UA-Net | | | 98.66 20 | 98.60 24 | 98.73 19 | 99.83 1 | 99.28 9 | 98.56 83 | 99.24 8 | 96.04 44 | 97.12 81 | 98.44 86 | 98.95 57 | 98.17 30 | 99.15 24 | 99.00 19 | 99.48 18 | 99.33 2 |
|
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 26 | 98.52 22 | 99.36 49 | 98.98 51 | 98.57 17 | 99.49 9 | 99.23 13 | 99.56 10 | 98.55 24 |
|
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 36 | 98.83 16 | 99.46 39 | 99.09 36 | 98.62 14 | 99.51 7 | 99.36 8 | 99.63 3 | 98.97 7 |
|
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 29 | 98.65 20 | 99.43 42 | 99.33 17 | 98.47 21 | 99.50 8 | 99.32 9 | 99.60 5 | 98.79 12 |
|
CP-MVSNet | | | 98.91 14 | 98.61 22 | 99.25 4 | 99.63 5 | 99.50 7 | 99.55 10 | 99.36 5 | 95.53 69 | 98.77 18 | 99.11 59 | 98.64 90 | 98.57 17 | 99.42 11 | 99.28 11 | 99.61 4 | 98.78 15 |
|
zzz-MVS | | | 98.14 36 | 97.78 52 | 98.55 26 | 99.58 6 | 98.58 58 | 98.98 42 | 98.48 27 | 95.98 47 | 97.39 68 | 94.73 164 | 99.27 22 | 97.98 41 | 98.81 33 | 98.64 37 | 98.90 50 | 98.46 31 |
|
ACMMPR | | | 98.31 26 | 98.07 38 | 98.60 24 | 99.58 6 | 98.83 27 | 99.09 30 | 98.48 27 | 96.25 39 | 97.03 85 | 96.81 125 | 99.09 36 | 98.39 24 | 98.55 49 | 98.45 43 | 99.01 36 | 98.53 28 |
|
mPP-MVS | | | | | | 99.58 6 | | | | | | | 98.98 51 | | | | | |
|
MP-MVS | | | 97.98 48 | 97.53 66 | 98.50 29 | 99.56 9 | 98.58 58 | 98.97 44 | 98.39 37 | 93.49 137 | 97.14 78 | 96.08 141 | 99.23 28 | 98.06 33 | 98.50 52 | 98.38 47 | 98.90 50 | 98.44 33 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
WR-MVS_H | | | 98.97 12 | 98.82 17 | 99.14 8 | 99.56 9 | 99.56 4 | 99.54 11 | 99.42 3 | 96.07 43 | 98.37 27 | 99.34 50 | 99.09 36 | 98.43 22 | 99.45 10 | 99.41 6 | 99.53 11 | 98.86 11 |
|
PGM-MVS | | | 97.82 59 | 97.25 72 | 98.48 31 | 99.54 11 | 98.75 43 | 99.02 34 | 98.35 42 | 92.41 155 | 96.84 96 | 95.39 153 | 98.99 49 | 98.24 27 | 98.43 53 | 98.34 50 | 98.90 50 | 98.41 35 |
|
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 44 | 98.88 7 | 99.54 6 | 99.49 3 | 99.81 1 | 98.87 10 |
|
SteuartSystems-ACMMP | | | 98.06 41 | 97.78 52 | 98.39 35 | 99.54 11 | 98.79 32 | 98.94 49 | 98.42 35 | 93.98 128 | 95.85 134 | 96.66 130 | 99.25 25 | 98.61 15 | 98.71 40 | 98.38 47 | 98.97 43 | 98.67 21 |
Skip Steuart: Steuart Systems R&D Blog. |
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 56 | 99.03 5 | 99.69 3 | 99.58 2 | 99.32 24 | 99.06 6 |
|
ACMMP | | | 97.99 47 | 97.60 61 | 98.45 33 | 99.53 14 | 98.83 27 | 99.13 29 | 98.30 45 | 94.57 102 | 96.39 117 | 95.32 154 | 98.95 57 | 98.37 25 | 98.61 47 | 98.47 40 | 99.00 38 | 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 |
ACMM | | 94.29 11 | 98.12 38 | 97.71 58 | 98.59 25 | 99.51 16 | 98.58 58 | 99.24 21 | 98.25 51 | 96.22 41 | 96.90 90 | 95.01 160 | 98.89 62 | 98.52 20 | 98.66 44 | 98.32 53 | 99.13 30 | 98.28 41 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
X-MVS | | | 97.60 66 | 97.00 92 | 98.29 38 | 99.50 17 | 98.76 39 | 98.90 53 | 98.37 39 | 94.67 99 | 96.40 113 | 91.47 198 | 98.78 76 | 97.60 66 | 98.55 49 | 98.50 39 | 98.96 45 | 98.29 38 |
|
XVS | | | | | | 99.48 18 | 98.76 39 | 99.22 24 | | | 96.40 113 | | 98.78 76 | | | | 98.94 48 | |
|
X-MVStestdata | | | | | | 99.48 18 | 98.76 39 | 99.22 24 | | | 96.40 113 | | 98.78 76 | | | | 98.94 48 | |
|
CP-MVS | | | 98.00 45 | 97.57 63 | 98.50 29 | 99.47 20 | 98.56 61 | 98.91 52 | 98.38 38 | 94.71 96 | 97.01 86 | 95.20 156 | 99.06 41 | 98.20 28 | 98.61 47 | 98.46 42 | 99.02 34 | 98.40 36 |
|
SMA-MVS | | | 98.22 31 | 98.31 29 | 98.11 48 | 99.46 21 | 98.77 34 | 98.34 93 | 97.92 79 | 95.27 80 | 96.97 88 | 98.82 70 | 99.39 14 | 97.10 85 | 98.69 41 | 98.47 40 | 98.84 58 | 98.77 16 |
|
APDe-MVS | | | 98.29 27 | 98.42 27 | 98.14 45 | 99.45 22 | 98.90 22 | 99.18 27 | 98.30 45 | 95.96 49 | 95.13 154 | 98.79 72 | 99.25 25 | 97.92 44 | 98.80 34 | 98.71 34 | 98.85 56 | 98.54 25 |
|
LGP-MVS_train | | | 97.96 52 | 97.53 66 | 98.45 33 | 99.45 22 | 98.64 54 | 99.09 30 | 98.27 50 | 92.99 150 | 96.04 129 | 96.57 131 | 99.29 18 | 98.66 12 | 98.73 36 | 98.42 45 | 99.19 28 | 98.09 45 |
|
ACMP | | 94.03 12 | 97.97 51 | 97.61 60 | 98.39 35 | 99.43 24 | 98.51 65 | 98.97 44 | 98.06 71 | 94.63 100 | 96.10 127 | 96.12 140 | 99.20 30 | 98.63 13 | 98.68 42 | 98.20 60 | 99.14 29 | 97.93 51 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
MIMVSNet1 | | | 98.22 31 | 98.51 25 | 97.87 74 | 99.40 25 | 98.82 29 | 99.31 18 | 98.53 25 | 97.39 20 | 96.59 104 | 99.31 52 | 99.23 28 | 94.76 137 | 98.93 30 | 98.67 35 | 98.63 69 | 97.25 85 |
|
DeepC-MVS | | 96.08 5 | 98.58 21 | 98.49 26 | 98.68 21 | 99.37 26 | 98.52 64 | 99.01 38 | 98.17 63 | 97.17 23 | 98.25 31 | 99.56 25 | 99.62 5 | 98.29 26 | 98.40 55 | 98.09 64 | 98.97 43 | 98.08 46 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
HFP-MVS | | | 98.17 33 | 98.02 39 | 98.35 37 | 99.36 27 | 98.62 55 | 98.79 61 | 98.46 32 | 96.24 40 | 96.53 106 | 97.13 122 | 98.98 51 | 98.02 35 | 98.20 63 | 98.42 45 | 98.95 47 | 98.54 25 |
|
HSP-MVS | | | 97.44 81 | 97.13 84 | 97.79 79 | 99.34 28 | 98.99 20 | 99.23 22 | 98.12 66 | 93.43 139 | 95.95 130 | 97.45 112 | 99.50 9 | 96.44 106 | 96.35 133 | 95.33 163 | 97.65 126 | 98.89 9 |
|
ACMMP_Plus | | | 98.12 38 | 98.08 37 | 98.18 43 | 99.34 28 | 98.74 44 | 98.97 44 | 98.00 75 | 95.13 84 | 96.90 90 | 97.54 111 | 99.27 22 | 97.18 83 | 98.72 38 | 98.45 43 | 98.68 67 | 98.69 19 |
|
TranMVSNet+NR-MVSNet | | | 98.45 22 | 98.22 33 | 98.72 20 | 99.32 30 | 99.06 14 | 98.99 40 | 98.89 14 | 95.52 70 | 97.53 61 | 99.42 44 | 98.83 70 | 98.01 36 | 98.55 49 | 98.34 50 | 99.57 8 | 97.80 56 |
|
APD-MVS | | | 97.47 79 | 97.16 79 | 97.84 76 | 99.32 30 | 98.39 73 | 98.47 87 | 98.21 56 | 92.08 160 | 95.23 151 | 96.68 129 | 98.90 61 | 96.99 88 | 98.20 63 | 98.21 57 | 98.80 59 | 97.67 60 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DU-MVS | | | 98.23 28 | 97.74 56 | 98.81 16 | 99.23 32 | 98.77 34 | 98.76 64 | 98.88 15 | 94.10 122 | 98.50 23 | 98.87 67 | 98.32 107 | 97.99 39 | 98.40 55 | 98.08 71 | 99.49 17 | 97.64 62 |
|
Baseline_NR-MVSNet | | | 98.17 33 | 97.90 43 | 98.48 31 | 99.23 32 | 98.59 57 | 98.83 59 | 98.73 22 | 93.97 129 | 96.95 89 | 99.66 12 | 98.23 112 | 97.90 45 | 98.40 55 | 99.06 17 | 99.25 27 | 97.42 77 |
|
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 72 | 99.75 1 | 99.63 1 | 99.80 2 | 99.19 3 |
|
ESAPD | | | 97.71 62 | 97.79 49 | 97.62 88 | 99.21 35 | 98.80 31 | 98.31 96 | 98.30 45 | 93.60 135 | 94.74 163 | 97.94 100 | 99.24 27 | 96.58 99 | 98.42 54 | 98.27 55 | 98.56 72 | 98.28 41 |
|
CPTT-MVS | | | 97.08 103 | 96.25 119 | 98.05 60 | 99.21 35 | 98.30 76 | 98.54 84 | 97.98 76 | 94.28 119 | 95.89 133 | 89.57 210 | 98.54 98 | 98.18 29 | 97.82 71 | 97.32 92 | 98.54 74 | 97.91 53 |
|
LS3D | | | 97.93 54 | 97.80 48 | 98.08 55 | 99.20 37 | 98.77 34 | 98.89 55 | 97.92 79 | 96.59 31 | 96.99 87 | 96.71 128 | 97.14 140 | 96.39 107 | 99.04 26 | 98.96 22 | 99.10 33 | 97.39 78 |
|
test20.03 | | | 96.08 131 | 96.80 108 | 95.25 179 | 99.19 38 | 97.58 136 | 97.24 161 | 97.56 106 | 94.95 90 | 91.91 206 | 98.58 81 | 98.03 118 | 87.88 207 | 97.43 88 | 96.94 102 | 97.69 123 | 94.05 172 |
|
HPM-MVS++ | | | 97.56 68 | 97.11 86 | 98.09 50 | 99.18 39 | 97.95 108 | 98.57 81 | 98.20 57 | 94.08 124 | 97.25 76 | 95.96 145 | 98.81 73 | 97.13 84 | 97.51 84 | 97.30 95 | 98.21 101 | 98.15 44 |
|
LTVRE_ROB | | 97.71 1 | 99.33 2 | 99.47 2 | 99.16 7 | 99.16 40 | 99.11 11 | 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 13 | 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 |
MVS_0304 | | | 97.18 99 | 96.84 106 | 97.58 91 | 99.15 41 | 98.19 81 | 98.11 103 | 97.81 90 | 92.36 156 | 98.06 42 | 97.43 113 | 99.06 41 | 94.24 147 | 96.80 118 | 96.54 121 | 98.12 105 | 97.52 71 |
|
UniMVSNet (Re) | | | 98.23 28 | 97.85 46 | 98.67 22 | 99.15 41 | 98.87 24 | 98.74 73 | 98.84 17 | 94.27 121 | 97.94 48 | 99.01 61 | 98.39 104 | 97.82 49 | 98.35 60 | 98.29 54 | 99.51 16 | 97.78 57 |
|
PVSNet_Blended_VisFu | | | 97.44 81 | 97.14 81 | 97.79 79 | 99.15 41 | 98.44 70 | 98.32 95 | 97.66 99 | 93.74 134 | 97.73 52 | 98.79 72 | 96.93 144 | 95.64 124 | 97.69 76 | 96.91 104 | 98.25 98 | 97.50 73 |
|
gm-plane-assit | | | 91.85 197 | 87.91 210 | 96.44 147 | 99.14 44 | 98.25 78 | 99.02 34 | 97.38 132 | 95.57 65 | 98.31 29 | 99.34 50 | 51.00 242 | 88.93 200 | 93.16 198 | 91.57 201 | 95.85 189 | 86.50 217 |
|
COLMAP_ROB | | 96.84 2 | 98.75 17 | 98.82 17 | 98.66 23 | 99.14 44 | 98.79 32 | 99.30 19 | 97.67 98 | 98.33 8 | 97.82 50 | 99.20 56 | 99.18 32 | 98.76 9 | 99.27 18 | 98.96 22 | 99.29 26 | 98.03 47 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
CDPH-MVS | | | 96.68 118 | 95.99 127 | 97.48 98 | 99.13 46 | 97.64 133 | 98.08 104 | 97.46 118 | 90.56 178 | 95.13 154 | 94.87 162 | 98.27 109 | 96.56 101 | 97.09 102 | 96.45 124 | 98.54 74 | 97.08 92 |
|
UniMVSNet_NR-MVSNet | | | 98.12 38 | 97.56 65 | 98.78 17 | 99.13 46 | 98.89 23 | 98.76 64 | 98.78 19 | 93.81 132 | 98.50 23 | 98.81 71 | 97.64 129 | 97.99 39 | 98.18 66 | 97.92 76 | 99.53 11 | 97.64 62 |
|
OPM-MVS | | | 98.01 43 | 98.01 40 | 98.00 64 | 99.11 48 | 98.12 91 | 98.68 77 | 97.72 96 | 96.65 30 | 96.68 102 | 98.40 88 | 99.28 21 | 97.44 74 | 98.20 63 | 97.82 82 | 98.40 90 | 97.58 67 |
|
CSCG | | | 98.45 22 | 98.61 22 | 98.26 39 | 99.11 48 | 99.06 14 | 98.17 101 | 97.49 113 | 97.93 15 | 97.37 70 | 98.88 65 | 99.29 18 | 98.10 31 | 98.40 55 | 97.51 84 | 99.32 24 | 99.16 4 |
|
CANet | | | 96.81 112 | 96.50 115 | 97.17 112 | 99.10 50 | 97.96 106 | 97.86 121 | 97.51 108 | 91.30 168 | 97.75 51 | 97.64 107 | 97.89 122 | 93.39 159 | 96.98 111 | 96.73 111 | 97.40 136 | 96.99 95 |
|
v7n | | | 99.03 7 | 99.03 9 | 99.02 10 | 99.09 51 | 99.11 11 | 99.57 9 | 98.82 18 | 98.21 9 | 99.25 2 | 99.84 3 | 99.59 8 | 98.76 9 | 99.23 20 | 98.83 29 | 98.63 69 | 98.40 36 |
|
train_agg | | | 96.68 118 | 95.93 130 | 97.56 92 | 99.08 52 | 97.16 152 | 98.44 90 | 97.37 134 | 91.12 171 | 95.18 153 | 95.43 152 | 98.48 102 | 97.36 77 | 96.48 129 | 95.52 158 | 97.95 114 | 97.34 83 |
|
testgi | | | 94.81 160 | 96.05 126 | 93.35 200 | 99.06 53 | 96.87 165 | 97.57 136 | 96.70 160 | 95.77 58 | 88.60 222 | 93.19 185 | 98.87 65 | 81.21 227 | 97.03 109 | 96.64 116 | 96.97 170 | 93.99 174 |
|
NCCC | | | 96.56 123 | 95.68 133 | 97.59 90 | 99.04 54 | 97.54 141 | 97.67 126 | 97.56 106 | 94.84 93 | 96.10 127 | 87.91 213 | 98.09 115 | 96.98 89 | 97.20 97 | 96.80 110 | 98.21 101 | 97.38 81 |
|
ambc | | | | 96.78 109 | | 99.01 55 | 97.11 158 | 95.73 201 | | 95.91 51 | 99.25 2 | 98.56 82 | 97.17 138 | 97.04 87 | 96.76 119 | 95.22 165 | 96.72 177 | 96.73 109 |
|
Gipuma | | | 98.43 24 | 98.15 35 | 98.76 18 | 99.00 56 | 98.29 77 | 97.91 116 | 98.06 71 | 99.02 4 | 99.50 1 | 96.33 135 | 98.67 87 | 99.22 1 | 99.02 27 | 98.02 74 | 98.88 55 | 97.66 61 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
TDRefinement | | | 99.00 9 | 99.13 6 | 98.86 12 | 98.99 57 | 99.05 16 | 99.58 7 | 98.29 49 | 98.96 5 | 97.96 47 | 99.40 46 | 98.67 87 | 98.87 8 | 99.60 4 | 99.46 5 | 99.46 19 | 98.74 18 |
|
3Dnovator+ | | 96.20 4 | 97.58 67 | 97.14 81 | 98.10 49 | 98.98 58 | 97.85 124 | 98.60 80 | 98.33 43 | 96.41 34 | 97.23 77 | 94.66 166 | 97.26 136 | 96.91 90 | 97.91 68 | 97.87 78 | 98.53 76 | 98.03 47 |
|
ACMH+ | | 94.90 8 | 98.40 25 | 98.71 20 | 98.04 61 | 98.93 59 | 98.84 26 | 99.30 19 | 97.86 86 | 97.78 16 | 94.19 175 | 98.77 74 | 99.39 14 | 98.61 15 | 99.33 14 | 99.07 15 | 99.33 22 | 97.81 55 |
|
v52 | | | 98.98 10 | 99.10 7 | 98.85 13 | 98.91 60 | 99.03 17 | 99.41 12 | 97.77 94 | 98.12 10 | 99.07 8 | 99.84 3 | 99.60 6 | 99.15 2 | 99.29 16 | 98.99 20 | 98.79 61 | 98.79 12 |
|
V4 | | | 98.98 10 | 99.10 7 | 98.85 13 | 98.91 60 | 99.03 17 | 99.41 12 | 97.77 94 | 98.12 10 | 99.06 9 | 99.85 2 | 99.60 6 | 99.15 2 | 99.30 15 | 98.99 20 | 98.80 59 | 98.79 12 |
|
CNVR-MVS | | | 97.03 106 | 96.77 110 | 97.34 102 | 98.89 62 | 97.67 132 | 97.64 129 | 97.17 141 | 94.40 115 | 95.70 142 | 94.02 175 | 98.76 80 | 96.49 105 | 97.78 73 | 97.29 96 | 98.12 105 | 97.47 74 |
|
FC-MVSNet-test | | | 97.54 69 | 98.26 31 | 96.70 132 | 98.87 63 | 97.79 130 | 98.49 85 | 98.56 24 | 96.04 44 | 90.39 210 | 99.65 14 | 98.67 87 | 95.15 130 | 99.23 20 | 99.07 15 | 98.73 63 | 97.39 78 |
|
ACMH | | 95.26 7 | 98.75 17 | 98.93 12 | 98.54 27 | 98.86 64 | 99.01 19 | 99.58 7 | 98.10 68 | 98.67 6 | 97.30 73 | 99.18 57 | 99.42 12 | 98.40 23 | 99.19 22 | 98.86 27 | 98.99 40 | 98.19 43 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
FC-MVSNet-train | | | 97.65 64 | 98.16 34 | 97.05 117 | 98.85 65 | 98.85 25 | 99.34 16 | 98.08 69 | 94.50 110 | 94.41 169 | 99.21 55 | 98.80 74 | 92.66 165 | 98.98 29 | 98.85 28 | 98.96 45 | 97.94 50 |
|
1111 | | | 88.65 219 | 87.69 212 | 89.78 225 | 98.84 66 | 94.02 197 | 95.79 198 | 98.19 59 | 91.57 164 | 82.27 234 | 98.19 93 | 53.19 240 | 74.80 232 | 94.98 173 | 93.04 191 | 88.80 224 | 88.82 204 |
|
.test1245 | | | 69.06 232 | 63.57 234 | 75.47 233 | 98.84 66 | 94.02 197 | 95.79 198 | 98.19 59 | 91.57 164 | 82.27 234 | 98.19 93 | 53.19 240 | 74.80 232 | 94.98 173 | 5.51 236 | 2.94 239 | 7.51 236 |
|
EG-PatchMatch MVS | | | 97.98 48 | 97.92 42 | 98.04 61 | 98.84 66 | 98.04 99 | 97.90 117 | 96.83 155 | 95.07 86 | 98.79 17 | 99.07 60 | 99.37 16 | 97.88 47 | 98.74 35 | 98.16 62 | 98.01 110 | 96.96 96 |
|
Anonymous20240521 | | | 98.69 19 | 98.84 16 | 98.52 28 | 98.83 69 | 99.14 10 | 99.22 24 | 98.76 20 | 96.99 25 | 96.73 97 | 99.49 34 | 99.14 34 | 98.01 36 | 99.42 11 | 99.27 12 | 99.57 8 | 98.43 34 |
|
DeepC-MVS_fast | | 95.38 6 | 97.53 72 | 97.30 70 | 97.79 79 | 98.83 69 | 97.64 133 | 98.18 99 | 97.14 142 | 95.57 65 | 97.83 49 | 97.10 123 | 98.80 74 | 96.53 103 | 97.41 89 | 97.32 92 | 98.24 99 | 97.26 84 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
EPNet | | | 94.33 172 | 93.52 170 | 95.27 177 | 98.81 71 | 94.71 194 | 96.77 174 | 98.20 57 | 88.12 199 | 96.53 106 | 92.53 190 | 91.19 185 | 85.25 221 | 95.22 169 | 95.26 164 | 96.09 187 | 97.63 66 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
MCST-MVS | | | 96.79 114 | 96.08 124 | 97.62 88 | 98.78 72 | 97.52 142 | 98.01 110 | 97.32 137 | 93.20 143 | 95.84 135 | 93.97 177 | 98.12 114 | 97.34 79 | 96.34 134 | 95.88 151 | 98.45 86 | 97.51 72 |
|
HQP-MVS | | | 95.97 135 | 95.01 148 | 97.08 114 | 98.72 73 | 97.19 151 | 97.07 168 | 96.69 161 | 91.49 166 | 95.77 138 | 92.19 193 | 97.93 120 | 96.15 113 | 94.66 177 | 94.16 176 | 98.10 107 | 97.45 75 |
|
EPP-MVSNet | | | 97.29 94 | 96.88 100 | 97.76 85 | 98.70 74 | 99.10 13 | 98.92 51 | 98.36 40 | 95.12 85 | 93.36 193 | 97.39 114 | 91.00 187 | 97.65 60 | 98.72 38 | 98.91 24 | 99.58 7 | 97.92 52 |
|
AdaColmap | | | 95.85 138 | 94.65 154 | 97.26 106 | 98.70 74 | 97.20 150 | 97.33 154 | 97.30 138 | 91.28 169 | 95.90 132 | 88.16 212 | 96.17 154 | 96.60 97 | 97.34 92 | 96.82 106 | 97.71 120 | 95.60 138 |
|
CLD-MVS | | | 96.73 117 | 96.92 96 | 96.51 142 | 98.70 74 | 97.57 138 | 97.64 129 | 92.07 217 | 93.10 148 | 96.31 118 | 98.29 90 | 99.02 47 | 95.99 117 | 97.20 97 | 96.47 123 | 98.37 92 | 96.81 107 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
anonymousdsp | | | 98.85 15 | 98.88 14 | 98.83 15 | 98.69 77 | 98.20 80 | 99.68 1 | 97.35 136 | 97.09 24 | 98.98 12 | 99.86 1 | 99.43 11 | 98.94 6 | 99.28 17 | 99.19 14 | 99.33 22 | 99.08 5 |
|
PMVS | | 90.51 17 | 97.77 60 | 97.98 41 | 97.53 95 | 98.68 78 | 98.14 90 | 97.67 126 | 97.03 146 | 96.43 32 | 98.38 26 | 98.72 77 | 97.03 143 | 94.44 143 | 99.37 13 | 99.30 10 | 98.98 42 | 96.86 103 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
Effi-MVS+ | | | 96.46 124 | 95.28 139 | 97.85 75 | 98.64 79 | 97.16 152 | 97.15 166 | 98.75 21 | 90.27 181 | 98.03 44 | 93.93 178 | 96.21 152 | 96.55 102 | 96.34 134 | 96.69 114 | 97.97 113 | 96.33 120 |
|
Fast-Effi-MVS+ | | | 96.80 113 | 95.92 131 | 97.84 76 | 98.57 80 | 97.46 144 | 98.06 105 | 98.24 52 | 89.64 188 | 97.57 60 | 96.45 133 | 97.35 134 | 96.73 93 | 97.22 96 | 96.64 116 | 97.86 116 | 96.65 111 |
|
conf0.05thres1000 | | | 95.91 137 | 94.67 153 | 97.37 101 | 98.54 81 | 98.73 46 | 98.41 91 | 98.07 70 | 96.10 42 | 94.93 161 | 92.83 188 | 80.67 213 | 95.26 127 | 98.68 42 | 98.65 36 | 98.99 40 | 97.02 94 |
|
Effi-MVS+-dtu | | | 95.94 136 | 95.08 145 | 96.94 121 | 98.54 81 | 97.38 145 | 96.66 178 | 97.89 84 | 88.68 191 | 95.92 131 | 92.90 187 | 97.28 135 | 94.18 152 | 96.68 123 | 96.13 139 | 98.45 86 | 96.51 117 |
|
v748 | | | 98.92 13 | 98.95 10 | 98.87 11 | 98.54 81 | 98.69 50 | 99.33 17 | 98.64 23 | 98.07 12 | 99.06 9 | 99.66 12 | 99.76 3 | 98.68 11 | 99.25 19 | 98.72 33 | 99.01 36 | 98.54 25 |
|
TSAR-MVS + MP. | | | 98.15 35 | 98.23 32 | 98.06 59 | 98.47 84 | 98.16 86 | 99.23 22 | 96.87 151 | 95.58 64 | 96.72 98 | 98.41 87 | 99.06 41 | 98.05 34 | 98.99 28 | 98.90 25 | 99.00 38 | 98.51 29 |
|
IS_MVSNet | | | 96.62 122 | 96.48 117 | 96.78 129 | 98.46 85 | 98.68 52 | 98.61 79 | 98.24 52 | 92.23 157 | 89.63 216 | 95.90 146 | 94.40 170 | 96.23 109 | 98.65 45 | 98.77 30 | 99.52 13 | 96.76 108 |
|
MVS_111021_HR | | | 97.27 95 | 97.11 86 | 97.46 99 | 98.46 85 | 97.82 127 | 97.50 139 | 96.86 152 | 94.97 89 | 97.13 80 | 96.99 124 | 98.39 104 | 96.82 92 | 97.65 82 | 97.38 89 | 98.02 109 | 96.56 115 |
|
PCF-MVS | | 92.69 14 | 95.98 134 | 95.05 146 | 97.06 116 | 98.43 87 | 97.56 139 | 97.76 123 | 96.65 163 | 89.95 186 | 95.70 142 | 96.18 139 | 98.48 102 | 95.74 119 | 93.64 193 | 93.35 189 | 98.09 108 | 96.18 122 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
tfpn | | | 92.86 187 | 89.37 204 | 96.93 122 | 98.40 88 | 98.34 75 | 98.02 109 | 97.80 91 | 92.54 153 | 93.99 178 | 86.54 216 | 57.58 237 | 94.82 135 | 97.66 81 | 97.99 75 | 98.56 72 | 94.95 154 |
|
EPNet_dtu | | | 93.45 183 | 92.51 182 | 94.55 189 | 98.39 89 | 91.67 217 | 95.46 208 | 97.50 110 | 86.56 216 | 97.38 69 | 93.52 180 | 94.20 173 | 85.82 216 | 93.31 196 | 92.53 194 | 92.72 207 | 95.76 134 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v11 | | | 97.94 53 | 97.72 57 | 98.20 42 | 98.37 90 | 98.69 50 | 98.96 47 | 98.30 45 | 95.68 60 | 98.35 28 | 99.70 9 | 99.19 31 | 97.93 43 | 96.76 119 | 96.82 106 | 97.28 150 | 97.23 88 |
|
v13 | | | 98.04 42 | 97.86 45 | 98.24 40 | 98.36 91 | 98.77 34 | 99.04 32 | 98.47 29 | 95.93 50 | 98.20 35 | 99.67 11 | 99.11 35 | 98.00 38 | 97.11 100 | 96.93 103 | 97.40 136 | 97.53 69 |
|
canonicalmvs | | | 97.11 101 | 96.88 100 | 97.38 100 | 98.34 92 | 98.72 48 | 97.52 138 | 97.94 78 | 95.60 62 | 95.01 159 | 94.58 167 | 94.50 169 | 96.59 98 | 97.84 70 | 98.03 73 | 98.90 50 | 98.91 8 |
|
v12 | | | 97.98 48 | 97.78 52 | 98.21 41 | 98.33 93 | 98.74 44 | 99.01 38 | 98.44 34 | 95.82 57 | 98.13 36 | 99.64 15 | 99.08 39 | 97.95 42 | 96.97 112 | 96.82 106 | 97.39 138 | 97.38 81 |
|
SD-MVS | | | 97.84 57 | 97.78 52 | 97.90 68 | 98.33 93 | 98.06 96 | 97.95 113 | 97.80 91 | 96.03 46 | 96.72 98 | 97.57 109 | 99.18 32 | 97.50 71 | 97.88 69 | 97.08 98 | 99.11 32 | 98.68 20 |
|
NR-MVSNet | | | 98.00 45 | 97.88 44 | 98.13 46 | 98.33 93 | 98.77 34 | 98.83 59 | 98.88 15 | 94.10 122 | 97.46 66 | 98.87 67 | 98.58 96 | 95.78 118 | 99.13 25 | 98.16 62 | 99.52 13 | 97.53 69 |
|
tfpnnormal | | | 97.66 63 | 97.79 49 | 97.52 97 | 98.32 96 | 98.53 63 | 98.45 88 | 97.69 97 | 97.59 19 | 96.12 126 | 97.79 105 | 96.70 145 | 95.69 121 | 98.35 60 | 98.34 50 | 98.85 56 | 97.22 90 |
|
pmmvs6 | | | 98.77 16 | 99.35 3 | 98.09 50 | 98.32 96 | 98.92 21 | 98.57 81 | 99.03 12 | 99.36 2 | 96.86 95 | 99.77 5 | 99.86 2 | 96.20 111 | 99.56 5 | 99.39 7 | 99.59 6 | 98.61 22 |
|
Anonymous20231206 | | | 95.69 142 | 95.68 133 | 95.70 166 | 98.32 96 | 96.95 161 | 97.37 151 | 96.65 163 | 93.33 140 | 93.61 185 | 98.70 79 | 98.03 118 | 91.04 181 | 95.07 171 | 94.59 175 | 97.20 155 | 93.09 183 |
|
v1192 | | | 97.52 73 | 97.03 90 | 98.09 50 | 98.31 99 | 98.01 102 | 98.96 47 | 97.25 139 | 95.22 81 | 98.89 14 | 99.64 15 | 98.83 70 | 97.68 58 | 95.63 160 | 95.91 149 | 97.47 131 | 95.97 128 |
|
V9 | | | 97.91 55 | 97.70 59 | 98.17 44 | 98.30 100 | 98.70 49 | 98.98 42 | 98.40 36 | 95.72 59 | 98.07 40 | 99.64 15 | 99.04 45 | 97.90 45 | 96.82 116 | 96.71 113 | 97.37 141 | 97.23 88 |
|
view800 | | | 94.54 163 | 92.55 179 | 96.86 127 | 98.28 101 | 98.22 79 | 97.97 112 | 97.62 101 | 92.10 159 | 94.19 175 | 85.52 219 | 81.33 212 | 94.61 139 | 97.41 89 | 98.51 38 | 98.50 80 | 94.72 157 |
|
V14 | | | 97.85 56 | 97.60 61 | 98.13 46 | 98.27 102 | 98.66 53 | 98.94 49 | 98.36 40 | 95.62 61 | 98.04 43 | 99.62 19 | 98.99 49 | 97.84 48 | 96.65 124 | 96.59 119 | 97.34 144 | 97.07 93 |
|
v1144 | | | 97.51 74 | 97.05 88 | 98.04 61 | 98.26 103 | 97.98 105 | 98.88 56 | 97.42 126 | 95.38 75 | 98.56 21 | 99.59 24 | 99.01 48 | 97.65 60 | 95.77 158 | 96.06 143 | 97.47 131 | 95.56 139 |
|
v1240 | | | 97.43 84 | 96.87 105 | 98.09 50 | 98.25 104 | 97.92 114 | 99.02 34 | 97.06 144 | 94.77 95 | 99.09 7 | 99.68 10 | 98.51 100 | 97.78 50 | 95.25 168 | 95.81 152 | 97.32 145 | 96.13 124 |
|
v15 | | | 97.77 60 | 97.50 68 | 98.09 50 | 98.23 105 | 98.62 55 | 98.90 53 | 98.32 44 | 95.51 72 | 98.01 45 | 99.60 21 | 98.95 57 | 97.78 50 | 96.47 130 | 96.45 124 | 97.32 145 | 96.90 98 |
|
TSAR-MVS + ACMM | | | 97.54 69 | 97.79 49 | 97.26 106 | 98.23 105 | 98.10 94 | 97.71 125 | 97.88 85 | 95.97 48 | 95.57 147 | 98.71 78 | 98.57 97 | 97.36 77 | 97.74 74 | 96.81 109 | 96.83 173 | 98.59 23 |
|
RPSCF | | | 97.83 58 | 98.27 30 | 97.31 105 | 98.23 105 | 98.06 96 | 97.44 148 | 95.79 183 | 96.90 27 | 95.81 136 | 98.76 75 | 98.61 94 | 97.70 56 | 98.90 32 | 98.36 49 | 98.90 50 | 98.29 38 |
|
TransMVSNet (Re) | | | 98.23 28 | 98.72 19 | 97.66 87 | 98.22 108 | 98.73 46 | 98.66 78 | 98.03 74 | 98.60 7 | 96.40 113 | 99.60 21 | 98.24 110 | 95.26 127 | 99.19 22 | 99.05 18 | 99.36 20 | 97.64 62 |
|
TSAR-MVS + GP. | | | 97.26 96 | 97.33 69 | 97.18 111 | 98.21 109 | 98.06 96 | 96.38 185 | 97.66 99 | 93.92 131 | 95.23 151 | 98.48 84 | 98.33 106 | 97.41 75 | 97.63 83 | 97.35 90 | 98.18 103 | 97.57 68 |
|
v1921920 | | | 97.50 77 | 97.00 92 | 98.07 57 | 98.20 110 | 97.94 111 | 99.03 33 | 97.06 144 | 95.29 79 | 99.01 11 | 99.62 19 | 98.73 84 | 97.74 53 | 95.52 163 | 95.78 154 | 97.39 138 | 96.12 125 |
|
v2v482 | | | 97.33 90 | 96.84 106 | 97.90 68 | 98.19 111 | 97.83 125 | 98.74 73 | 97.44 125 | 95.42 74 | 98.23 34 | 99.46 39 | 98.84 69 | 97.46 73 | 95.51 164 | 96.10 141 | 97.36 142 | 94.72 157 |
|
thres600view7 | | | 94.34 169 | 92.31 185 | 96.70 132 | 98.19 111 | 98.12 91 | 97.85 122 | 97.45 123 | 91.49 166 | 93.98 179 | 84.27 222 | 82.02 210 | 94.24 147 | 97.04 104 | 98.76 31 | 98.49 82 | 94.47 163 |
|
v1141 | | | 97.36 89 | 96.92 96 | 97.88 73 | 98.18 113 | 97.90 118 | 98.76 64 | 97.42 126 | 95.38 75 | 98.07 40 | 99.56 25 | 98.87 65 | 97.59 68 | 95.78 155 | 95.98 144 | 97.29 147 | 94.97 152 |
|
divwei89l23v2f112 | | | 97.37 87 | 96.92 96 | 97.89 70 | 98.18 113 | 97.90 118 | 98.76 64 | 97.42 126 | 95.38 75 | 98.09 38 | 99.56 25 | 98.87 65 | 97.59 68 | 95.78 155 | 95.98 144 | 97.29 147 | 94.97 152 |
|
v1 | | | 97.37 87 | 96.92 96 | 97.89 70 | 98.18 113 | 97.91 117 | 98.76 64 | 97.42 126 | 95.38 75 | 98.09 38 | 99.55 30 | 98.88 64 | 97.59 68 | 95.78 155 | 95.98 144 | 97.29 147 | 94.98 151 |
|
tfpn_n400 | | | 95.11 152 | 93.86 163 | 96.57 139 | 98.16 116 | 97.92 114 | 97.59 133 | 97.90 81 | 95.90 52 | 92.83 202 | 89.94 207 | 83.01 204 | 94.23 149 | 97.50 86 | 97.43 87 | 98.73 63 | 95.30 145 |
|
tfpnconf | | | 95.11 152 | 93.86 163 | 96.57 139 | 98.16 116 | 97.92 114 | 97.59 133 | 97.90 81 | 95.90 52 | 92.83 202 | 89.94 207 | 83.01 204 | 94.23 149 | 97.50 86 | 97.43 87 | 98.73 63 | 95.30 145 |
|
view600 | | | 94.36 167 | 92.33 184 | 96.73 130 | 98.14 118 | 98.03 100 | 97.88 119 | 97.36 135 | 91.61 163 | 94.29 172 | 84.38 221 | 82.08 209 | 94.31 146 | 97.05 103 | 98.75 32 | 98.42 89 | 94.41 165 |
|
PHI-MVS | | | 97.44 81 | 97.17 78 | 97.74 86 | 98.14 118 | 98.41 72 | 98.03 107 | 97.50 110 | 92.07 161 | 98.01 45 | 97.33 116 | 98.62 93 | 96.02 115 | 98.34 62 | 98.21 57 | 98.76 62 | 97.24 87 |
|
3Dnovator | | 96.31 3 | 97.22 98 | 97.19 76 | 97.25 109 | 98.14 118 | 97.95 108 | 98.03 107 | 96.77 157 | 96.42 33 | 97.14 78 | 95.11 157 | 97.59 130 | 95.14 132 | 97.79 72 | 97.72 83 | 98.26 96 | 97.76 59 |
|
PLC | | 92.55 15 | 96.10 130 | 95.36 136 | 96.96 119 | 98.13 121 | 96.88 163 | 96.49 182 | 96.67 162 | 94.07 125 | 95.71 141 | 91.14 201 | 96.09 155 | 96.84 91 | 96.70 122 | 96.58 120 | 97.92 115 | 96.03 126 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
tfpnview11 | | | 94.92 157 | 93.56 168 | 96.50 143 | 98.12 122 | 97.99 104 | 97.48 141 | 97.86 86 | 94.50 110 | 92.83 202 | 89.94 207 | 83.01 204 | 94.19 151 | 96.91 115 | 98.07 72 | 98.50 80 | 94.53 160 |
|
v144192 | | | 97.49 78 | 96.99 94 | 98.07 57 | 98.11 123 | 97.95 108 | 99.02 34 | 97.21 140 | 94.90 92 | 98.88 15 | 99.53 31 | 98.89 62 | 97.75 52 | 95.59 161 | 95.90 150 | 97.43 133 | 96.16 123 |
|
tfpn1000 | | | 94.36 167 | 93.33 174 | 95.56 172 | 98.09 124 | 98.07 95 | 97.08 167 | 97.78 93 | 94.02 127 | 89.16 219 | 91.38 199 | 80.56 214 | 92.54 173 | 96.76 119 | 98.09 64 | 98.69 66 | 94.40 167 |
|
v7 | | | 97.45 80 | 97.01 91 | 97.97 65 | 98.07 125 | 97.96 106 | 98.86 57 | 97.50 110 | 94.46 113 | 98.24 32 | 99.56 25 | 98.98 51 | 97.72 54 | 96.05 148 | 96.26 131 | 97.42 134 | 95.79 132 |
|
v10 | | | 97.64 65 | 97.26 71 | 98.08 55 | 98.07 125 | 98.56 61 | 98.86 57 | 98.18 62 | 94.48 112 | 98.24 32 | 99.56 25 | 98.98 51 | 97.72 54 | 96.05 148 | 96.26 131 | 97.42 134 | 96.93 97 |
|
CANet_DTU | | | 94.96 156 | 94.62 155 | 95.35 174 | 98.03 127 | 96.11 181 | 96.92 170 | 95.60 187 | 88.59 193 | 97.27 75 | 95.27 155 | 96.50 149 | 88.77 202 | 95.53 162 | 95.59 156 | 95.54 191 | 94.78 155 |
|
v17 | | | 97.54 69 | 97.21 74 | 97.92 66 | 98.02 128 | 98.50 66 | 98.79 61 | 98.24 52 | 94.39 116 | 97.60 59 | 99.45 41 | 98.72 85 | 97.68 58 | 96.29 137 | 96.28 129 | 97.19 159 | 96.86 103 |
|
MSLP-MVS++ | | | 96.66 120 | 96.46 118 | 96.89 126 | 98.02 128 | 97.71 131 | 95.57 203 | 96.96 147 | 94.36 117 | 96.19 124 | 91.37 200 | 98.24 110 | 97.07 86 | 97.69 76 | 97.89 77 | 97.52 129 | 97.95 49 |
|
test-LLR | | | 89.77 212 | 87.47 214 | 92.45 208 | 98.01 130 | 89.77 226 | 93.25 228 | 95.80 181 | 81.56 232 | 89.19 217 | 92.08 194 | 79.59 216 | 85.77 219 | 91.47 212 | 89.04 213 | 92.69 208 | 88.75 205 |
|
test0.0.03 1 | | | 91.17 203 | 91.50 194 | 90.80 220 | 98.01 130 | 95.46 187 | 94.22 221 | 95.80 181 | 86.55 217 | 81.75 237 | 90.83 204 | 87.93 191 | 78.48 230 | 94.51 183 | 94.11 179 | 96.50 180 | 91.08 193 |
|
gg-mvs-nofinetune | | | 94.13 174 | 93.93 162 | 94.37 190 | 97.99 132 | 95.86 184 | 95.45 210 | 99.22 9 | 97.61 18 | 95.10 156 | 99.50 33 | 84.50 195 | 81.73 226 | 95.31 167 | 94.12 178 | 96.71 178 | 90.59 195 |
|
v1neww | | | 97.30 91 | 96.88 100 | 97.78 82 | 97.99 132 | 97.87 121 | 98.75 70 | 97.46 118 | 94.54 106 | 97.62 56 | 99.48 35 | 98.76 80 | 97.65 60 | 96.09 145 | 96.15 133 | 97.20 155 | 95.28 147 |
|
v7new | | | 97.30 91 | 96.88 100 | 97.78 82 | 97.99 132 | 97.87 121 | 98.75 70 | 97.46 118 | 94.54 106 | 97.62 56 | 99.48 35 | 98.76 80 | 97.65 60 | 96.09 145 | 96.15 133 | 97.20 155 | 95.28 147 |
|
v16 | | | 97.51 74 | 97.19 76 | 97.89 70 | 97.99 132 | 98.49 67 | 98.77 63 | 98.23 55 | 94.29 118 | 97.48 63 | 99.42 44 | 98.68 86 | 97.69 57 | 96.28 138 | 96.29 128 | 97.18 160 | 96.85 105 |
|
v8 | | | 97.51 74 | 97.16 79 | 97.91 67 | 97.99 132 | 98.48 69 | 98.76 64 | 98.17 63 | 94.54 106 | 97.69 53 | 99.48 35 | 98.76 80 | 97.63 65 | 96.10 144 | 96.14 137 | 97.20 155 | 96.64 112 |
|
v6 | | | 97.30 91 | 96.88 100 | 97.78 82 | 97.99 132 | 97.87 121 | 98.75 70 | 97.46 118 | 94.54 106 | 97.61 58 | 99.48 35 | 98.77 79 | 97.65 60 | 96.09 145 | 96.15 133 | 97.21 154 | 95.28 147 |
|
thres400 | | | 94.04 176 | 91.94 189 | 96.50 143 | 97.98 138 | 97.82 127 | 97.66 128 | 96.96 147 | 90.96 172 | 94.20 173 | 83.24 224 | 82.82 207 | 93.80 156 | 96.50 128 | 98.09 64 | 98.38 91 | 94.15 171 |
|
Vis-MVSNet (Re-imp) | | | 96.29 127 | 96.50 115 | 96.05 157 | 97.96 139 | 97.83 125 | 97.30 155 | 97.86 86 | 93.14 145 | 88.90 220 | 96.80 126 | 95.28 161 | 95.15 130 | 98.37 59 | 98.25 56 | 99.12 31 | 95.84 129 |
|
pm-mvs1 | | | 98.14 36 | 98.66 21 | 97.53 95 | 97.93 140 | 98.49 67 | 98.14 102 | 98.19 59 | 97.95 14 | 96.17 125 | 99.63 18 | 98.85 68 | 95.41 125 | 98.91 31 | 98.89 26 | 99.34 21 | 97.86 54 |
|
MAR-MVS | | | 95.51 143 | 94.49 157 | 96.71 131 | 97.92 141 | 96.40 173 | 96.72 176 | 98.04 73 | 86.74 213 | 96.72 98 | 92.52 191 | 95.14 163 | 94.02 154 | 96.81 117 | 96.54 121 | 96.85 171 | 97.25 85 |
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 |
N_pmnet | | | 92.46 188 | 92.38 183 | 92.55 207 | 97.91 142 | 93.47 201 | 97.42 149 | 94.01 215 | 96.40 35 | 88.48 223 | 98.50 83 | 98.07 117 | 88.14 206 | 91.04 214 | 84.30 219 | 89.35 222 | 84.85 221 |
|
v18 | | | 97.40 85 | 97.04 89 | 97.81 78 | 97.90 143 | 98.42 71 | 98.71 76 | 98.17 63 | 94.06 126 | 97.34 72 | 99.40 46 | 98.59 95 | 97.60 66 | 96.05 148 | 96.12 140 | 97.14 163 | 96.67 110 |
|
v148 | | | 96.99 107 | 96.70 112 | 97.34 102 | 97.89 144 | 97.23 149 | 98.33 94 | 96.96 147 | 95.57 65 | 97.12 81 | 98.99 62 | 99.40 13 | 97.23 82 | 96.22 141 | 95.45 159 | 96.50 180 | 94.02 173 |
|
DELS-MVS | | | 96.90 108 | 97.24 73 | 96.50 143 | 97.85 145 | 98.18 82 | 97.88 119 | 95.92 176 | 93.48 138 | 95.34 149 | 98.86 69 | 98.94 60 | 94.03 153 | 97.33 93 | 97.04 99 | 98.00 111 | 96.85 105 |
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 |
CDS-MVSNet | | | 94.91 158 | 95.17 142 | 94.60 188 | 97.85 145 | 96.21 180 | 96.90 171 | 96.39 166 | 90.81 175 | 93.40 191 | 97.24 119 | 94.54 168 | 85.78 217 | 96.25 139 | 96.15 133 | 97.26 151 | 95.01 150 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
OMC-MVS | | | 97.23 97 | 97.21 74 | 97.25 109 | 97.85 145 | 97.52 142 | 97.92 115 | 95.77 184 | 95.83 56 | 97.09 83 | 97.86 103 | 98.52 99 | 96.62 96 | 97.51 84 | 96.65 115 | 98.26 96 | 96.57 113 |
|
OpenMVS | | 94.63 9 | 95.75 140 | 95.04 147 | 96.58 138 | 97.85 145 | 97.55 140 | 96.71 177 | 96.07 171 | 90.15 184 | 96.47 108 | 90.77 206 | 95.95 157 | 94.41 144 | 97.01 110 | 96.95 101 | 98.00 111 | 96.90 98 |
|
MSDG | | | 96.27 128 | 96.17 123 | 96.38 149 | 97.85 145 | 96.27 179 | 96.55 181 | 94.41 211 | 94.55 103 | 95.62 144 | 97.56 110 | 97.80 123 | 96.22 110 | 97.17 99 | 96.27 130 | 97.67 125 | 93.60 176 |
|
TinyColmap | | | 96.64 121 | 96.07 125 | 97.32 104 | 97.84 150 | 96.40 173 | 97.63 131 | 96.25 167 | 95.86 54 | 98.98 12 | 97.94 100 | 96.34 151 | 96.17 112 | 97.30 94 | 95.38 162 | 97.04 166 | 93.24 180 |
|
Fast-Effi-MVS+-dtu | | | 94.34 169 | 93.26 175 | 95.62 169 | 97.82 151 | 95.97 183 | 95.86 196 | 99.01 13 | 86.88 211 | 93.39 192 | 90.83 204 | 95.46 160 | 90.61 186 | 94.46 184 | 94.68 173 | 97.01 168 | 94.51 161 |
|
MIMVSNet | | | 93.68 182 | 93.96 161 | 93.35 200 | 97.82 151 | 96.08 182 | 96.34 186 | 98.46 32 | 91.28 169 | 86.67 231 | 94.95 161 | 94.87 165 | 84.39 222 | 94.53 179 | 94.65 174 | 96.45 182 | 91.34 192 |
|
HyFIR lowres test | | | 95.05 154 | 93.54 169 | 96.81 128 | 97.81 153 | 96.88 163 | 98.18 99 | 97.46 118 | 94.28 119 | 94.98 160 | 96.57 131 | 92.89 178 | 96.15 113 | 90.90 215 | 91.87 200 | 96.28 185 | 91.35 191 |
|
FMVSNet1 | | | 97.40 85 | 98.09 36 | 96.60 137 | 97.80 154 | 98.76 39 | 98.26 98 | 98.50 26 | 96.79 28 | 93.13 197 | 99.28 53 | 98.64 90 | 92.90 163 | 97.67 78 | 97.86 79 | 99.02 34 | 97.64 62 |
|
MVS_111021_LR | | | 96.86 109 | 96.72 111 | 97.03 118 | 97.80 154 | 97.06 160 | 97.04 169 | 95.51 189 | 94.55 103 | 97.47 64 | 97.35 115 | 97.68 127 | 96.66 94 | 97.11 100 | 96.73 111 | 97.69 123 | 96.57 113 |
|
abl_6 | | | | | 96.45 146 | 97.79 156 | 97.28 147 | 97.16 165 | 96.16 169 | 89.92 187 | 95.72 140 | 91.59 197 | 97.16 139 | 94.37 145 | | | 97.51 130 | 95.49 140 |
|
QAPM | | | 97.04 105 | 97.14 81 | 96.93 122 | 97.78 157 | 98.02 101 | 97.36 153 | 96.72 158 | 94.68 98 | 96.23 120 | 97.21 120 | 97.68 127 | 95.70 120 | 97.37 91 | 97.24 97 | 97.78 119 | 97.77 58 |
|
thres200 | | | 93.98 178 | 91.90 190 | 96.40 148 | 97.66 158 | 98.12 91 | 97.20 162 | 97.45 123 | 90.16 183 | 93.82 180 | 83.08 225 | 83.74 200 | 93.80 156 | 97.04 104 | 97.48 86 | 98.49 82 | 93.70 175 |
|
V42 | | | 97.10 102 | 96.97 95 | 97.26 106 | 97.64 159 | 97.60 135 | 98.45 88 | 95.99 173 | 94.44 114 | 97.35 71 | 99.40 46 | 98.63 92 | 97.34 79 | 96.33 136 | 96.38 127 | 96.82 175 | 96.00 127 |
|
TAPA-MVS | | 93.96 13 | 96.79 114 | 96.70 112 | 96.90 125 | 97.64 159 | 97.58 136 | 97.54 137 | 94.50 210 | 95.14 83 | 96.64 103 | 96.76 127 | 97.90 121 | 96.63 95 | 95.98 151 | 96.14 137 | 98.45 86 | 97.39 78 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
pmmvs-eth3d | | | 96.84 111 | 96.22 121 | 97.56 92 | 97.63 161 | 96.38 176 | 98.74 73 | 96.91 150 | 94.63 100 | 98.26 30 | 99.43 42 | 98.28 108 | 96.58 99 | 94.52 181 | 95.54 157 | 97.24 152 | 94.75 156 |
|
USDC | | | 96.30 126 | 95.64 135 | 97.07 115 | 97.62 162 | 96.35 178 | 97.17 164 | 95.71 185 | 95.52 70 | 99.17 6 | 98.11 97 | 97.46 131 | 95.67 122 | 95.44 166 | 93.60 185 | 97.09 164 | 92.99 185 |
|
UGNet | | | 96.79 114 | 97.82 47 | 95.58 170 | 97.57 163 | 98.39 73 | 98.48 86 | 97.84 89 | 95.85 55 | 94.68 164 | 97.91 102 | 99.07 40 | 87.12 211 | 97.71 75 | 97.51 84 | 97.80 117 | 98.29 38 |
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 |
IterMVS-LS | | | 96.35 125 | 95.85 132 | 96.93 122 | 97.53 164 | 98.00 103 | 97.37 151 | 97.97 77 | 95.49 73 | 96.71 101 | 98.94 64 | 93.23 176 | 94.82 135 | 93.15 199 | 95.05 167 | 97.17 161 | 97.12 91 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
tfpn111 | | | 93.73 181 | 91.63 193 | 96.17 153 | 97.52 165 | 98.15 87 | 97.48 141 | 97.48 115 | 87.65 202 | 93.42 189 | 82.19 229 | 84.12 196 | 92.62 166 | 97.04 104 | 98.09 64 | 98.52 77 | 94.17 168 |
|
conf200view11 | | | 93.79 180 | 91.75 191 | 96.17 153 | 97.52 165 | 98.15 87 | 97.48 141 | 97.48 115 | 87.65 202 | 93.42 189 | 83.03 226 | 84.12 196 | 92.62 166 | 97.04 104 | 98.09 64 | 98.52 77 | 94.17 168 |
|
thres100view900 | | | 92.93 186 | 90.89 197 | 95.31 175 | 97.52 165 | 96.82 167 | 96.41 183 | 95.08 195 | 87.65 202 | 93.56 187 | 83.03 226 | 84.12 196 | 91.12 180 | 94.53 179 | 96.91 104 | 98.17 104 | 93.21 181 |
|
tfpn200view9 | | | 93.80 179 | 91.75 191 | 96.20 151 | 97.52 165 | 98.15 87 | 97.48 141 | 97.47 117 | 87.65 202 | 93.56 187 | 83.03 226 | 84.12 196 | 92.62 166 | 97.04 104 | 98.09 64 | 98.52 77 | 94.17 168 |
|
IterMVS | | | 94.48 164 | 93.46 171 | 95.66 167 | 97.52 165 | 96.43 170 | 97.20 162 | 94.73 204 | 92.91 152 | 96.44 109 | 98.75 76 | 91.10 186 | 94.53 141 | 92.10 207 | 90.10 208 | 93.51 199 | 92.84 186 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
new-patchmatchnet | | | 94.48 164 | 94.02 160 | 95.02 182 | 97.51 170 | 95.00 191 | 95.68 202 | 94.26 212 | 97.32 21 | 95.73 139 | 99.60 21 | 98.22 113 | 91.30 177 | 94.13 189 | 84.41 218 | 95.65 190 | 89.45 202 |
|
CNLPA | | | 96.24 129 | 95.97 128 | 96.57 139 | 97.48 171 | 97.10 159 | 96.75 175 | 94.95 200 | 94.92 91 | 96.20 123 | 94.81 163 | 96.61 147 | 96.25 108 | 96.94 113 | 95.64 155 | 97.79 118 | 95.74 135 |
|
TSAR-MVS + COLMAP | | | 96.05 132 | 95.94 129 | 96.18 152 | 97.46 172 | 96.41 172 | 97.26 160 | 95.83 180 | 94.69 97 | 95.30 150 | 98.31 89 | 96.52 148 | 94.71 138 | 95.48 165 | 94.87 169 | 96.54 179 | 95.33 142 |
|
IB-MVS | | 92.44 16 | 93.33 184 | 92.15 187 | 94.70 186 | 97.42 173 | 96.39 175 | 95.57 203 | 94.67 205 | 86.40 219 | 93.59 186 | 78.28 235 | 95.76 159 | 89.59 197 | 95.88 154 | 95.98 144 | 97.39 138 | 96.34 119 |
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 |
conf0.01 | | | 91.86 196 | 88.22 207 | 96.10 155 | 97.40 174 | 97.94 111 | 97.48 141 | 97.41 130 | 87.65 202 | 93.22 195 | 80.39 231 | 63.83 233 | 92.62 166 | 96.63 125 | 98.09 64 | 98.47 84 | 93.03 184 |
|
thresconf0.02 | | | 91.75 198 | 88.21 208 | 95.87 161 | 97.38 175 | 97.14 154 | 97.27 159 | 96.85 153 | 93.04 149 | 92.39 205 | 82.19 229 | 63.31 234 | 93.10 160 | 94.43 185 | 95.06 166 | 98.23 100 | 92.32 188 |
|
GA-MVS | | | 94.18 173 | 92.98 176 | 95.58 170 | 97.36 176 | 96.42 171 | 96.21 192 | 95.86 177 | 90.29 180 | 95.08 157 | 96.19 138 | 85.37 194 | 92.82 164 | 94.01 191 | 94.14 177 | 96.16 186 | 94.41 165 |
|
conf0.002 | | | 91.12 204 | 86.87 218 | 96.08 156 | 97.35 177 | 97.89 120 | 97.48 141 | 97.38 132 | 87.65 202 | 93.19 196 | 79.38 233 | 57.48 238 | 92.62 166 | 96.56 127 | 96.64 116 | 98.46 85 | 92.50 187 |
|
our_test_3 | | | | | | 97.32 178 | 95.13 190 | 97.59 133 | | | | | | | | | | |
|
PVSNet_BlendedMVS | | | 95.44 146 | 95.09 143 | 95.86 162 | 97.31 179 | 97.13 155 | 96.31 189 | 95.01 197 | 88.55 194 | 96.23 120 | 94.55 170 | 97.75 124 | 92.56 171 | 96.42 131 | 95.44 160 | 97.71 120 | 95.81 130 |
|
PVSNet_Blended | | | 95.44 146 | 95.09 143 | 95.86 162 | 97.31 179 | 97.13 155 | 96.31 189 | 95.01 197 | 88.55 194 | 96.23 120 | 94.55 170 | 97.75 124 | 92.56 171 | 96.42 131 | 95.44 160 | 97.71 120 | 95.81 130 |
|
CHOSEN 1792x2688 | | | 94.98 155 | 94.69 152 | 95.31 175 | 97.27 181 | 95.58 186 | 97.90 117 | 95.56 188 | 95.03 87 | 93.77 184 | 95.65 149 | 99.29 18 | 95.30 126 | 91.51 211 | 91.28 203 | 92.05 213 | 94.50 162 |
|
MDTV_nov1_ep13_2view | | | 94.39 166 | 93.34 172 | 95.63 168 | 97.23 182 | 95.33 188 | 97.76 123 | 96.84 154 | 94.55 103 | 97.47 64 | 98.96 63 | 97.70 126 | 93.88 155 | 92.27 205 | 86.81 216 | 90.56 216 | 87.73 212 |
|
testmv | | | 92.35 191 | 92.53 181 | 92.13 211 | 97.16 183 | 92.68 206 | 96.31 189 | 94.61 209 | 86.68 215 | 88.16 225 | 97.27 118 | 97.09 142 | 83.28 224 | 94.52 181 | 93.39 188 | 93.26 200 | 86.10 219 |
|
test1235678 | | | 92.36 190 | 92.55 179 | 92.13 211 | 97.16 183 | 92.69 205 | 96.32 188 | 94.62 207 | 86.69 214 | 88.16 225 | 97.28 117 | 97.13 141 | 83.28 224 | 94.54 178 | 93.40 187 | 93.26 200 | 86.11 218 |
|
DI_MVS_plusplus_trai | | | 95.48 144 | 94.51 156 | 96.61 136 | 97.13 185 | 97.30 146 | 98.05 106 | 96.79 156 | 93.75 133 | 95.08 157 | 96.38 134 | 89.76 190 | 94.95 133 | 93.97 192 | 94.82 172 | 97.64 127 | 95.63 137 |
|
PM-MVS | | | 96.85 110 | 96.62 114 | 97.11 113 | 97.13 185 | 96.51 169 | 98.29 97 | 94.65 206 | 94.84 93 | 98.12 37 | 98.59 80 | 97.20 137 | 97.41 75 | 96.24 140 | 96.41 126 | 97.09 164 | 96.56 115 |
|
TAMVS | | | 92.46 188 | 93.34 172 | 91.44 217 | 97.03 187 | 93.84 200 | 94.68 220 | 90.60 221 | 90.44 179 | 85.31 232 | 97.14 121 | 93.03 177 | 85.78 217 | 94.34 186 | 93.67 184 | 95.22 193 | 90.93 194 |
|
pmmvs4 | | | 95.37 148 | 94.25 158 | 96.67 135 | 97.01 188 | 95.28 189 | 97.60 132 | 96.07 171 | 93.11 147 | 97.29 74 | 98.09 98 | 94.23 172 | 95.21 129 | 91.56 210 | 93.91 182 | 96.82 175 | 93.59 177 |
|
tfpn_ndepth | | | 93.27 185 | 92.11 188 | 94.61 187 | 96.96 189 | 97.93 113 | 96.87 172 | 97.49 113 | 90.91 174 | 87.89 227 | 85.98 217 | 83.53 201 | 89.77 195 | 95.91 153 | 97.31 94 | 98.67 68 | 93.25 179 |
|
MVS_Test | | | 95.34 149 | 94.88 150 | 95.89 160 | 96.93 190 | 96.84 166 | 96.66 178 | 97.08 143 | 90.06 185 | 94.02 177 | 97.61 108 | 96.64 146 | 93.59 158 | 92.73 203 | 94.02 180 | 97.03 167 | 96.24 121 |
|
Vis-MVSNet | | | 98.01 43 | 98.42 27 | 97.54 94 | 96.89 191 | 98.82 29 | 99.14 28 | 97.59 102 | 96.30 37 | 97.04 84 | 99.26 54 | 98.83 70 | 96.01 116 | 98.73 36 | 98.21 57 | 98.58 71 | 98.75 17 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
MVSTER | | | 91.97 194 | 90.31 198 | 93.91 195 | 96.81 192 | 96.91 162 | 94.22 221 | 95.64 186 | 84.98 222 | 92.98 201 | 93.42 181 | 72.56 226 | 86.64 215 | 95.11 170 | 93.89 183 | 97.16 162 | 95.31 143 |
|
PatchMatch-RL | | | 94.79 161 | 93.75 167 | 96.00 158 | 96.80 193 | 95.00 191 | 95.47 207 | 95.25 194 | 90.68 177 | 95.80 137 | 92.97 186 | 93.64 174 | 95.67 122 | 96.13 143 | 95.81 152 | 96.99 169 | 92.01 189 |
|
GBi-Net | | | 95.21 150 | 95.35 137 | 95.04 180 | 96.77 194 | 98.18 82 | 97.28 156 | 97.58 103 | 88.43 196 | 90.28 212 | 96.01 142 | 92.43 179 | 90.04 191 | 97.67 78 | 97.86 79 | 98.28 93 | 96.90 98 |
|
test1 | | | 95.21 150 | 95.35 137 | 95.04 180 | 96.77 194 | 98.18 82 | 97.28 156 | 97.58 103 | 88.43 196 | 90.28 212 | 96.01 142 | 92.43 179 | 90.04 191 | 97.67 78 | 97.86 79 | 98.28 93 | 96.90 98 |
|
FMVSNet2 | | | 95.77 139 | 96.20 122 | 95.27 177 | 96.77 194 | 98.18 82 | 97.28 156 | 97.90 81 | 93.12 146 | 91.37 207 | 98.25 92 | 96.05 156 | 90.04 191 | 94.96 175 | 95.94 148 | 98.28 93 | 96.90 98 |
|
tpm | | | 89.84 211 | 86.81 219 | 93.36 199 | 96.60 197 | 91.92 215 | 95.02 216 | 97.39 131 | 86.79 212 | 96.54 105 | 95.03 158 | 69.70 229 | 87.66 208 | 88.79 221 | 86.19 217 | 86.95 231 | 89.27 203 |
|
PatchmatchNet | | | 89.98 209 | 86.23 223 | 94.36 192 | 96.56 198 | 91.90 216 | 96.07 193 | 96.72 158 | 90.18 182 | 96.87 92 | 93.36 184 | 78.06 219 | 91.46 176 | 84.71 231 | 81.40 227 | 88.45 225 | 83.97 226 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
pmmvs5 | | | 95.70 141 | 95.22 140 | 96.26 150 | 96.55 199 | 97.24 148 | 97.50 139 | 94.99 199 | 90.95 173 | 96.87 92 | 98.47 85 | 97.40 132 | 94.45 142 | 92.86 201 | 94.98 168 | 97.23 153 | 94.64 159 |
|
diffmvs | | | 94.34 169 | 93.83 166 | 94.93 184 | 96.41 200 | 94.88 193 | 96.41 183 | 96.09 170 | 93.24 142 | 93.79 183 | 98.12 96 | 92.20 182 | 91.98 174 | 90.79 216 | 92.20 197 | 94.91 196 | 95.35 141 |
|
MS-PatchMatch | | | 94.84 159 | 94.76 151 | 94.94 183 | 96.38 201 | 94.69 195 | 95.90 195 | 94.03 214 | 92.49 154 | 93.81 181 | 95.79 147 | 96.38 150 | 94.54 140 | 94.70 176 | 94.85 170 | 94.97 194 | 94.43 164 |
|
no-one | | | 97.16 100 | 97.57 63 | 96.68 134 | 96.30 202 | 95.74 185 | 98.40 92 | 94.04 213 | 96.28 38 | 96.30 119 | 97.95 99 | 99.45 10 | 99.06 4 | 96.93 114 | 98.19 61 | 95.99 188 | 98.48 30 |
|
CR-MVSNet | | | 91.94 195 | 88.50 206 | 95.94 159 | 96.14 203 | 92.08 211 | 95.23 214 | 98.47 29 | 84.30 226 | 96.44 109 | 94.58 167 | 75.57 220 | 92.92 161 | 90.22 218 | 92.22 195 | 96.43 183 | 90.56 196 |
|
DeepPCF-MVS | | 94.55 10 | 97.05 104 | 97.13 84 | 96.95 120 | 96.06 204 | 97.12 157 | 98.01 110 | 95.44 190 | 95.18 82 | 97.50 62 | 97.86 103 | 98.08 116 | 97.31 81 | 97.23 95 | 97.00 100 | 97.36 142 | 97.45 75 |
|
EPMVS | | | 89.28 214 | 86.28 221 | 92.79 206 | 96.01 205 | 92.00 214 | 95.83 197 | 95.85 179 | 90.78 176 | 91.00 209 | 94.58 167 | 74.65 222 | 88.93 200 | 85.00 229 | 82.88 225 | 89.09 223 | 84.09 225 |
|
tpmp4_e23 | | | 88.68 218 | 84.61 226 | 93.43 198 | 96.00 206 | 91.46 218 | 95.40 212 | 96.60 165 | 87.71 201 | 94.67 165 | 88.54 211 | 69.81 228 | 88.41 204 | 85.50 228 | 81.08 228 | 89.52 221 | 88.18 210 |
|
MDTV_nov1_ep13 | | | 90.30 207 | 87.32 216 | 93.78 196 | 96.00 206 | 92.97 203 | 95.46 208 | 95.39 191 | 88.61 192 | 95.41 148 | 94.45 172 | 80.39 215 | 89.87 194 | 86.58 225 | 83.54 222 | 90.56 216 | 84.71 222 |
|
RPMNet | | | 90.52 206 | 86.27 222 | 95.48 173 | 95.95 208 | 92.08 211 | 95.55 206 | 98.12 66 | 84.30 226 | 95.60 146 | 87.49 215 | 72.78 225 | 91.24 178 | 87.93 222 | 89.34 209 | 96.41 184 | 89.98 199 |
|
testus | | | 90.01 208 | 90.03 201 | 89.98 222 | 95.89 209 | 91.43 219 | 93.88 224 | 89.30 223 | 83.54 228 | 89.68 215 | 87.81 214 | 94.62 166 | 78.31 231 | 92.87 200 | 92.01 198 | 92.85 206 | 87.91 211 |
|
CostFormer | | | 89.06 216 | 85.65 224 | 93.03 205 | 95.88 210 | 92.40 208 | 95.30 213 | 95.86 177 | 86.49 218 | 93.12 199 | 93.40 183 | 74.18 223 | 88.25 205 | 82.99 232 | 81.46 226 | 89.77 220 | 88.66 207 |
|
FPMVS | | | 94.70 162 | 94.99 149 | 94.37 190 | 95.84 211 | 93.20 202 | 96.00 194 | 91.93 218 | 95.03 87 | 94.64 166 | 94.68 165 | 93.29 175 | 90.95 182 | 98.07 67 | 97.34 91 | 96.85 171 | 93.29 178 |
|
E-PMN | | | 86.94 225 | 85.10 225 | 89.09 228 | 95.77 212 | 83.54 237 | 89.89 234 | 86.55 226 | 92.18 158 | 87.34 229 | 94.02 175 | 83.42 202 | 89.63 196 | 93.32 195 | 77.11 232 | 85.33 232 | 72.09 234 |
|
tpm cat1 | | | 87.19 224 | 82.78 230 | 92.33 210 | 95.66 213 | 90.61 223 | 94.19 223 | 95.27 193 | 86.97 210 | 94.38 170 | 90.91 203 | 69.40 230 | 87.21 210 | 79.57 235 | 77.82 231 | 87.25 230 | 84.18 224 |
|
tpmrst | | | 87.60 223 | 84.13 229 | 91.66 216 | 95.65 214 | 89.73 228 | 93.77 225 | 94.74 203 | 88.85 190 | 93.35 194 | 95.60 150 | 72.37 227 | 87.40 209 | 81.24 234 | 78.19 230 | 85.02 234 | 82.90 230 |
|
FMVSNet5 | | | 89.65 213 | 87.60 213 | 92.04 213 | 95.63 215 | 96.61 168 | 94.82 219 | 94.75 202 | 80.11 235 | 87.72 228 | 77.73 236 | 73.81 224 | 83.81 223 | 95.64 159 | 96.08 142 | 95.49 192 | 93.21 181 |
|
ADS-MVSNet | | | 89.89 210 | 87.70 211 | 92.43 209 | 95.52 216 | 90.91 222 | 95.57 203 | 95.33 192 | 93.19 144 | 91.21 208 | 93.41 182 | 82.12 208 | 89.05 198 | 86.21 226 | 83.77 221 | 87.92 226 | 84.31 223 |
|
EMVS | | | 86.63 227 | 84.48 227 | 89.15 227 | 95.51 217 | 83.66 236 | 90.19 233 | 86.14 228 | 91.78 162 | 88.68 221 | 93.83 179 | 81.97 211 | 89.05 198 | 92.76 202 | 76.09 233 | 85.31 233 | 71.28 235 |
|
EU-MVSNet | | | 96.03 133 | 96.23 120 | 95.80 164 | 95.48 218 | 94.18 196 | 98.99 40 | 91.51 219 | 97.22 22 | 97.66 54 | 99.15 58 | 98.51 100 | 98.08 32 | 95.92 152 | 92.88 193 | 93.09 204 | 95.72 136 |
|
FMVSNet3 | | | 94.06 175 | 93.85 165 | 94.31 193 | 95.46 219 | 97.80 129 | 96.34 186 | 97.58 103 | 88.43 196 | 90.28 212 | 96.01 142 | 92.43 179 | 88.67 203 | 91.82 208 | 93.96 181 | 97.53 128 | 96.50 118 |
|
test2356 | | | 85.48 229 | 81.66 231 | 89.94 223 | 95.36 220 | 88.71 231 | 91.69 232 | 92.78 216 | 78.28 237 | 86.79 230 | 85.80 218 | 58.29 236 | 80.44 228 | 89.39 220 | 89.17 211 | 92.60 211 | 81.98 231 |
|
DWT-MVSNet_training | | | 86.69 226 | 81.24 232 | 93.05 203 | 95.31 221 | 92.06 213 | 95.75 200 | 91.51 219 | 84.32 225 | 94.49 168 | 83.46 223 | 55.37 239 | 90.81 184 | 82.76 233 | 83.19 224 | 90.45 218 | 87.52 213 |
|
test-mter | | | 89.16 215 | 88.14 209 | 90.37 221 | 94.79 222 | 91.05 221 | 93.60 227 | 85.26 231 | 81.65 231 | 88.32 224 | 92.22 192 | 79.35 218 | 87.03 212 | 92.28 204 | 90.12 207 | 93.19 203 | 90.29 198 |
|
CHOSEN 280x420 | | | 91.55 200 | 90.27 199 | 93.05 203 | 94.61 223 | 88.01 233 | 96.56 180 | 94.62 207 | 88.04 200 | 94.20 173 | 92.66 189 | 86.60 192 | 90.82 183 | 95.06 172 | 91.89 199 | 87.49 229 | 89.61 201 |
|
LP | | | 92.03 193 | 90.19 200 | 94.17 194 | 94.52 224 | 93.87 199 | 96.79 173 | 95.05 196 | 93.58 136 | 95.62 144 | 95.68 148 | 83.37 203 | 91.78 175 | 90.73 217 | 86.99 215 | 91.27 214 | 87.09 215 |
|
CVMVSNet | | | 94.01 177 | 94.25 158 | 93.73 197 | 94.36 225 | 92.44 207 | 97.45 147 | 88.56 224 | 95.59 63 | 93.06 200 | 98.88 65 | 90.03 189 | 94.84 134 | 94.08 190 | 93.45 186 | 94.09 197 | 95.31 143 |
|
MDA-MVSNet-bldmvs | | | 95.45 145 | 95.20 141 | 95.74 165 | 94.24 226 | 96.38 176 | 97.93 114 | 94.80 201 | 95.56 68 | 96.87 92 | 98.29 90 | 95.24 162 | 96.50 104 | 98.65 45 | 90.38 206 | 94.09 197 | 91.93 190 |
|
TESTMET0.1,1 | | | 88.60 220 | 87.47 214 | 89.93 224 | 94.23 227 | 89.77 226 | 93.25 228 | 84.47 232 | 81.56 232 | 89.19 217 | 92.08 194 | 79.59 216 | 85.77 219 | 91.47 212 | 89.04 213 | 92.69 208 | 88.75 205 |
|
dps | | | 88.36 221 | 84.32 228 | 93.07 202 | 93.86 228 | 92.29 209 | 94.89 218 | 95.93 175 | 83.50 229 | 93.13 197 | 91.87 196 | 67.79 231 | 90.32 189 | 85.99 227 | 83.22 223 | 90.28 219 | 85.56 220 |
|
new_pmnet | | | 90.85 205 | 92.26 186 | 89.21 226 | 93.68 229 | 89.05 230 | 93.20 230 | 84.16 233 | 92.99 150 | 84.25 233 | 97.72 106 | 94.60 167 | 86.80 214 | 93.20 197 | 91.30 202 | 93.21 202 | 86.94 216 |
|
testpf | | | 81.59 231 | 76.31 233 | 87.75 229 | 93.50 230 | 83.16 238 | 89.19 235 | 95.94 174 | 73.85 238 | 90.39 210 | 80.32 232 | 61.17 235 | 73.99 234 | 76.52 236 | 75.82 234 | 83.50 235 | 83.33 229 |
|
CMPMVS | | 71.81 19 | 92.34 192 | 92.85 177 | 91.75 215 | 92.70 231 | 90.43 224 | 88.84 236 | 88.56 224 | 85.87 220 | 94.35 171 | 90.98 202 | 95.89 158 | 91.14 179 | 96.14 142 | 94.83 171 | 94.93 195 | 95.78 133 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PMMVS2 | | | 86.47 228 | 92.62 178 | 79.29 232 | 92.01 232 | 85.63 235 | 93.74 226 | 86.37 227 | 93.95 130 | 54.18 241 | 98.19 93 | 97.39 133 | 58.46 235 | 96.57 126 | 93.07 190 | 90.99 215 | 83.55 228 |
|
test12356 | | | 88.21 222 | 89.73 202 | 86.43 230 | 91.94 233 | 89.52 229 | 91.79 231 | 86.07 229 | 85.51 221 | 81.97 236 | 95.56 151 | 96.20 153 | 79.11 229 | 94.14 188 | 90.94 204 | 87.70 228 | 76.23 233 |
|
MVS-HIRNet | | | 88.72 217 | 86.49 220 | 91.33 218 | 91.81 234 | 85.66 234 | 87.02 238 | 96.25 167 | 81.48 234 | 94.82 162 | 96.31 137 | 92.14 183 | 90.32 189 | 87.60 223 | 83.82 220 | 87.74 227 | 78.42 232 |
|
pmmvs3 | | | 91.20 202 | 91.40 196 | 90.96 219 | 91.71 235 | 91.08 220 | 95.41 211 | 81.34 234 | 87.36 209 | 94.57 167 | 95.02 159 | 94.30 171 | 90.42 187 | 94.28 187 | 89.26 210 | 92.30 212 | 88.49 208 |
|
PatchT | | | 91.40 201 | 88.54 205 | 94.74 185 | 91.48 236 | 92.18 210 | 97.42 149 | 97.51 108 | 84.96 223 | 96.44 109 | 94.16 174 | 75.47 221 | 92.92 161 | 90.22 218 | 92.22 195 | 92.66 210 | 90.56 196 |
|
PMMVS | | | 91.67 199 | 91.47 195 | 91.91 214 | 89.43 237 | 88.61 232 | 94.99 217 | 85.67 230 | 87.50 208 | 93.80 182 | 94.42 173 | 94.88 164 | 90.71 185 | 92.26 206 | 92.96 192 | 96.83 173 | 89.65 200 |
|
MVE | | 72.99 18 | 85.37 230 | 89.43 203 | 80.63 231 | 74.43 238 | 71.94 240 | 88.25 237 | 89.81 222 | 93.27 141 | 67.32 239 | 96.32 136 | 91.83 184 | 90.40 188 | 93.36 194 | 90.79 205 | 73.55 237 | 88.49 208 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | | | 45.72 234 | 60.00 239 | 38.74 241 | 45.50 241 | 12.18 236 | 79.58 236 | 68.42 238 | 67.62 237 | 65.04 232 | 22.12 236 | 84.83 230 | 78.72 229 | 66.08 238 | |
|
testmvs | | | 4.99 234 | 6.88 235 | 2.78 237 | 1.73 240 | 2.04 243 | 3.10 243 | 1.71 237 | 7.27 239 | 3.92 244 | 12.18 238 | 6.71 243 | 3.31 238 | 6.94 237 | 5.51 236 | 2.94 239 | 7.51 236 |
|
test123 | | | 4.41 235 | 5.71 236 | 2.88 236 | 1.28 241 | 2.21 242 | 3.09 244 | 1.65 238 | 6.35 240 | 4.98 243 | 8.53 239 | 3.88 244 | 3.46 237 | 5.79 238 | 5.71 235 | 2.85 241 | 7.50 238 |
|
GG-mvs-BLEND | | | 61.03 233 | 87.02 217 | 30.71 235 | 0.74 242 | 90.01 225 | 78.90 240 | 0.74 239 | 84.56 224 | 9.46 242 | 79.17 234 | 90.69 188 | 1.37 239 | 91.74 209 | 89.13 212 | 93.04 205 | 83.83 227 |
|
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 |
|
MTAPA | | | | | | | | | | | 97.43 67 | | 99.27 22 | | | | | |
|
MTMP | | | | | | | | | | | 97.63 55 | | 99.03 46 | | | | | |
|
Patchmatch-RL test | | | | | | | | 17.42 242 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 89.27 189 | | | | | | | | |
|
Patchmtry | | | | | | | 92.70 204 | 95.23 214 | 98.47 29 | | 96.44 109 | | | | | | | |
|
DeepMVS_CX | | | | | | | 72.99 239 | 80.14 239 | 37.34 235 | 83.46 230 | 60.13 240 | 84.40 220 | 85.48 193 | 86.93 213 | 87.22 224 | | 79.61 236 | 87.32 214 |
|