WR-MVS | | | 99.22 4 | 99.15 5 | 99.30 2 | 99.54 11 | 99.62 1 | 99.63 4 | 99.45 2 | 97.75 17 | 98.47 25 | 99.71 8 | 99.05 43 | 98.88 7 | 99.54 6 | 99.49 3 | 99.81 1 | 98.87 10 |
|
Anonymous20231211 | | | 99.36 1 | 99.64 1 | 99.03 9 | 99.22 34 | 99.53 6 | 99.38 15 | 99.55 1 | 99.70 1 | 98.74 19 | 99.74 6 | 99.96 1 | 97.48 71 | 99.75 1 | 99.63 1 | 99.80 2 | 99.19 3 |
|
PS-CasMVS | | | 99.08 5 | 98.90 13 | 99.28 3 | 99.65 3 | 99.56 4 | 99.59 6 | 99.39 4 | 96.36 35 | 98.83 16 | 99.46 38 | 99.09 35 | 98.62 14 | 99.51 7 | 99.36 8 | 99.63 3 | 98.97 7 |
|
CP-MVSNet | | | 98.91 14 | 98.61 21 | 99.25 4 | 99.63 5 | 99.50 7 | 99.55 10 | 99.36 5 | 95.53 68 | 98.77 18 | 99.11 58 | 98.64 89 | 98.57 17 | 99.42 11 | 99.28 11 | 99.61 4 | 98.78 15 |
|
PEN-MVS | | | 99.08 5 | 98.95 10 | 99.23 5 | 99.65 3 | 99.59 2 | 99.64 2 | 99.34 6 | 96.68 28 | 98.65 20 | 99.43 41 | 99.33 17 | 98.47 21 | 99.50 8 | 99.32 9 | 99.60 5 | 98.79 12 |
|
pmmvs6 | | | 98.77 16 | 99.35 3 | 98.09 49 | 98.32 95 | 98.92 20 | 98.57 80 | 99.03 12 | 99.36 2 | 96.86 95 | 99.77 5 | 99.86 2 | 96.20 110 | 99.56 5 | 99.39 7 | 99.59 6 | 98.61 22 |
|
EPP-MVSNet | | | 97.29 93 | 96.88 99 | 97.76 84 | 98.70 73 | 99.10 12 | 98.92 50 | 98.36 39 | 95.12 84 | 93.36 192 | 97.39 113 | 91.00 186 | 97.65 59 | 98.72 37 | 98.91 23 | 99.58 7 | 97.92 51 |
|
TranMVSNet+NR-MVSNet | | | 98.45 21 | 98.22 32 | 98.72 20 | 99.32 30 | 99.06 13 | 98.99 39 | 98.89 14 | 95.52 69 | 97.53 61 | 99.42 43 | 98.83 69 | 98.01 36 | 98.55 48 | 98.34 49 | 99.57 8 | 97.80 55 |
|
DTE-MVSNet | | | 99.03 7 | 98.88 14 | 99.21 6 | 99.66 2 | 99.59 2 | 99.62 5 | 99.34 6 | 96.92 25 | 98.52 22 | 99.36 48 | 98.98 50 | 98.57 17 | 99.49 9 | 99.23 12 | 99.56 9 | 98.55 24 |
|
UniMVSNet_NR-MVSNet | | | 98.12 37 | 97.56 64 | 98.78 17 | 99.13 46 | 98.89 22 | 98.76 63 | 98.78 19 | 93.81 131 | 98.50 23 | 98.81 70 | 97.64 128 | 97.99 38 | 98.18 65 | 97.92 75 | 99.53 10 | 97.64 61 |
|
WR-MVS_H | | | 98.97 12 | 98.82 16 | 99.14 8 | 99.56 9 | 99.56 4 | 99.54 11 | 99.42 3 | 96.07 42 | 98.37 27 | 99.34 49 | 99.09 35 | 98.43 22 | 99.45 10 | 99.41 6 | 99.53 10 | 98.86 11 |
|
NR-MVSNet | | | 98.00 44 | 97.88 43 | 98.13 45 | 98.33 92 | 98.77 33 | 98.83 58 | 98.88 15 | 94.10 121 | 97.46 66 | 98.87 66 | 98.58 95 | 95.78 117 | 99.13 24 | 98.16 61 | 99.52 12 | 97.53 68 |
|
IS_MVSNet | | | 96.62 121 | 96.48 116 | 96.78 128 | 98.46 84 | 98.68 51 | 98.61 78 | 98.24 51 | 92.23 156 | 89.63 215 | 95.90 145 | 94.40 169 | 96.23 108 | 98.65 44 | 98.77 29 | 99.52 12 | 96.76 107 |
|
LTVRE_ROB | | 97.71 1 | 99.33 2 | 99.47 2 | 99.16 7 | 99.16 40 | 99.11 10 | 99.39 14 | 99.16 11 | 99.26 3 | 99.22 4 | 99.51 32 | 99.75 4 | 98.54 19 | 99.71 2 | 99.47 4 | 99.52 12 | 99.46 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 |
UniMVSNet (Re) | | | 98.23 27 | 97.85 45 | 98.67 22 | 99.15 41 | 98.87 23 | 98.74 72 | 98.84 17 | 94.27 120 | 97.94 48 | 99.01 60 | 98.39 103 | 97.82 48 | 98.35 59 | 98.29 53 | 99.51 15 | 97.78 56 |
|
DU-MVS | | | 98.23 27 | 97.74 55 | 98.81 16 | 99.23 32 | 98.77 33 | 98.76 63 | 98.88 15 | 94.10 121 | 98.50 23 | 98.87 66 | 98.32 106 | 97.99 38 | 98.40 54 | 98.08 70 | 99.49 16 | 97.64 61 |
|
UA-Net | | | 98.66 19 | 98.60 23 | 98.73 19 | 99.83 1 | 99.28 9 | 98.56 82 | 99.24 8 | 96.04 43 | 97.12 81 | 98.44 85 | 98.95 56 | 98.17 30 | 99.15 23 | 99.00 18 | 99.48 17 | 99.33 2 |
|
TDRefinement | | | 99.00 9 | 99.13 6 | 98.86 12 | 98.99 57 | 99.05 15 | 99.58 7 | 98.29 48 | 98.96 5 | 97.96 47 | 99.40 45 | 98.67 86 | 98.87 8 | 99.60 4 | 99.46 5 | 99.46 18 | 98.74 18 |
|
TransMVSNet (Re) | | | 98.23 27 | 98.72 18 | 97.66 86 | 98.22 107 | 98.73 45 | 98.66 77 | 98.03 73 | 98.60 7 | 96.40 112 | 99.60 21 | 98.24 109 | 95.26 126 | 99.19 21 | 99.05 17 | 99.36 19 | 97.64 61 |
|
pm-mvs1 | | | 98.14 35 | 98.66 20 | 97.53 94 | 97.93 139 | 98.49 66 | 98.14 101 | 98.19 58 | 97.95 14 | 96.17 124 | 99.63 18 | 98.85 67 | 95.41 124 | 98.91 30 | 98.89 25 | 99.34 20 | 97.86 53 |
|
anonymousdsp | | | 98.85 15 | 98.88 14 | 98.83 15 | 98.69 76 | 98.20 79 | 99.68 1 | 97.35 135 | 97.09 24 | 98.98 12 | 99.86 1 | 99.43 11 | 98.94 6 | 99.28 16 | 99.19 13 | 99.33 21 | 99.08 5 |
|
ACMH+ | | 94.90 8 | 98.40 24 | 98.71 19 | 98.04 60 | 98.93 59 | 98.84 25 | 99.30 19 | 97.86 85 | 97.78 16 | 94.19 174 | 98.77 73 | 99.39 14 | 98.61 15 | 99.33 13 | 99.07 14 | 99.33 21 | 97.81 54 |
|
SixPastTwentyTwo | | | 99.25 3 | 99.20 4 | 99.32 1 | 99.53 14 | 99.32 8 | 99.64 2 | 99.19 10 | 98.05 13 | 99.19 5 | 99.74 6 | 98.96 55 | 99.03 5 | 99.69 3 | 99.58 2 | 99.32 23 | 99.06 6 |
|
CSCG | | | 98.45 21 | 98.61 21 | 98.26 38 | 99.11 48 | 99.06 13 | 98.17 100 | 97.49 112 | 97.93 15 | 97.37 70 | 98.88 64 | 99.29 18 | 98.10 31 | 98.40 54 | 97.51 83 | 99.32 23 | 99.16 4 |
|
COLMAP_ROB | | 96.84 2 | 98.75 17 | 98.82 16 | 98.66 23 | 99.14 44 | 98.79 31 | 99.30 19 | 97.67 97 | 98.33 8 | 97.82 50 | 99.20 55 | 99.18 32 | 98.76 9 | 99.27 17 | 98.96 21 | 99.29 25 | 98.03 46 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
Baseline_NR-MVSNet | | | 98.17 32 | 97.90 42 | 98.48 30 | 99.23 32 | 98.59 56 | 98.83 58 | 98.73 21 | 93.97 128 | 96.95 89 | 99.66 12 | 98.23 111 | 97.90 44 | 98.40 54 | 99.06 16 | 99.25 26 | 97.42 76 |
|
LGP-MVS_train | | | 97.96 51 | 97.53 65 | 98.45 32 | 99.45 22 | 98.64 53 | 99.09 29 | 98.27 49 | 92.99 149 | 96.04 128 | 96.57 130 | 99.29 18 | 98.66 12 | 98.73 35 | 98.42 44 | 99.19 27 | 98.09 44 |
|
ACMP | | 94.03 12 | 97.97 50 | 97.61 59 | 98.39 34 | 99.43 24 | 98.51 64 | 98.97 43 | 98.06 70 | 94.63 99 | 96.10 126 | 96.12 139 | 99.20 30 | 98.63 13 | 98.68 41 | 98.20 59 | 99.14 28 | 97.93 50 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
ACMM | | 94.29 11 | 98.12 37 | 97.71 57 | 98.59 25 | 99.51 16 | 98.58 57 | 99.24 21 | 98.25 50 | 96.22 40 | 96.90 90 | 95.01 159 | 98.89 61 | 98.52 20 | 98.66 43 | 98.32 52 | 99.13 29 | 98.28 40 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Vis-MVSNet (Re-imp) | | | 96.29 126 | 96.50 114 | 96.05 156 | 97.96 138 | 97.83 124 | 97.30 153 | 97.86 85 | 93.14 144 | 88.90 219 | 96.80 125 | 95.28 160 | 95.15 129 | 98.37 58 | 98.25 55 | 99.12 30 | 95.84 128 |
|
SD-MVS | | | 97.84 56 | 97.78 51 | 97.90 67 | 98.33 92 | 98.06 95 | 97.95 112 | 97.80 90 | 96.03 45 | 96.72 97 | 97.57 108 | 99.18 32 | 97.50 70 | 97.88 68 | 97.08 97 | 99.11 31 | 98.68 20 |
|
LS3D | | | 97.93 53 | 97.80 47 | 98.08 54 | 99.20 37 | 98.77 33 | 98.89 54 | 97.92 78 | 96.59 30 | 96.99 87 | 96.71 127 | 97.14 139 | 96.39 106 | 99.04 25 | 98.96 21 | 99.10 32 | 97.39 77 |
|
CP-MVS | | | 98.00 44 | 97.57 62 | 98.50 28 | 99.47 20 | 98.56 60 | 98.91 51 | 98.38 37 | 94.71 95 | 97.01 86 | 95.20 155 | 99.06 40 | 98.20 28 | 98.61 46 | 98.46 41 | 99.02 33 | 98.40 35 |
|
FMVSNet1 | | | 97.40 84 | 98.09 35 | 96.60 136 | 97.80 153 | 98.76 38 | 98.26 97 | 98.50 25 | 96.79 27 | 93.13 196 | 99.28 52 | 98.64 89 | 92.90 162 | 97.67 77 | 97.86 78 | 99.02 33 | 97.64 61 |
|
v748 | | | 98.92 13 | 98.95 10 | 98.87 11 | 98.54 80 | 98.69 49 | 99.33 17 | 98.64 22 | 98.07 12 | 99.06 9 | 99.66 12 | 99.76 3 | 98.68 11 | 99.25 18 | 98.72 32 | 99.01 35 | 98.54 25 |
|
ACMMPR | | | 98.31 25 | 98.07 37 | 98.60 24 | 99.58 6 | 98.83 26 | 99.09 29 | 98.48 26 | 96.25 38 | 97.03 85 | 96.81 124 | 99.09 35 | 98.39 24 | 98.55 48 | 98.45 42 | 99.01 35 | 98.53 28 |
|
TSAR-MVS + MP. | | | 98.15 34 | 98.23 31 | 98.06 58 | 98.47 83 | 98.16 85 | 99.23 22 | 96.87 150 | 95.58 63 | 96.72 97 | 98.41 86 | 99.06 40 | 98.05 34 | 98.99 27 | 98.90 24 | 99.00 37 | 98.51 29 |
|
ACMMP | | | 97.99 46 | 97.60 60 | 98.45 32 | 99.53 14 | 98.83 26 | 99.13 28 | 98.30 44 | 94.57 101 | 96.39 116 | 95.32 153 | 98.95 56 | 98.37 25 | 98.61 46 | 98.47 39 | 99.00 37 | 98.45 32 |
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 |
conf0.05thres1000 | | | 95.91 136 | 94.67 152 | 97.37 100 | 98.54 80 | 98.73 45 | 98.41 90 | 98.07 69 | 96.10 41 | 94.93 160 | 92.83 187 | 80.67 212 | 95.26 126 | 98.68 41 | 98.65 35 | 98.99 39 | 97.02 93 |
|
ACMH | | 95.26 7 | 98.75 17 | 98.93 12 | 98.54 27 | 98.86 64 | 99.01 18 | 99.58 7 | 98.10 67 | 98.67 6 | 97.30 73 | 99.18 56 | 99.42 12 | 98.40 23 | 99.19 21 | 98.86 26 | 98.99 39 | 98.19 42 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PMVS | | 90.51 17 | 97.77 59 | 97.98 40 | 97.53 94 | 98.68 77 | 98.14 89 | 97.67 125 | 97.03 145 | 96.43 31 | 98.38 26 | 98.72 76 | 97.03 142 | 94.44 142 | 99.37 12 | 99.30 10 | 98.98 41 | 96.86 102 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
SteuartSystems-ACMMP | | | 98.06 40 | 97.78 51 | 98.39 34 | 99.54 11 | 98.79 31 | 98.94 48 | 98.42 34 | 93.98 127 | 95.85 133 | 96.66 129 | 99.25 25 | 98.61 15 | 98.71 39 | 98.38 46 | 98.97 42 | 98.67 21 |
Skip Steuart: Steuart Systems R&D Blog. |
DeepC-MVS | | 96.08 5 | 98.58 20 | 98.49 25 | 98.68 21 | 99.37 26 | 98.52 63 | 99.01 37 | 98.17 62 | 97.17 23 | 98.25 31 | 99.56 25 | 99.62 5 | 98.29 26 | 98.40 54 | 98.09 63 | 98.97 42 | 98.08 45 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
FC-MVSNet-train | | | 97.65 63 | 98.16 33 | 97.05 116 | 98.85 65 | 98.85 24 | 99.34 16 | 98.08 68 | 94.50 109 | 94.41 168 | 99.21 54 | 98.80 73 | 92.66 164 | 98.98 28 | 98.85 27 | 98.96 44 | 97.94 49 |
|
X-MVS | | | 97.60 65 | 97.00 91 | 98.29 37 | 99.50 17 | 98.76 38 | 98.90 52 | 98.37 38 | 94.67 98 | 96.40 112 | 91.47 197 | 98.78 75 | 97.60 65 | 98.55 48 | 98.50 38 | 98.96 44 | 98.29 37 |
|
HFP-MVS | | | 98.17 32 | 98.02 38 | 98.35 36 | 99.36 27 | 98.62 54 | 98.79 60 | 98.46 31 | 96.24 39 | 96.53 105 | 97.13 121 | 98.98 50 | 98.02 35 | 98.20 62 | 98.42 44 | 98.95 46 | 98.54 25 |
|
XVS | | | | | | 99.48 18 | 98.76 38 | 99.22 24 | | | 96.40 112 | | 98.78 75 | | | | 98.94 47 | |
|
X-MVStestdata | | | | | | 99.48 18 | 98.76 38 | 99.22 24 | | | 96.40 112 | | 98.78 75 | | | | 98.94 47 | |
|
zzz-MVS | | | 98.14 35 | 97.78 51 | 98.55 26 | 99.58 6 | 98.58 57 | 98.98 41 | 98.48 26 | 95.98 46 | 97.39 68 | 94.73 163 | 99.27 22 | 97.98 40 | 98.81 32 | 98.64 36 | 98.90 49 | 98.46 31 |
|
canonicalmvs | | | 97.11 100 | 96.88 99 | 97.38 99 | 98.34 91 | 98.72 47 | 97.52 136 | 97.94 77 | 95.60 61 | 95.01 158 | 94.58 166 | 94.50 168 | 96.59 97 | 97.84 69 | 98.03 72 | 98.90 49 | 98.91 8 |
|
MP-MVS | | | 97.98 47 | 97.53 65 | 98.50 28 | 99.56 9 | 98.58 57 | 98.97 43 | 98.39 36 | 93.49 136 | 97.14 78 | 96.08 140 | 99.23 28 | 98.06 33 | 98.50 51 | 98.38 46 | 98.90 49 | 98.44 33 |
|
PGM-MVS | | | 97.82 58 | 97.25 71 | 98.48 30 | 99.54 11 | 98.75 42 | 99.02 33 | 98.35 41 | 92.41 154 | 96.84 96 | 95.39 152 | 98.99 48 | 98.24 27 | 98.43 52 | 98.34 49 | 98.90 49 | 98.41 34 |
|
RPSCF | | | 97.83 57 | 98.27 29 | 97.31 104 | 98.23 104 | 98.06 95 | 97.44 146 | 95.79 182 | 96.90 26 | 95.81 135 | 98.76 74 | 98.61 93 | 97.70 55 | 98.90 31 | 98.36 48 | 98.90 49 | 98.29 37 |
|
Gipuma | | | 98.43 23 | 98.15 34 | 98.76 18 | 99.00 56 | 98.29 76 | 97.91 115 | 98.06 70 | 99.02 4 | 99.50 1 | 96.33 134 | 98.67 86 | 99.22 1 | 99.02 26 | 98.02 73 | 98.88 54 | 97.66 60 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
tfpnnormal | | | 97.66 62 | 97.79 48 | 97.52 96 | 98.32 95 | 98.53 62 | 98.45 87 | 97.69 96 | 97.59 19 | 96.12 125 | 97.79 104 | 96.70 144 | 95.69 120 | 98.35 59 | 98.34 49 | 98.85 55 | 97.22 89 |
|
APDe-MVS | | | 98.29 26 | 98.42 26 | 98.14 44 | 99.45 22 | 98.90 21 | 99.18 26 | 98.30 44 | 95.96 48 | 95.13 153 | 98.79 71 | 99.25 25 | 97.92 43 | 98.80 33 | 98.71 33 | 98.85 55 | 98.54 25 |
|
SMA-MVS | | | 98.22 30 | 98.31 28 | 98.11 47 | 99.46 21 | 98.77 33 | 98.34 92 | 97.92 78 | 95.27 79 | 96.97 88 | 98.82 69 | 99.39 14 | 97.10 84 | 98.69 40 | 98.47 39 | 98.84 57 | 98.77 16 |
|
V4 | | | 98.98 10 | 99.10 7 | 98.85 13 | 98.91 60 | 99.03 16 | 99.41 12 | 97.77 93 | 98.12 10 | 99.06 9 | 99.85 2 | 99.60 6 | 99.15 2 | 99.30 14 | 98.99 19 | 98.80 58 | 98.79 12 |
|
APD-MVS | | | 97.47 78 | 97.16 78 | 97.84 75 | 99.32 30 | 98.39 72 | 98.47 86 | 98.21 55 | 92.08 159 | 95.23 150 | 96.68 128 | 98.90 60 | 96.99 87 | 98.20 62 | 98.21 56 | 98.80 58 | 97.67 59 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
v52 | | | 98.98 10 | 99.10 7 | 98.85 13 | 98.91 60 | 99.03 16 | 99.41 12 | 97.77 93 | 98.12 10 | 99.07 8 | 99.84 3 | 99.60 6 | 99.15 2 | 99.29 15 | 98.99 19 | 98.79 60 | 98.79 12 |
|
PHI-MVS | | | 97.44 80 | 97.17 77 | 97.74 85 | 98.14 117 | 98.41 71 | 98.03 106 | 97.50 109 | 92.07 160 | 98.01 45 | 97.33 115 | 98.62 92 | 96.02 114 | 98.34 61 | 98.21 56 | 98.76 61 | 97.24 86 |
|
tfpn_n400 | | | 95.11 151 | 93.86 162 | 96.57 138 | 98.16 115 | 97.92 113 | 97.59 132 | 97.90 80 | 95.90 51 | 92.83 201 | 89.94 206 | 83.01 203 | 94.23 148 | 97.50 85 | 97.43 86 | 98.73 62 | 95.30 144 |
|
tfpnconf | | | 95.11 151 | 93.86 162 | 96.57 138 | 98.16 115 | 97.92 113 | 97.59 132 | 97.90 80 | 95.90 51 | 92.83 201 | 89.94 206 | 83.01 203 | 94.23 148 | 97.50 85 | 97.43 86 | 98.73 62 | 95.30 144 |
|
FC-MVSNet-test | | | 97.54 68 | 98.26 30 | 96.70 131 | 98.87 63 | 97.79 129 | 98.49 84 | 98.56 23 | 96.04 43 | 90.39 209 | 99.65 14 | 98.67 86 | 95.15 129 | 99.23 19 | 99.07 14 | 98.73 62 | 97.39 77 |
|
tfpn1000 | | | 94.36 166 | 93.33 173 | 95.56 171 | 98.09 123 | 98.07 94 | 97.08 165 | 97.78 92 | 94.02 126 | 89.16 218 | 91.38 198 | 80.56 213 | 92.54 172 | 96.76 118 | 98.09 63 | 98.69 65 | 94.40 166 |
|
ACMMP_Plus | | | 98.12 37 | 98.08 36 | 98.18 42 | 99.34 28 | 98.74 43 | 98.97 43 | 98.00 74 | 95.13 83 | 96.90 90 | 97.54 110 | 99.27 22 | 97.18 82 | 98.72 37 | 98.45 42 | 98.68 66 | 98.69 19 |
|
tfpn_ndepth | | | 93.27 184 | 92.11 187 | 94.61 186 | 96.96 187 | 97.93 112 | 96.87 170 | 97.49 112 | 90.91 173 | 87.89 226 | 85.98 216 | 83.53 200 | 89.77 194 | 95.91 152 | 97.31 93 | 98.67 67 | 93.25 178 |
|
v7n | | | 99.03 7 | 99.03 9 | 99.02 10 | 99.09 51 | 99.11 10 | 99.57 9 | 98.82 18 | 98.21 9 | 99.25 2 | 99.84 3 | 99.59 8 | 98.76 9 | 99.23 19 | 98.83 28 | 98.63 68 | 98.40 35 |
|
MIMVSNet1 | | | 98.22 30 | 98.51 24 | 97.87 73 | 99.40 25 | 98.82 28 | 99.31 18 | 98.53 24 | 97.39 20 | 96.59 103 | 99.31 51 | 99.23 28 | 94.76 136 | 98.93 29 | 98.67 34 | 98.63 68 | 97.25 84 |
|
Vis-MVSNet | | | 98.01 42 | 98.42 26 | 97.54 93 | 96.89 189 | 98.82 28 | 99.14 27 | 97.59 101 | 96.30 36 | 97.04 84 | 99.26 53 | 98.83 69 | 96.01 115 | 98.73 35 | 98.21 56 | 98.58 70 | 98.75 17 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
tfpn | | | 92.86 186 | 89.37 203 | 96.93 121 | 98.40 87 | 98.34 74 | 98.02 108 | 97.80 90 | 92.54 152 | 93.99 177 | 86.54 215 | 57.58 236 | 94.82 134 | 97.66 80 | 97.99 74 | 98.56 71 | 94.95 153 |
|
ESAPD | | | 97.71 61 | 97.79 48 | 97.62 87 | 99.21 35 | 98.80 30 | 98.31 95 | 98.30 44 | 93.60 134 | 94.74 162 | 97.94 99 | 99.24 27 | 96.58 98 | 98.42 53 | 98.27 54 | 98.56 71 | 98.28 40 |
|
CDPH-MVS | | | 96.68 117 | 95.99 126 | 97.48 97 | 99.13 46 | 97.64 132 | 98.08 103 | 97.46 117 | 90.56 177 | 95.13 153 | 94.87 161 | 98.27 108 | 96.56 100 | 97.09 101 | 96.45 123 | 98.54 73 | 97.08 91 |
|
CPTT-MVS | | | 97.08 102 | 96.25 118 | 98.05 59 | 99.21 35 | 98.30 75 | 98.54 83 | 97.98 75 | 94.28 118 | 95.89 132 | 89.57 209 | 98.54 97 | 98.18 29 | 97.82 70 | 97.32 91 | 98.54 73 | 97.91 52 |
|
3Dnovator+ | | 96.20 4 | 97.58 66 | 97.14 80 | 98.10 48 | 98.98 58 | 97.85 123 | 98.60 79 | 98.33 42 | 96.41 33 | 97.23 77 | 94.66 165 | 97.26 135 | 96.91 89 | 97.91 67 | 97.87 77 | 98.53 75 | 98.03 46 |
|
tfpn111 | | | 93.73 180 | 91.63 192 | 96.17 152 | 97.52 164 | 98.15 86 | 97.48 139 | 97.48 114 | 87.65 201 | 93.42 188 | 82.19 228 | 84.12 195 | 92.62 165 | 97.04 103 | 98.09 63 | 98.52 76 | 94.17 167 |
|
conf200view11 | | | 93.79 179 | 91.75 190 | 96.17 152 | 97.52 164 | 98.15 86 | 97.48 139 | 97.48 114 | 87.65 201 | 93.42 188 | 83.03 225 | 84.12 195 | 92.62 165 | 97.04 103 | 98.09 63 | 98.52 76 | 94.17 167 |
|
tfpn200view9 | | | 93.80 178 | 91.75 190 | 96.20 150 | 97.52 164 | 98.15 86 | 97.48 139 | 97.47 116 | 87.65 201 | 93.56 186 | 83.03 225 | 84.12 195 | 92.62 165 | 97.04 103 | 98.09 63 | 98.52 76 | 94.17 167 |
|
tfpnview11 | | | 94.92 156 | 93.56 167 | 96.50 142 | 98.12 121 | 97.99 103 | 97.48 139 | 97.86 85 | 94.50 109 | 92.83 201 | 89.94 206 | 83.01 203 | 94.19 150 | 96.91 114 | 98.07 71 | 98.50 79 | 94.53 159 |
|
view800 | | | 94.54 162 | 92.55 178 | 96.86 126 | 98.28 100 | 98.22 78 | 97.97 111 | 97.62 100 | 92.10 158 | 94.19 174 | 85.52 218 | 81.33 211 | 94.61 138 | 97.41 88 | 98.51 37 | 98.50 79 | 94.72 156 |
|
thres600view7 | | | 94.34 168 | 92.31 184 | 96.70 131 | 98.19 110 | 98.12 90 | 97.85 121 | 97.45 122 | 91.49 165 | 93.98 178 | 84.27 221 | 82.02 209 | 94.24 146 | 97.04 103 | 98.76 30 | 98.49 81 | 94.47 162 |
|
thres200 | | | 93.98 177 | 91.90 189 | 96.40 147 | 97.66 157 | 98.12 90 | 97.20 160 | 97.45 122 | 90.16 182 | 93.82 179 | 83.08 224 | 83.74 199 | 93.80 155 | 97.04 103 | 97.48 85 | 98.49 81 | 93.70 174 |
|
conf0.01 | | | 91.86 195 | 88.22 206 | 96.10 154 | 97.40 173 | 97.94 110 | 97.48 139 | 97.41 129 | 87.65 201 | 93.22 194 | 80.39 230 | 63.83 232 | 92.62 165 | 96.63 124 | 98.09 63 | 98.47 83 | 93.03 183 |
|
conf0.002 | | | 91.12 203 | 86.87 217 | 96.08 155 | 97.35 176 | 97.89 119 | 97.48 139 | 97.38 131 | 87.65 201 | 93.19 195 | 79.38 232 | 57.48 237 | 92.62 165 | 96.56 126 | 96.64 115 | 98.46 84 | 92.50 186 |
|
Effi-MVS+-dtu | | | 95.94 135 | 95.08 144 | 96.94 120 | 98.54 80 | 97.38 144 | 96.66 176 | 97.89 83 | 88.68 190 | 95.92 130 | 92.90 186 | 97.28 134 | 94.18 151 | 96.68 122 | 96.13 138 | 98.45 85 | 96.51 116 |
|
MCST-MVS | | | 96.79 113 | 96.08 123 | 97.62 87 | 98.78 71 | 97.52 141 | 98.01 109 | 97.32 136 | 93.20 142 | 95.84 134 | 93.97 176 | 98.12 113 | 97.34 78 | 96.34 133 | 95.88 150 | 98.45 85 | 97.51 71 |
|
TAPA-MVS | | 93.96 13 | 96.79 113 | 96.70 111 | 96.90 124 | 97.64 158 | 97.58 135 | 97.54 135 | 94.50 209 | 95.14 82 | 96.64 102 | 96.76 126 | 97.90 120 | 96.63 94 | 95.98 150 | 96.14 136 | 98.45 85 | 97.39 77 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
view600 | | | 94.36 166 | 92.33 183 | 96.73 129 | 98.14 117 | 98.03 99 | 97.88 118 | 97.36 134 | 91.61 162 | 94.29 171 | 84.38 220 | 82.08 208 | 94.31 145 | 97.05 102 | 98.75 31 | 98.42 88 | 94.41 164 |
|
OPM-MVS | | | 98.01 42 | 98.01 39 | 98.00 63 | 99.11 48 | 98.12 90 | 98.68 76 | 97.72 95 | 96.65 29 | 96.68 101 | 98.40 87 | 99.28 21 | 97.44 73 | 98.20 62 | 97.82 81 | 98.40 89 | 97.58 66 |
|
thres400 | | | 94.04 175 | 91.94 188 | 96.50 142 | 97.98 137 | 97.82 126 | 97.66 127 | 96.96 146 | 90.96 171 | 94.20 172 | 83.24 223 | 82.82 206 | 93.80 155 | 96.50 127 | 98.09 63 | 98.38 90 | 94.15 170 |
|
CLD-MVS | | | 96.73 116 | 96.92 95 | 96.51 141 | 98.70 73 | 97.57 137 | 97.64 128 | 92.07 216 | 93.10 147 | 96.31 117 | 98.29 89 | 99.02 46 | 95.99 116 | 97.20 96 | 96.47 122 | 98.37 91 | 96.81 106 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
GBi-Net | | | 95.21 149 | 95.35 136 | 95.04 179 | 96.77 192 | 98.18 81 | 97.28 154 | 97.58 102 | 88.43 195 | 90.28 211 | 96.01 141 | 92.43 178 | 90.04 190 | 97.67 77 | 97.86 78 | 98.28 92 | 96.90 97 |
|
test1 | | | 95.21 149 | 95.35 136 | 95.04 179 | 96.77 192 | 98.18 81 | 97.28 154 | 97.58 102 | 88.43 195 | 90.28 211 | 96.01 141 | 92.43 178 | 90.04 190 | 97.67 77 | 97.86 78 | 98.28 92 | 96.90 97 |
|
FMVSNet2 | | | 95.77 138 | 96.20 121 | 95.27 176 | 96.77 192 | 98.18 81 | 97.28 154 | 97.90 80 | 93.12 145 | 91.37 206 | 98.25 91 | 96.05 155 | 90.04 190 | 94.96 174 | 95.94 147 | 98.28 92 | 96.90 97 |
|
OMC-MVS | | | 97.23 96 | 97.21 73 | 97.25 108 | 97.85 144 | 97.52 141 | 97.92 114 | 95.77 183 | 95.83 55 | 97.09 83 | 97.86 102 | 98.52 98 | 96.62 95 | 97.51 83 | 96.65 114 | 98.26 95 | 96.57 112 |
|
3Dnovator | | 96.31 3 | 97.22 97 | 97.19 75 | 97.25 108 | 98.14 117 | 97.95 107 | 98.03 106 | 96.77 156 | 96.42 32 | 97.14 78 | 95.11 156 | 97.59 129 | 95.14 131 | 97.79 71 | 97.72 82 | 98.26 95 | 97.76 58 |
|
PVSNet_Blended_VisFu | | | 97.44 80 | 97.14 80 | 97.79 78 | 99.15 41 | 98.44 69 | 98.32 94 | 97.66 98 | 93.74 133 | 97.73 52 | 98.79 71 | 96.93 143 | 95.64 123 | 97.69 75 | 96.91 103 | 98.25 97 | 97.50 72 |
|
DeepC-MVS_fast | | 95.38 6 | 97.53 71 | 97.30 69 | 97.79 78 | 98.83 69 | 97.64 132 | 98.18 98 | 97.14 141 | 95.57 64 | 97.83 49 | 97.10 122 | 98.80 73 | 96.53 102 | 97.41 88 | 97.32 91 | 98.24 98 | 97.26 83 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
thresconf0.02 | | | 91.75 197 | 88.21 207 | 95.87 160 | 97.38 174 | 97.14 153 | 97.27 157 | 96.85 152 | 93.04 148 | 92.39 204 | 82.19 228 | 63.31 233 | 93.10 159 | 94.43 184 | 95.06 165 | 98.23 99 | 92.32 187 |
|
HPM-MVS++ | | | 97.56 67 | 97.11 85 | 98.09 49 | 99.18 39 | 97.95 107 | 98.57 80 | 98.20 56 | 94.08 123 | 97.25 76 | 95.96 144 | 98.81 72 | 97.13 83 | 97.51 83 | 97.30 94 | 98.21 100 | 98.15 43 |
|
NCCC | | | 96.56 122 | 95.68 132 | 97.59 89 | 99.04 54 | 97.54 140 | 97.67 125 | 97.56 105 | 94.84 92 | 96.10 126 | 87.91 212 | 98.09 114 | 96.98 88 | 97.20 96 | 96.80 109 | 98.21 100 | 97.38 80 |
|
TSAR-MVS + GP. | | | 97.26 95 | 97.33 68 | 97.18 110 | 98.21 108 | 98.06 95 | 96.38 183 | 97.66 98 | 93.92 130 | 95.23 150 | 98.48 83 | 98.33 105 | 97.41 74 | 97.63 82 | 97.35 89 | 98.18 102 | 97.57 67 |
|
thres100view900 | | | 92.93 185 | 90.89 196 | 95.31 174 | 97.52 164 | 96.82 166 | 96.41 181 | 95.08 194 | 87.65 201 | 93.56 186 | 83.03 225 | 84.12 195 | 91.12 179 | 94.53 178 | 96.91 103 | 98.17 103 | 93.21 180 |
|
MVS_0304 | | | 97.18 98 | 96.84 105 | 97.58 90 | 99.15 41 | 98.19 80 | 98.11 102 | 97.81 89 | 92.36 155 | 98.06 42 | 97.43 112 | 99.06 40 | 94.24 146 | 96.80 117 | 96.54 120 | 98.12 104 | 97.52 70 |
|
CNVR-MVS | | | 97.03 105 | 96.77 109 | 97.34 101 | 98.89 62 | 97.67 131 | 97.64 128 | 97.17 140 | 94.40 114 | 95.70 141 | 94.02 174 | 98.76 79 | 96.49 104 | 97.78 72 | 97.29 95 | 98.12 104 | 97.47 73 |
|
HQP-MVS | | | 95.97 134 | 95.01 147 | 97.08 113 | 98.72 72 | 97.19 150 | 97.07 166 | 96.69 160 | 91.49 165 | 95.77 137 | 92.19 192 | 97.93 119 | 96.15 112 | 94.66 176 | 94.16 175 | 98.10 106 | 97.45 74 |
|
PCF-MVS | | 92.69 14 | 95.98 133 | 95.05 145 | 97.06 115 | 98.43 86 | 97.56 138 | 97.76 122 | 96.65 162 | 89.95 185 | 95.70 141 | 96.18 138 | 98.48 101 | 95.74 118 | 93.64 192 | 93.35 188 | 98.09 107 | 96.18 121 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVS_111021_HR | | | 97.27 94 | 97.11 85 | 97.46 98 | 98.46 84 | 97.82 126 | 97.50 137 | 96.86 151 | 94.97 88 | 97.13 80 | 96.99 123 | 98.39 103 | 96.82 91 | 97.65 81 | 97.38 88 | 98.02 108 | 96.56 114 |
|
EG-PatchMatch MVS | | | 97.98 47 | 97.92 41 | 98.04 60 | 98.84 66 | 98.04 98 | 97.90 116 | 96.83 154 | 95.07 85 | 98.79 17 | 99.07 59 | 99.37 16 | 97.88 46 | 98.74 34 | 98.16 61 | 98.01 109 | 96.96 95 |
|
DELS-MVS | | | 96.90 107 | 97.24 72 | 96.50 142 | 97.85 144 | 98.18 81 | 97.88 118 | 95.92 175 | 93.48 137 | 95.34 148 | 98.86 68 | 98.94 59 | 94.03 152 | 97.33 92 | 97.04 98 | 98.00 110 | 96.85 104 |
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 |
OpenMVS | | 94.63 9 | 95.75 139 | 95.04 146 | 96.58 137 | 97.85 144 | 97.55 139 | 96.71 175 | 96.07 170 | 90.15 183 | 96.47 107 | 90.77 205 | 95.95 156 | 94.41 143 | 97.01 109 | 96.95 100 | 98.00 110 | 96.90 97 |
|
Effi-MVS+ | | | 96.46 123 | 95.28 138 | 97.85 74 | 98.64 78 | 97.16 151 | 97.15 164 | 98.75 20 | 90.27 180 | 98.03 44 | 93.93 177 | 96.21 151 | 96.55 101 | 96.34 133 | 96.69 113 | 97.97 112 | 96.33 119 |
|
train_agg | | | 96.68 117 | 95.93 129 | 97.56 91 | 99.08 52 | 97.16 151 | 98.44 89 | 97.37 133 | 91.12 170 | 95.18 152 | 95.43 151 | 98.48 101 | 97.36 76 | 96.48 128 | 95.52 157 | 97.95 113 | 97.34 82 |
|
PLC | | 92.55 15 | 96.10 129 | 95.36 135 | 96.96 118 | 98.13 120 | 96.88 162 | 96.49 180 | 96.67 161 | 94.07 124 | 95.71 140 | 91.14 200 | 96.09 154 | 96.84 90 | 96.70 121 | 96.58 119 | 97.92 114 | 96.03 125 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
Fast-Effi-MVS+ | | | 96.80 112 | 95.92 130 | 97.84 75 | 98.57 79 | 97.46 143 | 98.06 104 | 98.24 51 | 89.64 187 | 97.57 60 | 96.45 132 | 97.35 133 | 96.73 92 | 97.22 95 | 96.64 115 | 97.86 115 | 96.65 110 |
|
UGNet | | | 96.79 113 | 97.82 46 | 95.58 169 | 97.57 162 | 98.39 72 | 98.48 85 | 97.84 88 | 95.85 54 | 94.68 163 | 97.91 101 | 99.07 39 | 87.12 210 | 97.71 74 | 97.51 83 | 97.80 116 | 98.29 37 |
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022 |
CNLPA | | | 96.24 128 | 95.97 127 | 96.57 138 | 97.48 170 | 97.10 158 | 96.75 173 | 94.95 199 | 94.92 90 | 96.20 122 | 94.81 162 | 96.61 146 | 96.25 107 | 96.94 112 | 95.64 154 | 97.79 117 | 95.74 134 |
|
QAPM | | | 97.04 104 | 97.14 80 | 96.93 121 | 97.78 156 | 98.02 100 | 97.36 151 | 96.72 157 | 94.68 97 | 96.23 119 | 97.21 119 | 97.68 126 | 95.70 119 | 97.37 90 | 97.24 96 | 97.78 118 | 97.77 57 |
|
PVSNet_BlendedMVS | | | 95.44 145 | 95.09 142 | 95.86 161 | 97.31 177 | 97.13 154 | 96.31 187 | 95.01 196 | 88.55 193 | 96.23 119 | 94.55 169 | 97.75 123 | 92.56 170 | 96.42 130 | 95.44 159 | 97.71 119 | 95.81 129 |
|
PVSNet_Blended | | | 95.44 145 | 95.09 142 | 95.86 161 | 97.31 177 | 97.13 154 | 96.31 187 | 95.01 196 | 88.55 193 | 96.23 119 | 94.55 169 | 97.75 123 | 92.56 170 | 96.42 130 | 95.44 159 | 97.71 119 | 95.81 129 |
|
AdaColmap | | | 95.85 137 | 94.65 153 | 97.26 105 | 98.70 73 | 97.20 149 | 97.33 152 | 97.30 137 | 91.28 168 | 95.90 131 | 88.16 211 | 96.17 153 | 96.60 96 | 97.34 91 | 96.82 105 | 97.71 119 | 95.60 137 |
|
test20.03 | | | 96.08 130 | 96.80 107 | 95.25 178 | 99.19 38 | 97.58 135 | 97.24 159 | 97.56 105 | 94.95 89 | 91.91 205 | 98.58 80 | 98.03 117 | 87.88 206 | 97.43 87 | 96.94 101 | 97.69 122 | 94.05 171 |
|
MVS_111021_LR | | | 96.86 108 | 96.72 110 | 97.03 117 | 97.80 153 | 97.06 159 | 97.04 167 | 95.51 188 | 94.55 102 | 97.47 64 | 97.35 114 | 97.68 126 | 96.66 93 | 97.11 99 | 96.73 110 | 97.69 122 | 96.57 112 |
|
MSDG | | | 96.27 127 | 96.17 122 | 96.38 148 | 97.85 144 | 96.27 178 | 96.55 179 | 94.41 210 | 94.55 102 | 95.62 143 | 97.56 109 | 97.80 122 | 96.22 109 | 97.17 98 | 96.27 129 | 97.67 124 | 93.60 175 |
|
HSP-MVS | | | 97.44 80 | 97.13 83 | 97.79 78 | 99.34 28 | 98.99 19 | 99.23 22 | 98.12 65 | 93.43 138 | 95.95 129 | 97.45 111 | 99.50 9 | 96.44 105 | 96.35 132 | 95.33 162 | 97.65 125 | 98.89 9 |
|
DI_MVS_plusplus_trai | | | 95.48 143 | 94.51 155 | 96.61 135 | 97.13 183 | 97.30 145 | 98.05 105 | 96.79 155 | 93.75 132 | 95.08 156 | 96.38 133 | 89.76 189 | 94.95 132 | 93.97 191 | 94.82 171 | 97.64 126 | 95.63 136 |
|
FMVSNet3 | | | 94.06 174 | 93.85 164 | 94.31 192 | 95.46 217 | 97.80 128 | 96.34 184 | 97.58 102 | 88.43 195 | 90.28 211 | 96.01 141 | 92.43 178 | 88.67 202 | 91.82 207 | 93.96 180 | 97.53 127 | 96.50 117 |
|
MSLP-MVS++ | | | 96.66 119 | 96.46 117 | 96.89 125 | 98.02 127 | 97.71 130 | 95.57 201 | 96.96 146 | 94.36 116 | 96.19 123 | 91.37 199 | 98.24 109 | 97.07 85 | 97.69 75 | 97.89 76 | 97.52 128 | 97.95 48 |
|
abl_6 | | | | | 96.45 145 | 97.79 155 | 97.28 146 | 97.16 163 | 96.16 168 | 89.92 186 | 95.72 139 | 91.59 196 | 97.16 138 | 94.37 144 | | | 97.51 129 | 95.49 139 |
|
v1192 | | | 97.52 72 | 97.03 89 | 98.09 49 | 98.31 98 | 98.01 101 | 98.96 46 | 97.25 138 | 95.22 80 | 98.89 14 | 99.64 15 | 98.83 69 | 97.68 57 | 95.63 159 | 95.91 148 | 97.47 130 | 95.97 127 |
|
v1144 | | | 97.51 73 | 97.05 87 | 98.04 60 | 98.26 102 | 97.98 104 | 98.88 55 | 97.42 125 | 95.38 74 | 98.56 21 | 99.59 24 | 99.01 47 | 97.65 59 | 95.77 157 | 96.06 142 | 97.47 130 | 95.56 138 |
|
v144192 | | | 97.49 77 | 96.99 93 | 98.07 56 | 98.11 122 | 97.95 107 | 99.02 33 | 97.21 139 | 94.90 91 | 98.88 15 | 99.53 31 | 98.89 61 | 97.75 51 | 95.59 160 | 95.90 149 | 97.43 132 | 96.16 122 |
|
v7 | | | 97.45 79 | 97.01 90 | 97.97 64 | 98.07 124 | 97.96 105 | 98.86 56 | 97.50 109 | 94.46 112 | 98.24 32 | 99.56 25 | 98.98 50 | 97.72 53 | 96.05 147 | 96.26 130 | 97.42 133 | 95.79 131 |
|
v10 | | | 97.64 64 | 97.26 70 | 98.08 54 | 98.07 124 | 98.56 60 | 98.86 56 | 98.18 61 | 94.48 111 | 98.24 32 | 99.56 25 | 98.98 50 | 97.72 53 | 96.05 147 | 96.26 130 | 97.42 133 | 96.93 96 |
|
CANet | | | 96.81 111 | 96.50 114 | 97.17 111 | 99.10 50 | 97.96 105 | 97.86 120 | 97.51 107 | 91.30 167 | 97.75 51 | 97.64 106 | 97.89 121 | 93.39 158 | 96.98 110 | 96.73 110 | 97.40 135 | 96.99 94 |
|
v13 | | | 98.04 41 | 97.86 44 | 98.24 39 | 98.36 90 | 98.77 33 | 99.04 31 | 98.47 28 | 95.93 49 | 98.20 35 | 99.67 11 | 99.11 34 | 98.00 37 | 97.11 99 | 96.93 102 | 97.40 135 | 97.53 68 |
|
v1921920 | | | 97.50 76 | 97.00 91 | 98.07 56 | 98.20 109 | 97.94 110 | 99.03 32 | 97.06 143 | 95.29 78 | 99.01 11 | 99.62 19 | 98.73 83 | 97.74 52 | 95.52 162 | 95.78 153 | 97.39 137 | 96.12 124 |
|
v12 | | | 97.98 47 | 97.78 51 | 98.21 40 | 98.33 92 | 98.74 43 | 99.01 37 | 98.44 33 | 95.82 56 | 98.13 36 | 99.64 15 | 99.08 38 | 97.95 41 | 96.97 111 | 96.82 105 | 97.39 137 | 97.38 80 |
|
IB-MVS | | 92.44 16 | 93.33 183 | 92.15 186 | 94.70 185 | 97.42 172 | 96.39 174 | 95.57 201 | 94.67 204 | 86.40 218 | 93.59 185 | 78.28 234 | 95.76 158 | 89.59 196 | 95.88 153 | 95.98 143 | 97.39 137 | 96.34 118 |
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 |
V9 | | | 97.91 54 | 97.70 58 | 98.17 43 | 98.30 99 | 98.70 48 | 98.98 41 | 98.40 35 | 95.72 58 | 98.07 40 | 99.64 15 | 99.04 44 | 97.90 44 | 96.82 115 | 96.71 112 | 97.37 140 | 97.23 87 |
|
v2v482 | | | 97.33 89 | 96.84 105 | 97.90 67 | 98.19 110 | 97.83 124 | 98.74 72 | 97.44 124 | 95.42 73 | 98.23 34 | 99.46 38 | 98.84 68 | 97.46 72 | 95.51 163 | 96.10 140 | 97.36 141 | 94.72 156 |
|
DeepPCF-MVS | | 94.55 10 | 97.05 103 | 97.13 83 | 96.95 119 | 96.06 202 | 97.12 156 | 98.01 109 | 95.44 189 | 95.18 81 | 97.50 62 | 97.86 102 | 98.08 115 | 97.31 80 | 97.23 94 | 97.00 99 | 97.36 141 | 97.45 74 |
|
V14 | | | 97.85 55 | 97.60 60 | 98.13 45 | 98.27 101 | 98.66 52 | 98.94 48 | 98.36 39 | 95.62 60 | 98.04 43 | 99.62 19 | 98.99 48 | 97.84 47 | 96.65 123 | 96.59 118 | 97.34 143 | 97.07 92 |
|
v1240 | | | 97.43 83 | 96.87 104 | 98.09 49 | 98.25 103 | 97.92 113 | 99.02 33 | 97.06 143 | 94.77 94 | 99.09 7 | 99.68 10 | 98.51 99 | 97.78 49 | 95.25 167 | 95.81 151 | 97.32 144 | 96.13 123 |
|
v15 | | | 97.77 59 | 97.50 67 | 98.09 49 | 98.23 104 | 98.62 54 | 98.90 52 | 98.32 43 | 95.51 71 | 98.01 45 | 99.60 21 | 98.95 56 | 97.78 49 | 96.47 129 | 96.45 123 | 97.32 144 | 96.90 97 |
|
v1141 | | | 97.36 88 | 96.92 95 | 97.88 72 | 98.18 112 | 97.90 117 | 98.76 63 | 97.42 125 | 95.38 74 | 98.07 40 | 99.56 25 | 98.87 64 | 97.59 67 | 95.78 154 | 95.98 143 | 97.29 146 | 94.97 151 |
|
divwei89l23v2f112 | | | 97.37 86 | 96.92 95 | 97.89 69 | 98.18 112 | 97.90 117 | 98.76 63 | 97.42 125 | 95.38 74 | 98.09 38 | 99.56 25 | 98.87 64 | 97.59 67 | 95.78 154 | 95.98 143 | 97.29 146 | 94.97 151 |
|
v1 | | | 97.37 86 | 96.92 95 | 97.89 69 | 98.18 112 | 97.91 116 | 98.76 63 | 97.42 125 | 95.38 74 | 98.09 38 | 99.55 30 | 98.88 63 | 97.59 67 | 95.78 154 | 95.98 143 | 97.29 146 | 94.98 150 |
|
v11 | | | 97.94 52 | 97.72 56 | 98.20 41 | 98.37 89 | 98.69 49 | 98.96 46 | 98.30 44 | 95.68 59 | 98.35 28 | 99.70 9 | 99.19 31 | 97.93 42 | 96.76 118 | 96.82 105 | 97.28 149 | 97.23 87 |
|
CDS-MVSNet | | | 94.91 157 | 95.17 141 | 94.60 187 | 97.85 144 | 96.21 179 | 96.90 169 | 96.39 165 | 90.81 174 | 93.40 190 | 97.24 118 | 94.54 167 | 85.78 216 | 96.25 138 | 96.15 132 | 97.26 150 | 95.01 149 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
pmmvs-eth3d | | | 96.84 110 | 96.22 120 | 97.56 91 | 97.63 160 | 96.38 175 | 98.74 72 | 96.91 149 | 94.63 99 | 98.26 30 | 99.43 41 | 98.28 107 | 96.58 98 | 94.52 180 | 95.54 156 | 97.24 151 | 94.75 155 |
|
pmmvs5 | | | 95.70 140 | 95.22 139 | 96.26 149 | 96.55 197 | 97.24 147 | 97.50 137 | 94.99 198 | 90.95 172 | 96.87 92 | 98.47 84 | 97.40 131 | 94.45 141 | 92.86 200 | 94.98 167 | 97.23 152 | 94.64 158 |
|
v6 | | | 97.30 90 | 96.88 99 | 97.78 81 | 97.99 131 | 97.87 120 | 98.75 69 | 97.46 117 | 94.54 105 | 97.61 58 | 99.48 34 | 98.77 78 | 97.65 59 | 96.09 144 | 96.15 132 | 97.21 153 | 95.28 146 |
|
Anonymous20231206 | | | 95.69 141 | 95.68 132 | 95.70 165 | 98.32 95 | 96.95 160 | 97.37 149 | 96.65 162 | 93.33 139 | 93.61 184 | 98.70 78 | 98.03 117 | 91.04 180 | 95.07 170 | 94.59 174 | 97.20 154 | 93.09 182 |
|
v1neww | | | 97.30 90 | 96.88 99 | 97.78 81 | 97.99 131 | 97.87 120 | 98.75 69 | 97.46 117 | 94.54 105 | 97.62 56 | 99.48 34 | 98.76 79 | 97.65 59 | 96.09 144 | 96.15 132 | 97.20 154 | 95.28 146 |
|
v7new | | | 97.30 90 | 96.88 99 | 97.78 81 | 97.99 131 | 97.87 120 | 98.75 69 | 97.46 117 | 94.54 105 | 97.62 56 | 99.48 34 | 98.76 79 | 97.65 59 | 96.09 144 | 96.15 132 | 97.20 154 | 95.28 146 |
|
v8 | | | 97.51 73 | 97.16 78 | 97.91 66 | 97.99 131 | 98.48 68 | 98.76 63 | 98.17 62 | 94.54 105 | 97.69 53 | 99.48 34 | 98.76 79 | 97.63 64 | 96.10 143 | 96.14 136 | 97.20 154 | 96.64 111 |
|
v17 | | | 97.54 68 | 97.21 73 | 97.92 65 | 98.02 127 | 98.50 65 | 98.79 60 | 98.24 51 | 94.39 115 | 97.60 59 | 99.45 40 | 98.72 84 | 97.68 57 | 96.29 136 | 96.28 128 | 97.19 158 | 96.86 102 |
|
v16 | | | 97.51 73 | 97.19 75 | 97.89 69 | 97.99 131 | 98.49 66 | 98.77 62 | 98.23 54 | 94.29 117 | 97.48 63 | 99.42 43 | 98.68 85 | 97.69 56 | 96.28 137 | 96.29 127 | 97.18 159 | 96.85 104 |
|
IterMVS-LS | | | 96.35 124 | 95.85 131 | 96.93 121 | 97.53 163 | 98.00 102 | 97.37 149 | 97.97 76 | 95.49 72 | 96.71 100 | 98.94 63 | 93.23 175 | 94.82 134 | 93.15 198 | 95.05 166 | 97.17 160 | 97.12 90 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MVSTER | | | 91.97 193 | 90.31 197 | 93.91 194 | 96.81 190 | 96.91 161 | 94.22 219 | 95.64 185 | 84.98 221 | 92.98 200 | 93.42 180 | 72.56 225 | 86.64 214 | 95.11 169 | 93.89 182 | 97.16 161 | 95.31 142 |
|
v18 | | | 97.40 84 | 97.04 88 | 97.81 77 | 97.90 142 | 98.42 70 | 98.71 75 | 98.17 62 | 94.06 125 | 97.34 72 | 99.40 45 | 98.59 94 | 97.60 65 | 96.05 147 | 96.12 139 | 97.14 162 | 96.67 109 |
|
PM-MVS | | | 96.85 109 | 96.62 113 | 97.11 112 | 97.13 183 | 96.51 168 | 98.29 96 | 94.65 205 | 94.84 92 | 98.12 37 | 98.59 79 | 97.20 136 | 97.41 74 | 96.24 139 | 96.41 125 | 97.09 163 | 96.56 114 |
|
USDC | | | 96.30 125 | 95.64 134 | 97.07 114 | 97.62 161 | 96.35 177 | 97.17 162 | 95.71 184 | 95.52 69 | 99.17 6 | 98.11 96 | 97.46 130 | 95.67 121 | 95.44 165 | 93.60 184 | 97.09 163 | 92.99 184 |
|
TinyColmap | | | 96.64 120 | 96.07 124 | 97.32 103 | 97.84 149 | 96.40 172 | 97.63 130 | 96.25 166 | 95.86 53 | 98.98 12 | 97.94 99 | 96.34 150 | 96.17 111 | 97.30 93 | 95.38 161 | 97.04 165 | 93.24 179 |
|
MVS_Test | | | 95.34 148 | 94.88 149 | 95.89 159 | 96.93 188 | 96.84 165 | 96.66 176 | 97.08 142 | 90.06 184 | 94.02 176 | 97.61 107 | 96.64 145 | 93.59 157 | 92.73 202 | 94.02 179 | 97.03 166 | 96.24 120 |
|
Fast-Effi-MVS+-dtu | | | 94.34 168 | 93.26 174 | 95.62 168 | 97.82 150 | 95.97 182 | 95.86 194 | 99.01 13 | 86.88 210 | 93.39 191 | 90.83 203 | 95.46 159 | 90.61 185 | 94.46 183 | 94.68 172 | 97.01 167 | 94.51 160 |
|
PatchMatch-RL | | | 94.79 160 | 93.75 166 | 96.00 157 | 96.80 191 | 95.00 189 | 95.47 205 | 95.25 193 | 90.68 176 | 95.80 136 | 92.97 185 | 93.64 173 | 95.67 121 | 96.13 142 | 95.81 151 | 96.99 168 | 92.01 188 |
|
testgi | | | 94.81 159 | 96.05 125 | 93.35 199 | 99.06 53 | 96.87 164 | 97.57 134 | 96.70 159 | 95.77 57 | 88.60 221 | 93.19 184 | 98.87 64 | 81.21 226 | 97.03 108 | 96.64 115 | 96.97 169 | 93.99 173 |
|
MAR-MVS | | | 95.51 142 | 94.49 156 | 96.71 130 | 97.92 140 | 96.40 172 | 96.72 174 | 98.04 72 | 86.74 212 | 96.72 97 | 92.52 190 | 95.14 162 | 94.02 153 | 96.81 116 | 96.54 120 | 96.85 170 | 97.25 84 |
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 |
FPMVS | | | 94.70 161 | 94.99 148 | 94.37 189 | 95.84 209 | 93.20 200 | 96.00 192 | 91.93 217 | 95.03 86 | 94.64 165 | 94.68 164 | 93.29 174 | 90.95 181 | 98.07 66 | 97.34 90 | 96.85 170 | 93.29 177 |
|
TSAR-MVS + ACMM | | | 97.54 68 | 97.79 48 | 97.26 105 | 98.23 104 | 98.10 93 | 97.71 124 | 97.88 84 | 95.97 47 | 95.57 146 | 98.71 77 | 98.57 96 | 97.36 76 | 97.74 73 | 96.81 108 | 96.83 172 | 98.59 23 |
|
PMMVS | | | 91.67 198 | 91.47 194 | 91.91 213 | 89.43 235 | 88.61 230 | 94.99 215 | 85.67 229 | 87.50 207 | 93.80 181 | 94.42 172 | 94.88 163 | 90.71 184 | 92.26 205 | 92.96 191 | 96.83 172 | 89.65 199 |
|
V42 | | | 97.10 101 | 96.97 94 | 97.26 105 | 97.64 158 | 97.60 134 | 98.45 87 | 95.99 172 | 94.44 113 | 97.35 71 | 99.40 45 | 98.63 91 | 97.34 78 | 96.33 135 | 96.38 126 | 96.82 174 | 96.00 126 |
|
pmmvs4 | | | 95.37 147 | 94.25 157 | 96.67 134 | 97.01 186 | 95.28 188 | 97.60 131 | 96.07 170 | 93.11 146 | 97.29 74 | 98.09 97 | 94.23 171 | 95.21 128 | 91.56 209 | 93.91 181 | 96.82 174 | 93.59 176 |
|
ambc | | | | 96.78 108 | | 99.01 55 | 97.11 157 | 95.73 199 | | 95.91 50 | 99.25 2 | 98.56 81 | 97.17 137 | 97.04 86 | 96.76 118 | 95.22 164 | 96.72 176 | 96.73 108 |
|
gg-mvs-nofinetune | | | 94.13 173 | 93.93 161 | 94.37 189 | 97.99 131 | 95.86 183 | 95.45 208 | 99.22 9 | 97.61 18 | 95.10 155 | 99.50 33 | 84.50 194 | 81.73 225 | 95.31 166 | 94.12 177 | 96.71 177 | 90.59 194 |
|
TSAR-MVS + COLMAP | | | 96.05 131 | 95.94 128 | 96.18 151 | 97.46 171 | 96.41 171 | 97.26 158 | 95.83 179 | 94.69 96 | 95.30 149 | 98.31 88 | 96.52 147 | 94.71 137 | 95.48 164 | 94.87 168 | 96.54 178 | 95.33 141 |
|
v148 | | | 96.99 106 | 96.70 111 | 97.34 101 | 97.89 143 | 97.23 148 | 98.33 93 | 96.96 146 | 95.57 64 | 97.12 81 | 98.99 61 | 99.40 13 | 97.23 81 | 96.22 140 | 95.45 158 | 96.50 179 | 94.02 172 |
|
test0.0.03 1 | | | 91.17 202 | 91.50 193 | 90.80 219 | 98.01 129 | 95.46 186 | 94.22 219 | 95.80 180 | 86.55 216 | 81.75 236 | 90.83 203 | 87.93 190 | 78.48 229 | 94.51 182 | 94.11 178 | 96.50 179 | 91.08 192 |
|
MIMVSNet | | | 93.68 181 | 93.96 160 | 93.35 199 | 97.82 150 | 96.08 181 | 96.34 184 | 98.46 31 | 91.28 168 | 86.67 230 | 94.95 160 | 94.87 164 | 84.39 221 | 94.53 178 | 94.65 173 | 96.45 181 | 91.34 191 |
|
CR-MVSNet | | | 91.94 194 | 88.50 205 | 95.94 158 | 96.14 201 | 92.08 209 | 95.23 212 | 98.47 28 | 84.30 225 | 96.44 108 | 94.58 166 | 75.57 219 | 92.92 160 | 90.22 217 | 92.22 194 | 96.43 182 | 90.56 195 |
|
RPMNet | | | 90.52 205 | 86.27 221 | 95.48 172 | 95.95 206 | 92.08 209 | 95.55 204 | 98.12 65 | 84.30 225 | 95.60 145 | 87.49 214 | 72.78 224 | 91.24 177 | 87.93 221 | 89.34 208 | 96.41 183 | 89.98 198 |
|
HyFIR lowres test | | | 95.05 153 | 93.54 168 | 96.81 127 | 97.81 152 | 96.88 162 | 98.18 98 | 97.46 117 | 94.28 118 | 94.98 159 | 96.57 130 | 92.89 177 | 96.15 112 | 90.90 214 | 91.87 199 | 96.28 184 | 91.35 190 |
|
GA-MVS | | | 94.18 172 | 92.98 175 | 95.58 169 | 97.36 175 | 96.42 170 | 96.21 190 | 95.86 176 | 90.29 179 | 95.08 156 | 96.19 137 | 85.37 193 | 92.82 163 | 94.01 190 | 94.14 176 | 96.16 185 | 94.41 164 |
|
EPNet | | | 94.33 171 | 93.52 169 | 95.27 176 | 98.81 70 | 94.71 192 | 96.77 172 | 98.20 56 | 88.12 198 | 96.53 105 | 92.53 189 | 91.19 184 | 85.25 220 | 95.22 168 | 95.26 163 | 96.09 186 | 97.63 65 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
no-one | | | 97.16 99 | 97.57 62 | 96.68 133 | 96.30 200 | 95.74 184 | 98.40 91 | 94.04 212 | 96.28 37 | 96.30 118 | 97.95 98 | 99.45 10 | 99.06 4 | 96.93 113 | 98.19 60 | 95.99 187 | 98.48 30 |
|
gm-plane-assit | | | 91.85 196 | 87.91 209 | 96.44 146 | 99.14 44 | 98.25 77 | 99.02 33 | 97.38 131 | 95.57 64 | 98.31 29 | 99.34 49 | 51.00 241 | 88.93 199 | 93.16 197 | 91.57 200 | 95.85 188 | 86.50 216 |
|
new-patchmatchnet | | | 94.48 163 | 94.02 159 | 95.02 181 | 97.51 169 | 95.00 189 | 95.68 200 | 94.26 211 | 97.32 21 | 95.73 138 | 99.60 21 | 98.22 112 | 91.30 176 | 94.13 188 | 84.41 217 | 95.65 189 | 89.45 201 |
|
CANet_DTU | | | 94.96 155 | 94.62 154 | 95.35 173 | 98.03 126 | 96.11 180 | 96.92 168 | 95.60 186 | 88.59 192 | 97.27 75 | 95.27 154 | 96.50 148 | 88.77 201 | 95.53 161 | 95.59 155 | 95.54 190 | 94.78 154 |
|
FMVSNet5 | | | 89.65 212 | 87.60 212 | 92.04 212 | 95.63 213 | 96.61 167 | 94.82 217 | 94.75 201 | 80.11 234 | 87.72 227 | 77.73 235 | 73.81 223 | 83.81 222 | 95.64 158 | 96.08 141 | 95.49 191 | 93.21 180 |
|
TAMVS | | | 92.46 187 | 93.34 171 | 91.44 216 | 97.03 185 | 93.84 198 | 94.68 218 | 90.60 220 | 90.44 178 | 85.31 231 | 97.14 120 | 93.03 176 | 85.78 216 | 94.34 185 | 93.67 183 | 95.22 192 | 90.93 193 |
|
MS-PatchMatch | | | 94.84 158 | 94.76 150 | 94.94 182 | 96.38 199 | 94.69 193 | 95.90 193 | 94.03 213 | 92.49 153 | 93.81 180 | 95.79 146 | 96.38 149 | 94.54 139 | 94.70 175 | 94.85 169 | 94.97 193 | 94.43 163 |
|
CMPMVS | | 71.81 19 | 92.34 191 | 92.85 176 | 91.75 214 | 92.70 229 | 90.43 222 | 88.84 234 | 88.56 223 | 85.87 219 | 94.35 170 | 90.98 201 | 95.89 157 | 91.14 178 | 96.14 141 | 94.83 170 | 94.93 194 | 95.78 132 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
diffmvs | | | 94.34 168 | 93.83 165 | 94.93 183 | 96.41 198 | 94.88 191 | 96.41 181 | 96.09 169 | 93.24 141 | 93.79 182 | 98.12 95 | 92.20 181 | 91.98 173 | 90.79 215 | 92.20 196 | 94.91 195 | 95.35 140 |
|
CVMVSNet | | | 94.01 176 | 94.25 157 | 93.73 196 | 94.36 223 | 92.44 205 | 97.45 145 | 88.56 223 | 95.59 62 | 93.06 199 | 98.88 64 | 90.03 188 | 94.84 133 | 94.08 189 | 93.45 185 | 94.09 196 | 95.31 142 |
|
MDA-MVSNet-bldmvs | | | 95.45 144 | 95.20 140 | 95.74 164 | 94.24 224 | 96.38 175 | 97.93 113 | 94.80 200 | 95.56 67 | 96.87 92 | 98.29 89 | 95.24 161 | 96.50 103 | 98.65 44 | 90.38 205 | 94.09 196 | 91.93 189 |
|
IterMVS | | | 94.48 163 | 93.46 170 | 95.66 166 | 97.52 164 | 96.43 169 | 97.20 160 | 94.73 203 | 92.91 151 | 96.44 108 | 98.75 75 | 91.10 185 | 94.53 140 | 92.10 206 | 90.10 207 | 93.51 198 | 92.84 185 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
testmv | | | 92.35 190 | 92.53 180 | 92.13 210 | 97.16 181 | 92.68 204 | 96.31 187 | 94.61 208 | 86.68 214 | 88.16 224 | 97.27 117 | 97.09 141 | 83.28 223 | 94.52 180 | 93.39 187 | 93.26 199 | 86.10 218 |
|
test1235678 | | | 92.36 189 | 92.55 178 | 92.13 210 | 97.16 181 | 92.69 203 | 96.32 186 | 94.62 206 | 86.69 213 | 88.16 224 | 97.28 116 | 97.13 140 | 83.28 223 | 94.54 177 | 93.40 186 | 93.26 199 | 86.11 217 |
|
new_pmnet | | | 90.85 204 | 92.26 185 | 89.21 225 | 93.68 227 | 89.05 228 | 93.20 228 | 84.16 232 | 92.99 149 | 84.25 232 | 97.72 105 | 94.60 166 | 86.80 213 | 93.20 196 | 91.30 201 | 93.21 201 | 86.94 215 |
|
test-mter | | | 89.16 214 | 88.14 208 | 90.37 220 | 94.79 220 | 91.05 219 | 93.60 225 | 85.26 230 | 81.65 230 | 88.32 223 | 92.22 191 | 79.35 217 | 87.03 211 | 92.28 203 | 90.12 206 | 93.19 202 | 90.29 197 |
|
EU-MVSNet | | | 96.03 132 | 96.23 119 | 95.80 163 | 95.48 216 | 94.18 194 | 98.99 39 | 91.51 218 | 97.22 22 | 97.66 54 | 99.15 57 | 98.51 99 | 98.08 32 | 95.92 151 | 92.88 192 | 93.09 203 | 95.72 135 |
|
GG-mvs-BLEND | | | 61.03 232 | 87.02 216 | 30.71 234 | 0.74 240 | 90.01 223 | 78.90 238 | 0.74 238 | 84.56 223 | 9.46 241 | 79.17 233 | 90.69 187 | 1.37 238 | 91.74 208 | 89.13 211 | 93.04 204 | 83.83 226 |
|
testus | | | 90.01 207 | 90.03 200 | 89.98 221 | 95.89 207 | 91.43 217 | 93.88 222 | 89.30 222 | 83.54 227 | 89.68 214 | 87.81 213 | 94.62 165 | 78.31 230 | 92.87 199 | 92.01 197 | 92.85 205 | 87.91 210 |
|
EPNet_dtu | | | 93.45 182 | 92.51 181 | 94.55 188 | 98.39 88 | 91.67 215 | 95.46 206 | 97.50 109 | 86.56 215 | 97.38 69 | 93.52 179 | 94.20 172 | 85.82 215 | 93.31 195 | 92.53 193 | 92.72 206 | 95.76 133 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
test-LLR | | | 89.77 211 | 87.47 213 | 92.45 207 | 98.01 129 | 89.77 224 | 93.25 226 | 95.80 180 | 81.56 231 | 89.19 216 | 92.08 193 | 79.59 215 | 85.77 218 | 91.47 211 | 89.04 212 | 92.69 207 | 88.75 204 |
|
TESTMET0.1,1 | | | 88.60 219 | 87.47 213 | 89.93 223 | 94.23 225 | 89.77 224 | 93.25 226 | 84.47 231 | 81.56 231 | 89.19 216 | 92.08 193 | 79.59 215 | 85.77 218 | 91.47 211 | 89.04 212 | 92.69 207 | 88.75 204 |
|
PatchT | | | 91.40 200 | 88.54 204 | 94.74 184 | 91.48 234 | 92.18 208 | 97.42 147 | 97.51 107 | 84.96 222 | 96.44 108 | 94.16 173 | 75.47 220 | 92.92 160 | 90.22 217 | 92.22 194 | 92.66 209 | 90.56 195 |
|
test2356 | | | 85.48 228 | 81.66 230 | 89.94 222 | 95.36 218 | 88.71 229 | 91.69 230 | 92.78 215 | 78.28 236 | 86.79 229 | 85.80 217 | 58.29 235 | 80.44 227 | 89.39 219 | 89.17 210 | 92.60 210 | 81.98 230 |
|
pmmvs3 | | | 91.20 201 | 91.40 195 | 90.96 218 | 91.71 233 | 91.08 218 | 95.41 209 | 81.34 233 | 87.36 208 | 94.57 166 | 95.02 158 | 94.30 170 | 90.42 186 | 94.28 186 | 89.26 209 | 92.30 211 | 88.49 207 |
|
CHOSEN 1792x2688 | | | 94.98 154 | 94.69 151 | 95.31 174 | 97.27 179 | 95.58 185 | 97.90 116 | 95.56 187 | 95.03 86 | 93.77 183 | 95.65 148 | 99.29 18 | 95.30 125 | 91.51 210 | 91.28 202 | 92.05 212 | 94.50 161 |
|
LP | | | 92.03 192 | 90.19 199 | 94.17 193 | 94.52 222 | 93.87 197 | 96.79 171 | 95.05 195 | 93.58 135 | 95.62 143 | 95.68 147 | 83.37 202 | 91.78 174 | 90.73 216 | 86.99 214 | 91.27 213 | 87.09 214 |
|
PMMVS2 | | | 86.47 227 | 92.62 177 | 79.29 231 | 92.01 230 | 85.63 233 | 93.74 224 | 86.37 226 | 93.95 129 | 54.18 240 | 98.19 92 | 97.39 132 | 58.46 234 | 96.57 125 | 93.07 189 | 90.99 214 | 83.55 227 |
|
MDTV_nov1_ep13_2view | | | 94.39 165 | 93.34 171 | 95.63 167 | 97.23 180 | 95.33 187 | 97.76 122 | 96.84 153 | 94.55 102 | 97.47 64 | 98.96 62 | 97.70 125 | 93.88 154 | 92.27 204 | 86.81 215 | 90.56 215 | 87.73 211 |
|
MDTV_nov1_ep13 | | | 90.30 206 | 87.32 215 | 93.78 195 | 96.00 204 | 92.97 201 | 95.46 206 | 95.39 190 | 88.61 191 | 95.41 147 | 94.45 171 | 80.39 214 | 89.87 193 | 86.58 224 | 83.54 221 | 90.56 215 | 84.71 221 |
|
DWT-MVSNet_training | | | 86.69 225 | 81.24 231 | 93.05 202 | 95.31 219 | 92.06 211 | 95.75 198 | 91.51 218 | 84.32 224 | 94.49 167 | 83.46 222 | 55.37 238 | 90.81 183 | 82.76 232 | 83.19 223 | 90.45 217 | 87.52 212 |
|
dps | | | 88.36 220 | 84.32 227 | 93.07 201 | 93.86 226 | 92.29 207 | 94.89 216 | 95.93 174 | 83.50 228 | 93.13 196 | 91.87 195 | 67.79 230 | 90.32 188 | 85.99 226 | 83.22 222 | 90.28 218 | 85.56 219 |
|
CostFormer | | | 89.06 215 | 85.65 223 | 93.03 204 | 95.88 208 | 92.40 206 | 95.30 211 | 95.86 176 | 86.49 217 | 93.12 198 | 93.40 182 | 74.18 222 | 88.25 204 | 82.99 231 | 81.46 225 | 89.77 219 | 88.66 206 |
|
tpmp4_e23 | | | 88.68 217 | 84.61 225 | 93.43 197 | 96.00 204 | 91.46 216 | 95.40 210 | 96.60 164 | 87.71 200 | 94.67 164 | 88.54 210 | 69.81 227 | 88.41 203 | 85.50 227 | 81.08 227 | 89.52 220 | 88.18 209 |
|
N_pmnet | | | 92.46 187 | 92.38 182 | 92.55 206 | 97.91 141 | 93.47 199 | 97.42 147 | 94.01 214 | 96.40 34 | 88.48 222 | 98.50 82 | 98.07 116 | 88.14 205 | 91.04 213 | 84.30 218 | 89.35 221 | 84.85 220 |
|
EPMVS | | | 89.28 213 | 86.28 220 | 92.79 205 | 96.01 203 | 92.00 212 | 95.83 195 | 95.85 178 | 90.78 175 | 91.00 208 | 94.58 166 | 74.65 221 | 88.93 199 | 85.00 228 | 82.88 224 | 89.09 222 | 84.09 224 |
|
1111 | | | 88.65 218 | 87.69 211 | 89.78 224 | 98.84 66 | 94.02 195 | 95.79 196 | 98.19 58 | 91.57 163 | 82.27 233 | 98.19 92 | 53.19 239 | 74.80 231 | 94.98 172 | 93.04 190 | 88.80 223 | 88.82 203 |
|
PatchmatchNet | | | 89.98 208 | 86.23 222 | 94.36 191 | 96.56 196 | 91.90 214 | 96.07 191 | 96.72 157 | 90.18 181 | 96.87 92 | 93.36 183 | 78.06 218 | 91.46 175 | 84.71 230 | 81.40 226 | 88.45 224 | 83.97 225 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
ADS-MVSNet | | | 89.89 209 | 87.70 210 | 92.43 208 | 95.52 214 | 90.91 220 | 95.57 201 | 95.33 191 | 93.19 143 | 91.21 207 | 93.41 181 | 82.12 207 | 89.05 197 | 86.21 225 | 83.77 220 | 87.92 225 | 84.31 222 |
|
MVS-HIRNet | | | 88.72 216 | 86.49 219 | 91.33 217 | 91.81 232 | 85.66 232 | 87.02 236 | 96.25 166 | 81.48 233 | 94.82 161 | 96.31 136 | 92.14 182 | 90.32 188 | 87.60 222 | 83.82 219 | 87.74 226 | 78.42 231 |
|
test12356 | | | 88.21 221 | 89.73 201 | 86.43 229 | 91.94 231 | 89.52 227 | 91.79 229 | 86.07 228 | 85.51 220 | 81.97 235 | 95.56 150 | 96.20 152 | 79.11 228 | 94.14 187 | 90.94 203 | 87.70 227 | 76.23 232 |
|
CHOSEN 280x420 | | | 91.55 199 | 90.27 198 | 93.05 202 | 94.61 221 | 88.01 231 | 96.56 178 | 94.62 206 | 88.04 199 | 94.20 172 | 92.66 188 | 86.60 191 | 90.82 182 | 95.06 171 | 91.89 198 | 87.49 228 | 89.61 200 |
|
tpm cat1 | | | 87.19 223 | 82.78 229 | 92.33 209 | 95.66 211 | 90.61 221 | 94.19 221 | 95.27 192 | 86.97 209 | 94.38 169 | 90.91 202 | 69.40 229 | 87.21 209 | 79.57 234 | 77.82 230 | 87.25 229 | 84.18 223 |
|
tpm | | | 89.84 210 | 86.81 218 | 93.36 198 | 96.60 195 | 91.92 213 | 95.02 214 | 97.39 130 | 86.79 211 | 96.54 104 | 95.03 157 | 69.70 228 | 87.66 207 | 88.79 220 | 86.19 216 | 86.95 230 | 89.27 202 |
|
E-PMN | | | 86.94 224 | 85.10 224 | 89.09 227 | 95.77 210 | 83.54 235 | 89.89 232 | 86.55 225 | 92.18 157 | 87.34 228 | 94.02 174 | 83.42 201 | 89.63 195 | 93.32 194 | 77.11 231 | 85.33 231 | 72.09 233 |
|
EMVS | | | 86.63 226 | 84.48 226 | 89.15 226 | 95.51 215 | 83.66 234 | 90.19 231 | 86.14 227 | 91.78 161 | 88.68 220 | 93.83 178 | 81.97 210 | 89.05 197 | 92.76 201 | 76.09 232 | 85.31 232 | 71.28 234 |
|
tpmrst | | | 87.60 222 | 84.13 228 | 91.66 215 | 95.65 212 | 89.73 226 | 93.77 223 | 94.74 202 | 88.85 189 | 93.35 193 | 95.60 149 | 72.37 226 | 87.40 208 | 81.24 233 | 78.19 229 | 85.02 233 | 82.90 229 |
|
testpf | | | 81.59 230 | 76.31 232 | 87.75 228 | 93.50 228 | 83.16 236 | 89.19 233 | 95.94 173 | 73.85 237 | 90.39 209 | 80.32 231 | 61.17 234 | 73.99 233 | 76.52 235 | 75.82 233 | 83.50 234 | 83.33 228 |
|
DeepMVS_CX | | | | | | | 72.99 237 | 80.14 237 | 37.34 234 | 83.46 229 | 60.13 239 | 84.40 219 | 85.48 192 | 86.93 212 | 87.22 223 | | 79.61 235 | 87.32 213 |
|
MVE | | 72.99 18 | 85.37 229 | 89.43 202 | 80.63 230 | 74.43 236 | 71.94 238 | 88.25 235 | 89.81 221 | 93.27 140 | 67.32 238 | 96.32 135 | 91.83 183 | 90.40 187 | 93.36 193 | 90.79 204 | 73.55 236 | 88.49 207 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | | | 45.72 233 | 60.00 237 | 38.74 239 | 45.50 239 | 12.18 235 | 79.58 235 | 68.42 237 | 67.62 236 | 65.04 231 | 22.12 235 | 84.83 229 | 78.72 228 | 66.08 237 | |
|
.test1245 | | | 69.06 231 | 63.57 233 | 75.47 232 | 98.84 66 | 94.02 195 | 95.79 196 | 98.19 58 | 91.57 163 | 82.27 233 | 98.19 92 | 53.19 239 | 74.80 231 | 94.98 172 | 5.51 235 | 2.94 238 | 7.51 235 |
|
testmvs | | | 4.99 233 | 6.88 234 | 2.78 236 | 1.73 238 | 2.04 241 | 3.10 241 | 1.71 236 | 7.27 238 | 3.92 243 | 12.18 237 | 6.71 242 | 3.31 237 | 6.94 236 | 5.51 235 | 2.94 238 | 7.51 235 |
|
test123 | | | 4.41 234 | 5.71 235 | 2.88 235 | 1.28 239 | 2.21 240 | 3.09 242 | 1.65 237 | 6.35 239 | 4.98 242 | 8.53 238 | 3.88 243 | 3.46 236 | 5.79 237 | 5.71 234 | 2.85 240 | 7.50 237 |
|
sosnet-low-res | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
sosnet | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 244 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
MTAPA | | | | | | | | | | | 97.43 67 | | 99.27 22 | | | | | |
|
MTMP | | | | | | | | | | | 97.63 55 | | 99.03 45 | | | | | |
|
Patchmatch-RL test | | | | | | | | 17.42 240 | | | | | | | | | | |
|
mPP-MVS | | | | | | 99.58 6 | | | | | | | 98.98 50 | | | | | |
|
NP-MVS | | | | | | | | | | 89.27 188 | | | | | | | | |
|
Patchmtry | | | | | | | 92.70 202 | 95.23 212 | 98.47 28 | | 96.44 108 | | | | | | | |
|