 | 99.39 94 | 94.95 170 | 98.96 101 | 96.32 182 | 85.35 215 | 95.50 192 | 88.89 229 | 85.89 227 | 91.99 231 | 90.15 227 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
EPMVS | | | 93.67 220 | 90.82 222 | 96.99 205 | 98.62 209 | 96.39 212 | 98.40 172 | 99.11 156 | 95.54 157 | 97.87 176 | 97.14 161 | 81.27 226 | 94.97 199 | 88.54 231 | 86.80 224 | 92.95 225 | 90.06 228 |
|
MVE | | 82.47 18 | 93.12 221 | 94.09 197 | 91.99 232 | 90.79 238 | 82.50 240 | 93.93 237 | 96.30 225 | 96.06 147 | 88.81 239 | 98.19 130 | 96.38 175 | 97.56 154 | 97.24 181 | 95.18 203 | 84.58 237 | 93.07 214 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
CostFormer | | | 92.75 222 | 89.49 225 | 96.55 215 | 98.78 199 | 95.83 225 | 97.55 213 | 98.59 189 | 91.83 223 | 97.34 199 | 96.31 183 | 78.53 227 | 94.50 205 | 86.14 232 | 84.92 228 | 92.54 228 | 92.84 216 |
|
test2356 | | | 92.46 223 | 88.72 230 | 96.82 208 | 98.48 215 | 95.34 229 | 96.22 232 | 98.09 207 | 87.46 237 | 96.01 217 | 92.82 215 | 64.42 236 | 95.10 197 | 94.08 220 | 94.05 212 | 97.02 202 | 92.87 215 |
|
tpmrst | | | 92.45 224 | 89.48 226 | 95.92 223 | 98.43 219 | 95.03 231 | 97.14 222 | 97.92 213 | 94.16 189 | 97.56 189 | 97.86 142 | 81.63 225 | 93.56 216 | 85.89 234 | 82.86 230 | 90.91 235 | 88.95 233 |
|
tpmp4_e23 | | | 92.43 225 | 88.82 228 | 96.64 214 | 98.46 217 | 95.17 230 | 97.61 212 | 98.85 173 | 92.42 207 | 98.18 159 | 93.03 213 | 74.92 231 | 93.80 214 | 88.91 228 | 84.60 229 | 92.95 225 | 92.66 218 |
|
dps | | | 92.35 226 | 88.78 229 | 96.52 216 | 98.21 226 | 95.94 222 | 97.78 201 | 98.38 198 | 89.88 234 | 96.81 212 | 95.07 196 | 75.31 230 | 94.70 203 | 88.62 230 | 86.21 226 | 93.21 224 | 90.41 225 |
|
E-PMN | | | 92.28 227 | 90.12 223 | 94.79 229 | 98.56 212 | 90.90 236 | 95.16 234 | 93.68 233 | 95.36 159 | 95.10 229 | 96.56 177 | 89.05 196 | 95.24 195 | 95.21 211 | 81.84 233 | 90.98 233 | 81.94 234 |
|
EMVS | | | 91.84 228 | 89.39 227 | 94.70 230 | 98.44 218 | 90.84 237 | 95.27 233 | 93.53 234 | 95.18 162 | 95.26 227 | 95.62 193 | 87.59 203 | 94.77 202 | 94.87 214 | 80.72 234 | 90.95 234 | 80.88 235 |
|
tpm cat1 | | | 91.52 229 | 87.70 231 | 95.97 222 | 98.33 221 | 94.98 232 | 97.06 224 | 98.03 209 | 92.11 214 | 98.03 167 | 94.77 199 | 77.19 229 | 92.71 221 | 83.56 235 | 82.24 232 | 91.67 232 | 89.04 232 |
|
DWT-MVSNet_training | | | 91.07 230 | 86.55 232 | 96.35 218 | 98.28 223 | 95.82 226 | 98.00 192 | 95.03 230 | 91.24 228 | 97.99 172 | 90.35 221 | 63.43 237 | 95.25 194 | 86.06 233 | 86.62 225 | 93.55 221 | 92.30 219 |
|
testpf | | | 87.81 231 | 83.90 233 | 92.37 231 | 96.76 237 | 88.65 238 | 93.04 238 | 98.24 202 | 85.20 238 | 95.28 226 | 86.82 228 | 72.43 234 | 82.35 235 | 82.62 236 | 82.30 231 | 88.55 236 | 89.29 231 |
|
.test1245 | | | 74.10 232 | 68.09 234 | 81.11 233 | 99.71 61 | 98.75 134 | 99.03 108 | 99.83 12 | 95.01 166 | 93.39 235 | 99.54 64 | 60.23 240 | 89.58 230 | 97.90 133 | 10.38 236 | 5.14 240 | 14.81 236 |
|
GG-mvs-BLEND | | | 65.66 233 | 92.62 212 | 34.20 235 | 1.45 242 | 93.75 235 | 85.40 240 | 1.64 239 | 91.37 225 | 17.21 242 | 87.25 225 | 94.78 184 | 3.25 239 | 95.64 206 | 93.80 213 | 96.27 206 | 91.74 223 |
|
testmvs | | | 9.73 234 | 13.38 235 | 5.48 237 | 3.62 240 | 4.12 242 | 6.40 243 | 3.19 238 | 14.92 239 | 7.68 244 | 22.10 238 | 13.89 244 | 6.83 237 | 13.47 237 | 10.38 236 | 5.14 240 | 14.81 236 |
|
test123 | | | 9.37 235 | 12.26 236 | 6.00 236 | 3.32 241 | 4.06 243 | 6.39 244 | 3.41 237 | 13.20 240 | 10.48 243 | 16.43 239 | 16.22 243 | 6.76 238 | 11.37 238 | 10.40 235 | 5.62 239 | 14.10 238 |
|
sosnet-low-res | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
sosnet | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
our_test_3 | | | | | | 99.29 158 | 97.72 195 | 98.98 114 | | | | | | | | | | |
|
ambc | | | | 97.89 122 | | 99.45 132 | 97.88 186 | 97.78 201 | | 97.27 89 | 99.80 3 | 98.99 104 | 98.48 139 | 98.55 111 | 97.80 145 | 96.68 181 | 98.54 167 | 98.10 139 |
|
MTAPA | | | | | | | | | | | 99.19 66 | | 99.68 22 | | | | | |
|
MTMP | | | | | | | | | | | 99.20 64 | | 99.54 42 | | | | | |
|
Patchmatch-RL test | | | | | | | | 32.47 242 | | | | | | | | | | |
|
tmp_tt | | | | | 65.28 234 | 82.24 239 | 71.50 241 | 70.81 241 | 23.21 236 | 96.14 144 | 81.70 241 | 85.98 234 | 92.44 192 | 49.84 236 | 95.81 202 | 94.36 210 | 83.86 238 | |
|
XVS | | | | | | 99.77 38 | 99.07 86 | 99.46 61 | | | 98.95 103 | | 99.37 58 | | | | 99.33 78 | |
|
X-MVStestdata | | | | | | 99.77 38 | 99.07 86 | 99.46 61 | | | 98.95 103 | | 99.37 58 | | | | 99.33 78 | |
|
abl_6 | | | | | 98.38 158 | 99.03 188 | 98.04 179 | 98.08 191 | 98.65 185 | 93.23 202 | 98.56 132 | 94.58 204 | 98.57 137 | 97.17 165 | | | 98.81 141 | 97.42 165 |
|
mPP-MVS | | | | | | 99.75 47 | | | | | | | 99.49 50 | | | | | |
|
NP-MVS | | | | | | | | | | 93.07 204 | | | | | | | | |
|
Patchmtry | | | | | | | 96.05 219 | 97.64 210 | 99.78 19 | | 98.50 138 | | | | | | | |
|
DeepMVS_CX | | | | | | | 87.86 239 | 92.27 239 | 61.98 235 | 93.64 198 | 93.62 234 | 91.17 217 | 91.67 193 | 94.90 201 | 95.99 201 | | 92.48 229 | 94.18 210 |
|