UA-Net | | | 96.56 13 | 96.73 24 | 96.36 10 | 98.99 1 | 97.90 7 | 97.79 41 | 95.64 10 | 92.78 62 | 92.54 90 | 96.23 80 | 95.02 135 | 94.31 21 | 98.43 15 | 98.12 12 | 98.89 3 | 98.58 2 |
|
zzz-MVS | | | 96.18 23 | 96.01 45 | 96.38 8 | 98.30 2 | 96.18 50 | 98.51 14 | 94.48 22 | 94.56 30 | 94.81 42 | 91.73 144 | 96.96 84 | 94.30 22 | 98.09 22 | 97.83 16 | 97.91 43 | 96.73 33 |
|
mPP-MVS | | | | | | 98.24 3 | | | | | | | 97.65 72 | | | | | |
|
MP-MVS | | | 96.13 25 | 95.93 48 | 96.37 9 | 98.19 4 | 97.31 23 | 98.49 15 | 94.53 21 | 91.39 98 | 94.38 47 | 94.32 117 | 96.43 101 | 94.59 17 | 97.75 39 | 97.44 26 | 98.04 40 | 96.88 29 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
ambc | | | | 94.61 76 | | 98.09 5 | 95.14 85 | 91.71 184 | | 94.18 39 | 96.46 14 | 96.26 77 | 96.30 103 | 91.26 75 | 94.70 103 | 92.00 141 | 93.45 173 | 93.67 86 |
|
HPM-MVS++ | | | 95.21 52 | 94.89 67 | 95.59 24 | 97.79 6 | 95.39 77 | 97.68 43 | 94.05 30 | 91.91 82 | 94.35 48 | 93.38 128 | 95.07 134 | 92.94 41 | 96.01 74 | 95.88 63 | 96.73 73 | 96.61 37 |
|
DeepC-MVS | | 92.47 4 | 96.44 16 | 96.75 23 | 96.08 17 | 97.57 7 | 97.19 28 | 97.96 34 | 94.28 24 | 95.29 21 | 94.92 37 | 98.31 23 | 96.92 86 | 93.69 29 | 96.81 60 | 96.50 47 | 98.06 39 | 96.27 42 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CP-MVS | | | 96.21 22 | 96.16 43 | 96.27 13 | 97.56 8 | 97.13 31 | 98.43 16 | 94.70 17 | 92.62 64 | 94.13 53 | 92.71 135 | 98.03 59 | 94.54 19 | 98.00 28 | 97.60 20 | 98.23 31 | 97.05 20 |
|
ACMMPR | | | 96.54 14 | 96.71 25 | 96.35 11 | 97.55 9 | 97.63 11 | 98.62 10 | 94.54 18 | 94.45 32 | 94.19 50 | 95.04 105 | 97.35 76 | 94.92 13 | 97.85 33 | 97.50 23 | 98.26 30 | 97.17 15 |
|
PGM-MVS | | | 95.90 35 | 95.72 51 | 96.10 16 | 97.53 10 | 97.45 19 | 98.55 13 | 94.12 29 | 90.25 116 | 93.71 65 | 93.20 130 | 97.18 80 | 94.63 16 | 97.68 41 | 97.34 33 | 98.08 37 | 96.97 22 |
|
SMA-MVS | | | 96.11 27 | 96.61 27 | 95.53 28 | 97.49 11 | 97.41 20 | 97.62 46 | 93.78 37 | 94.14 41 | 94.18 51 | 97.16 64 | 94.67 138 | 92.42 49 | 97.74 40 | 97.33 34 | 97.70 48 | 97.79 4 |
|
DTE-MVSNet | | | 97.16 6 | 97.75 9 | 96.47 6 | 97.40 12 | 97.95 5 | 98.20 27 | 96.89 4 | 95.30 20 | 95.15 28 | 98.66 11 | 98.80 18 | 92.77 46 | 98.97 7 | 98.27 10 | 98.44 24 | 96.28 41 |
|
SixPastTwentyTwo | | | 97.36 4 | 97.73 10 | 96.92 2 | 97.36 13 | 96.15 51 | 98.29 23 | 94.43 23 | 96.50 11 | 96.96 8 | 98.74 8 | 98.74 20 | 96.04 3 | 99.03 5 | 97.74 17 | 98.44 24 | 97.22 13 |
|
train_agg | | | 93.89 79 | 93.46 113 | 94.40 54 | 97.35 14 | 93.78 128 | 97.63 45 | 92.19 60 | 88.12 146 | 90.52 130 | 93.57 127 | 95.78 116 | 92.31 52 | 94.78 102 | 93.46 115 | 96.36 84 | 94.70 72 |
|
X-MVS | | | 95.33 50 | 95.13 63 | 95.57 26 | 97.35 14 | 97.48 16 | 98.43 16 | 94.28 24 | 92.30 72 | 93.28 73 | 86.89 190 | 96.82 90 | 91.87 58 | 97.85 33 | 97.59 21 | 98.19 32 | 96.95 25 |
|
HFP-MVS | | | 96.18 23 | 96.53 30 | 95.77 21 | 97.34 16 | 97.26 25 | 98.16 29 | 94.54 18 | 94.45 32 | 92.52 91 | 95.05 103 | 96.95 85 | 93.89 26 | 97.28 45 | 97.46 24 | 98.19 32 | 97.25 10 |
|
APD-MVS | | | 95.38 48 | 95.68 52 | 95.03 43 | 97.30 17 | 96.90 34 | 97.83 38 | 93.92 32 | 89.40 132 | 90.35 132 | 95.41 93 | 97.69 71 | 92.97 39 | 97.24 47 | 97.17 36 | 97.83 45 | 95.96 47 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
EPNet | | | 90.17 144 | 89.07 161 | 91.45 124 | 97.25 18 | 90.62 176 | 94.84 122 | 93.54 41 | 80.96 193 | 91.85 106 | 86.98 189 | 85.88 184 | 77.79 194 | 92.30 153 | 92.58 127 | 93.41 174 | 94.20 80 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
ACMM | | 90.06 9 | 96.31 19 | 96.42 32 | 96.19 15 | 97.21 19 | 97.16 30 | 98.71 5 | 93.79 36 | 94.35 36 | 93.81 61 | 92.80 134 | 98.23 45 | 95.11 9 | 98.07 24 | 97.45 25 | 98.51 18 | 96.86 30 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PEN-MVS | | | 97.16 6 | 97.87 6 | 96.33 12 | 97.20 20 | 97.97 4 | 98.25 25 | 96.86 5 | 95.09 25 | 94.93 36 | 98.66 11 | 99.16 8 | 92.27 53 | 98.98 6 | 98.39 8 | 98.49 19 | 96.83 31 |
|
ACMMP | | | 96.12 26 | 96.27 39 | 95.93 19 | 97.20 20 | 97.60 12 | 98.64 8 | 93.74 38 | 92.47 66 | 93.13 80 | 93.23 129 | 98.06 56 | 94.51 20 | 97.99 29 | 97.57 22 | 98.39 28 | 96.99 21 |
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 |
ACMMP_Plus | | | 95.86 37 | 96.18 40 | 95.47 30 | 97.11 22 | 97.26 25 | 98.37 21 | 93.48 42 | 93.49 48 | 93.99 56 | 95.61 87 | 94.11 145 | 92.49 47 | 97.87 32 | 97.44 26 | 97.40 54 | 97.52 8 |
|
PS-CasMVS | | | 97.22 5 | 97.84 7 | 96.50 5 | 97.08 23 | 97.92 6 | 98.17 28 | 97.02 2 | 94.71 28 | 95.32 24 | 98.52 15 | 98.97 12 | 92.91 42 | 99.04 4 | 98.47 6 | 98.49 19 | 97.24 12 |
|
CP-MVSNet | | | 96.97 10 | 97.42 13 | 96.44 7 | 97.06 24 | 97.82 8 | 98.12 30 | 96.98 3 | 93.50 47 | 95.21 26 | 97.98 32 | 98.44 33 | 92.83 45 | 98.93 8 | 98.37 9 | 98.46 22 | 96.91 28 |
|
SteuartSystems-ACMMP | | | 95.96 32 | 96.13 44 | 95.76 22 | 97.06 24 | 97.36 21 | 98.40 20 | 94.24 26 | 91.49 90 | 91.91 105 | 94.50 113 | 96.89 87 | 94.99 11 | 98.01 27 | 97.44 26 | 97.97 42 | 97.25 10 |
Skip Steuart: Steuart Systems R&D Blog. |
PMVS | | 87.16 16 | 95.88 36 | 96.47 31 | 95.19 38 | 97.00 26 | 96.02 55 | 96.70 67 | 91.57 78 | 94.43 34 | 95.33 23 | 97.16 64 | 95.37 124 | 92.39 50 | 98.89 10 | 98.72 3 | 98.17 34 | 94.71 70 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
WR-MVS | | | 97.53 3 | 98.20 4 | 96.76 3 | 96.93 27 | 98.17 1 | 98.60 11 | 96.67 6 | 96.39 13 | 94.46 44 | 99.14 1 | 98.92 13 | 94.57 18 | 99.06 3 | 98.80 2 | 99.32 1 | 96.92 27 |
|
HSP-MVS | | | 95.04 54 | 95.45 58 | 94.57 50 | 96.87 28 | 97.77 10 | 98.71 5 | 93.88 34 | 91.21 103 | 91.48 113 | 95.36 94 | 98.37 39 | 90.73 94 | 94.37 108 | 92.98 122 | 95.77 117 | 98.08 3 |
|
TSAR-MVS + MP. | | | 95.99 31 | 96.57 29 | 95.31 33 | 96.87 28 | 96.50 43 | 98.71 5 | 91.58 77 | 93.25 53 | 92.71 86 | 96.86 70 | 96.57 97 | 93.92 24 | 98.09 22 | 97.91 14 | 98.08 37 | 96.81 32 |
|
XVS | | | | | | 96.86 30 | 97.48 16 | 98.73 3 | | | 93.28 73 | | 96.82 90 | | | | 98.17 34 | |
|
X-MVStestdata | | | | | | 96.86 30 | 97.48 16 | 98.73 3 | | | 93.28 73 | | 96.82 90 | | | | 98.17 34 | |
|
NCCC | | | 93.87 82 | 93.42 114 | 94.40 54 | 96.84 32 | 95.42 74 | 96.47 79 | 92.62 49 | 92.36 70 | 92.05 101 | 83.83 206 | 95.55 118 | 91.84 60 | 95.89 76 | 95.23 77 | 96.56 77 | 95.63 53 |
|
CPTT-MVS | | | 95.00 55 | 94.52 78 | 95.57 26 | 96.84 32 | 96.78 35 | 97.88 36 | 93.67 40 | 92.20 74 | 92.35 96 | 85.87 197 | 97.56 73 | 94.98 12 | 96.96 53 | 96.07 58 | 97.70 48 | 96.18 43 |
|
DeepC-MVS_fast | | 91.38 6 | 94.73 59 | 94.98 64 | 94.44 51 | 96.83 34 | 96.12 52 | 96.69 69 | 92.17 61 | 92.98 58 | 93.72 64 | 94.14 119 | 95.45 122 | 90.49 103 | 95.73 80 | 95.30 74 | 96.71 74 | 95.13 64 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ESAPD | | | 95.63 42 | 96.35 34 | 94.80 49 | 96.76 35 | 97.29 24 | 97.74 42 | 94.15 28 | 91.69 85 | 90.01 137 | 96.65 72 | 97.29 77 | 92.45 48 | 97.41 43 | 97.18 35 | 97.67 51 | 96.95 25 |
|
WR-MVS_H | | | 97.06 9 | 97.78 8 | 96.23 14 | 96.74 36 | 98.04 3 | 98.25 25 | 97.32 1 | 94.40 35 | 93.71 65 | 98.55 14 | 98.89 14 | 92.97 39 | 98.91 9 | 98.45 7 | 98.38 29 | 97.19 14 |
|
SD-MVS | | | 95.77 40 | 96.17 41 | 95.30 34 | 96.72 37 | 96.19 49 | 97.01 52 | 93.04 45 | 94.03 42 | 92.71 86 | 96.45 75 | 96.78 94 | 93.91 25 | 96.79 61 | 95.89 62 | 98.42 26 | 97.09 18 |
|
LGP-MVS_train | | | 96.10 28 | 96.29 37 | 95.87 20 | 96.72 37 | 97.35 22 | 98.43 16 | 93.83 35 | 90.81 113 | 92.67 89 | 95.05 103 | 98.86 16 | 95.01 10 | 98.11 21 | 97.37 31 | 98.52 17 | 96.50 38 |
|
OPM-MVS | | | 95.96 32 | 96.59 28 | 95.23 36 | 96.67 39 | 96.52 42 | 97.86 37 | 93.28 43 | 95.27 23 | 93.46 70 | 96.26 77 | 98.85 17 | 92.89 43 | 97.09 49 | 96.37 50 | 97.22 62 | 95.78 51 |
|
APDe-MVS | | | 96.23 21 | 97.22 17 | 95.08 42 | 96.66 40 | 97.56 14 | 98.63 9 | 93.69 39 | 94.62 29 | 89.80 140 | 97.73 44 | 98.13 53 | 93.84 27 | 97.79 37 | 97.63 19 | 97.87 44 | 97.08 19 |
|
test20.03 | | | 88.20 171 | 91.26 143 | 84.63 201 | 96.64 41 | 89.39 180 | 90.73 196 | 89.97 108 | 91.07 106 | 72.02 222 | 94.98 106 | 95.45 122 | 69.35 214 | 92.70 139 | 91.19 161 | 89.06 194 | 84.02 186 |
|
ACMP | | 89.62 11 | 95.96 32 | 96.28 38 | 95.59 24 | 96.58 42 | 97.23 27 | 98.26 24 | 93.22 44 | 92.33 71 | 92.31 97 | 94.29 118 | 98.73 21 | 94.68 15 | 98.04 25 | 97.14 38 | 98.47 21 | 96.17 44 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
CNVR-MVS | | | 94.24 70 | 94.47 80 | 93.96 69 | 96.56 43 | 95.67 66 | 96.43 82 | 91.95 68 | 92.08 77 | 91.28 117 | 90.51 156 | 95.35 125 | 91.20 76 | 96.34 71 | 95.50 71 | 96.34 89 | 95.88 48 |
|
TSAR-MVS + GP. | | | 94.25 69 | 94.81 70 | 93.60 82 | 96.52 44 | 95.80 63 | 94.37 131 | 92.47 53 | 90.89 110 | 88.92 143 | 95.34 95 | 94.38 142 | 92.85 44 | 96.36 70 | 95.62 68 | 96.47 79 | 95.28 61 |
|
LS3D | | | 95.83 39 | 96.35 34 | 95.22 37 | 96.47 45 | 97.49 15 | 97.99 31 | 92.35 55 | 94.92 26 | 94.58 43 | 94.88 108 | 95.11 133 | 91.52 67 | 98.48 14 | 98.05 13 | 98.42 26 | 95.49 56 |
|
DU-MVS | | | 95.51 44 | 95.68 52 | 95.33 32 | 96.45 46 | 96.44 45 | 96.61 74 | 95.32 11 | 89.97 122 | 93.78 62 | 97.46 57 | 98.07 55 | 91.19 77 | 97.03 50 | 96.53 45 | 98.61 14 | 94.22 78 |
|
Baseline_NR-MVSNet | | | 94.85 56 | 95.35 60 | 94.26 56 | 96.45 46 | 93.86 127 | 96.70 67 | 94.54 18 | 90.07 120 | 90.17 136 | 98.77 7 | 97.89 64 | 90.64 98 | 97.03 50 | 96.16 54 | 97.04 69 | 93.67 86 |
|
LTVRE_ROB | | 95.06 1 | 97.73 1 | 98.39 2 | 96.95 1 | 96.33 48 | 96.94 32 | 98.30 22 | 94.90 15 | 98.61 2 | 97.73 3 | 97.97 33 | 98.57 29 | 95.74 7 | 99.24 1 | 98.70 4 | 98.72 7 | 98.70 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 |
CDPH-MVS | | | 93.96 74 | 93.86 93 | 94.08 61 | 96.31 49 | 95.84 61 | 96.92 56 | 91.85 71 | 87.21 161 | 91.25 119 | 92.83 132 | 96.06 112 | 91.05 86 | 95.57 81 | 94.81 87 | 97.12 63 | 94.72 69 |
|
PVSNet_Blended_VisFu | | | 93.60 88 | 93.41 115 | 93.83 74 | 96.31 49 | 95.65 67 | 95.71 107 | 90.58 99 | 88.08 149 | 93.17 78 | 95.29 97 | 92.20 159 | 90.72 95 | 94.69 104 | 93.41 118 | 96.51 78 | 94.54 74 |
|
TranMVSNet+NR-MVSNet | | | 95.72 41 | 96.42 32 | 94.91 46 | 96.21 51 | 96.77 36 | 96.90 59 | 94.99 13 | 92.62 64 | 91.92 104 | 98.51 16 | 98.63 26 | 90.82 93 | 97.27 46 | 96.83 41 | 98.63 13 | 94.31 77 |
|
3Dnovator+ | | 92.82 3 | 95.22 51 | 95.16 62 | 95.29 35 | 96.17 52 | 96.55 38 | 97.64 44 | 94.02 31 | 94.16 40 | 94.29 49 | 92.09 141 | 93.71 150 | 91.90 56 | 96.68 63 | 96.51 46 | 97.70 48 | 96.40 39 |
|
UniMVSNet (Re) | | | 95.46 45 | 95.86 49 | 95.00 44 | 96.09 53 | 96.60 37 | 96.68 71 | 94.99 13 | 90.36 115 | 92.13 100 | 97.64 52 | 98.13 53 | 91.38 70 | 96.90 55 | 96.74 42 | 98.73 6 | 94.63 73 |
|
MIMVSNet1 | | | 92.52 121 | 94.88 68 | 89.77 146 | 96.09 53 | 91.99 163 | 96.92 56 | 89.68 116 | 95.92 17 | 84.55 172 | 96.64 73 | 98.21 48 | 78.44 190 | 96.08 73 | 95.10 79 | 92.91 181 | 90.22 141 |
|
TSAR-MVS + ACMM | | | 95.17 53 | 95.95 46 | 94.26 56 | 96.07 55 | 96.46 44 | 95.67 109 | 94.21 27 | 93.84 44 | 90.99 123 | 97.18 63 | 95.24 132 | 93.55 31 | 96.60 66 | 95.61 69 | 95.06 135 | 96.69 35 |
|
MVS_0304 | | | 93.92 76 | 93.81 98 | 94.05 62 | 96.06 56 | 96.00 56 | 96.43 82 | 92.76 48 | 85.99 170 | 94.43 46 | 94.04 122 | 97.08 81 | 88.12 125 | 94.65 105 | 94.20 105 | 96.47 79 | 94.71 70 |
|
CSCG | | | 96.07 29 | 97.15 19 | 94.81 47 | 96.06 56 | 97.58 13 | 96.52 77 | 90.98 90 | 96.51 10 | 93.60 68 | 97.13 66 | 98.55 31 | 93.01 38 | 97.17 48 | 95.36 73 | 98.68 9 | 97.78 5 |
|
Anonymous20240521 | | | 95.52 43 | 97.08 20 | 93.69 81 | 96.01 58 | 95.99 57 | 96.24 96 | 92.87 47 | 94.91 27 | 88.51 149 | 98.51 16 | 98.72 23 | 90.09 107 | 98.43 15 | 97.37 31 | 98.46 22 | 95.60 54 |
|
COLMAP_ROB | | 93.74 2 | 97.09 8 | 97.98 5 | 96.05 18 | 95.97 59 | 97.78 9 | 98.56 12 | 91.72 74 | 97.53 7 | 96.01 17 | 98.14 27 | 98.76 19 | 95.28 8 | 98.76 11 | 98.23 11 | 98.77 5 | 96.67 36 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
AdaColmap | | | 92.41 124 | 91.49 141 | 93.48 84 | 95.96 60 | 95.02 90 | 95.37 115 | 91.73 73 | 87.97 152 | 91.28 117 | 82.82 211 | 91.04 165 | 90.62 100 | 95.82 78 | 95.07 80 | 95.95 110 | 92.67 108 |
|
Gipuma | | | 95.86 37 | 96.17 41 | 95.50 29 | 95.92 61 | 94.59 105 | 94.77 124 | 92.50 51 | 97.82 6 | 97.90 2 | 95.56 89 | 97.88 67 | 94.71 14 | 98.02 26 | 94.81 87 | 97.23 61 | 94.48 76 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
CANet | | | 93.07 109 | 93.05 123 | 93.10 104 | 95.90 62 | 95.41 75 | 95.88 101 | 91.94 69 | 84.77 177 | 93.36 71 | 94.05 121 | 95.25 131 | 86.25 135 | 94.33 109 | 93.94 107 | 95.30 124 | 93.58 89 |
|
TDRefinement | | | 97.59 2 | 98.32 3 | 96.73 4 | 95.90 62 | 98.10 2 | 99.08 2 | 93.92 32 | 98.24 4 | 96.44 15 | 98.12 28 | 97.86 69 | 96.06 2 | 99.24 1 | 98.93 1 | 99.00 2 | 97.77 6 |
|
Anonymous20231211 | | | 96.59 12 | 98.43 1 | 94.44 51 | 95.89 64 | 96.12 52 | 95.23 117 | 95.91 8 | 99.42 1 | 92.75 85 | 98.87 5 | 99.94 1 | 88.19 123 | 98.64 13 | 98.50 5 | 98.66 10 | 97.49 9 |
|
EG-PatchMatch MVS | | | 94.81 57 | 95.53 55 | 93.97 67 | 95.89 64 | 94.62 102 | 95.55 113 | 88.18 150 | 92.77 63 | 94.88 39 | 97.04 68 | 98.61 27 | 93.31 33 | 96.89 56 | 95.19 78 | 95.99 109 | 93.56 90 |
|
gm-plane-assit | | | 86.15 185 | 82.51 201 | 90.40 135 | 95.81 66 | 92.29 157 | 97.99 31 | 84.66 195 | 92.15 76 | 93.15 79 | 97.84 39 | 44.65 239 | 78.60 186 | 88.02 197 | 85.95 194 | 92.20 183 | 76.69 215 |
|
UniMVSNet_NR-MVSNet | | | 95.34 49 | 95.51 56 | 95.14 39 | 95.80 67 | 96.55 38 | 96.61 74 | 94.79 16 | 90.04 121 | 93.78 62 | 97.51 55 | 97.25 78 | 91.19 77 | 96.68 63 | 96.31 53 | 98.65 12 | 94.22 78 |
|
HQP-MVS | | | 92.87 113 | 92.49 129 | 93.31 91 | 95.75 68 | 95.01 91 | 95.64 110 | 91.06 88 | 88.54 143 | 91.62 112 | 88.16 176 | 96.25 105 | 89.47 111 | 92.26 154 | 91.81 143 | 96.34 89 | 95.40 57 |
|
RPSCF | | | 95.46 45 | 96.95 22 | 93.73 80 | 95.72 69 | 95.94 59 | 95.58 112 | 88.08 154 | 95.31 19 | 91.34 115 | 96.26 77 | 98.04 58 | 93.63 30 | 98.28 18 | 97.67 18 | 98.01 41 | 97.13 16 |
|
EPNet_dtu | | | 87.40 181 | 86.27 187 | 88.72 161 | 95.68 70 | 83.37 207 | 92.09 176 | 90.08 103 | 78.11 219 | 91.29 116 | 86.33 193 | 89.74 171 | 75.39 206 | 89.07 186 | 87.89 186 | 87.81 199 | 89.38 153 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
testgi | | | 86.49 183 | 90.31 149 | 82.03 206 | 95.63 71 | 88.18 184 | 93.47 148 | 84.89 193 | 93.23 55 | 69.54 229 | 87.16 187 | 97.96 62 | 60.66 223 | 91.90 164 | 89.90 171 | 87.99 197 | 83.84 187 |
|
v7n | | | 96.49 15 | 97.20 18 | 95.65 23 | 95.57 72 | 96.04 54 | 97.93 35 | 92.49 52 | 96.40 12 | 97.13 7 | 98.99 4 | 99.41 4 | 93.79 28 | 97.84 35 | 96.15 55 | 97.00 70 | 95.60 54 |
|
MSLP-MVS++ | | | 93.91 77 | 94.30 86 | 93.45 85 | 95.51 73 | 95.83 62 | 93.12 160 | 91.93 70 | 91.45 95 | 91.40 114 | 87.42 185 | 96.12 111 | 93.27 34 | 96.57 67 | 96.40 49 | 95.49 121 | 96.29 40 |
|
CLD-MVS | | | 92.81 115 | 94.32 84 | 91.05 127 | 95.39 74 | 95.31 79 | 95.82 103 | 81.44 212 | 89.40 132 | 91.94 103 | 95.86 84 | 97.36 75 | 85.83 137 | 95.35 85 | 94.59 99 | 95.85 114 | 92.34 119 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
MCST-MVS | | | 93.60 88 | 93.40 117 | 93.83 74 | 95.30 75 | 95.40 76 | 96.49 78 | 90.87 91 | 90.08 119 | 91.72 110 | 90.28 158 | 95.99 114 | 91.69 63 | 93.94 122 | 92.99 121 | 96.93 72 | 95.13 64 |
|
ACMH | | 90.17 8 | 96.61 11 | 97.69 11 | 95.35 31 | 95.29 76 | 96.94 32 | 98.43 16 | 92.05 66 | 98.04 5 | 95.38 22 | 98.07 30 | 99.25 7 | 93.23 36 | 98.35 17 | 97.16 37 | 97.72 46 | 96.00 46 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
FC-MVSNet-train | | | 92.75 117 | 95.40 59 | 89.66 150 | 95.21 77 | 94.82 96 | 97.00 53 | 89.40 128 | 91.13 104 | 81.71 190 | 97.72 45 | 96.43 101 | 77.57 197 | 96.89 56 | 96.72 43 | 97.05 68 | 94.09 81 |
|
ACMH+ | | 89.90 10 | 96.27 20 | 97.52 12 | 94.81 47 | 95.19 78 | 97.18 29 | 97.97 33 | 92.52 50 | 96.72 9 | 90.50 131 | 97.31 60 | 99.11 9 | 94.10 23 | 98.67 12 | 97.90 15 | 98.56 16 | 95.79 50 |
|
EPP-MVSNet | | | 93.63 87 | 93.95 90 | 93.26 95 | 95.15 79 | 96.54 41 | 96.18 98 | 91.97 67 | 91.74 84 | 85.76 164 | 94.95 107 | 84.27 188 | 91.60 66 | 97.61 42 | 97.38 30 | 98.87 4 | 95.18 63 |
|
Effi-MVS+-dtu | | | 92.32 128 | 91.66 139 | 93.09 105 | 95.13 80 | 94.73 99 | 94.57 129 | 92.14 62 | 81.74 190 | 90.33 133 | 88.13 177 | 95.91 115 | 89.24 112 | 94.23 118 | 93.65 114 | 97.12 63 | 93.23 95 |
|
MVS_111021_HR | | | 93.82 84 | 94.26 88 | 93.31 91 | 95.01 81 | 93.97 124 | 95.73 106 | 89.75 114 | 92.06 78 | 92.49 92 | 94.01 124 | 96.05 113 | 90.61 101 | 95.95 75 | 94.78 90 | 96.28 94 | 93.04 100 |
|
FC-MVSNet-test | | | 91.49 136 | 94.43 81 | 88.07 175 | 94.97 82 | 90.53 177 | 95.42 114 | 91.18 85 | 93.24 54 | 72.94 220 | 98.37 19 | 93.86 148 | 78.78 184 | 97.82 36 | 96.13 57 | 95.13 131 | 91.05 135 |
|
PHI-MVS | | | 94.65 60 | 94.84 69 | 94.44 51 | 94.95 83 | 96.55 38 | 96.46 80 | 91.10 87 | 88.96 136 | 96.00 18 | 94.55 112 | 95.32 127 | 90.67 96 | 96.97 52 | 96.69 44 | 97.44 53 | 94.84 66 |
|
IS_MVSNet | | | 92.76 116 | 93.25 121 | 92.19 116 | 94.91 84 | 95.56 68 | 95.86 102 | 92.12 63 | 88.10 147 | 82.71 184 | 93.15 131 | 88.30 177 | 88.86 115 | 97.29 44 | 96.95 40 | 98.66 10 | 93.38 92 |
|
pmmvs6 | | | 94.58 61 | 97.30 16 | 91.40 125 | 94.84 85 | 94.61 103 | 93.40 150 | 92.43 54 | 98.51 3 | 85.61 167 | 98.73 10 | 99.53 3 | 84.40 144 | 97.88 31 | 97.03 39 | 97.72 46 | 94.79 68 |
|
PLC | | 87.27 15 | 93.08 108 | 92.92 124 | 93.26 95 | 94.67 86 | 95.03 88 | 94.38 130 | 90.10 102 | 91.69 85 | 92.14 99 | 87.24 186 | 93.91 147 | 91.61 65 | 95.05 96 | 94.73 96 | 96.67 76 | 92.80 104 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
PCF-MVS | | 87.46 14 | 92.44 123 | 91.80 137 | 93.19 99 | 94.66 87 | 95.80 63 | 96.37 92 | 90.19 101 | 87.57 154 | 92.23 98 | 89.26 167 | 93.97 146 | 89.24 112 | 91.32 169 | 90.82 165 | 96.46 81 | 93.86 85 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
Vis-MVSNet (Re-imp) | | | 90.68 140 | 92.18 132 | 88.92 159 | 94.63 88 | 92.75 148 | 92.91 164 | 91.20 84 | 89.21 135 | 75.01 215 | 93.96 125 | 89.07 175 | 82.72 156 | 95.88 77 | 95.30 74 | 97.08 67 | 89.08 157 |
|
TSAR-MVS + COLMAP | | | 93.06 110 | 93.65 103 | 92.36 112 | 94.62 89 | 94.28 111 | 95.36 116 | 89.46 127 | 92.18 75 | 91.64 111 | 95.55 90 | 95.27 130 | 88.60 119 | 93.24 131 | 92.50 128 | 94.46 161 | 92.55 113 |
|
CNLPA | | | 93.14 107 | 93.67 102 | 92.53 111 | 94.62 89 | 94.73 99 | 95.00 120 | 86.57 178 | 92.85 61 | 92.43 93 | 90.94 149 | 94.67 138 | 90.35 105 | 95.41 83 | 93.70 110 | 96.23 99 | 93.37 93 |
|
OMC-MVS | | | 94.74 58 | 95.46 57 | 93.91 72 | 94.62 89 | 96.26 48 | 96.64 73 | 89.36 130 | 94.20 38 | 94.15 52 | 94.02 123 | 97.73 70 | 91.34 72 | 96.15 72 | 95.04 81 | 97.37 55 | 94.80 67 |
|
conf0.05thres1000 | | | 91.24 137 | 91.85 136 | 90.53 133 | 94.59 92 | 94.56 107 | 94.33 135 | 89.52 124 | 93.67 46 | 83.77 177 | 91.04 147 | 79.10 205 | 83.98 145 | 96.66 65 | 95.56 70 | 96.98 71 | 92.36 117 |
|
CDS-MVSNet | | | 88.41 164 | 89.79 152 | 86.79 187 | 94.55 93 | 90.82 173 | 92.50 172 | 89.85 112 | 83.26 186 | 80.52 196 | 91.05 146 | 89.93 170 | 69.11 215 | 93.17 134 | 92.71 126 | 94.21 166 | 87.63 171 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
v748 | | | 96.05 30 | 97.00 21 | 94.95 45 | 94.41 94 | 94.77 98 | 96.72 66 | 91.03 89 | 96.12 16 | 96.71 11 | 98.74 8 | 99.59 2 | 93.55 31 | 97.97 30 | 95.96 59 | 97.28 58 | 95.84 49 |
|
TransMVSNet (Re) | | | 93.55 92 | 96.32 36 | 90.32 137 | 94.38 95 | 94.05 119 | 93.30 157 | 89.53 123 | 97.15 8 | 85.12 169 | 98.83 6 | 97.89 64 | 82.21 159 | 96.75 62 | 96.14 56 | 97.35 56 | 93.46 91 |
|
tfpn | | | 87.65 178 | 85.66 190 | 89.96 143 | 94.36 96 | 93.94 125 | 93.85 144 | 89.02 135 | 88.71 142 | 82.78 182 | 83.79 207 | 53.79 234 | 83.43 150 | 95.35 85 | 94.54 100 | 96.35 88 | 89.51 152 |
|
Effi-MVS+ | | | 92.93 111 | 92.16 134 | 93.83 74 | 94.29 97 | 93.53 138 | 95.04 119 | 92.98 46 | 85.27 174 | 94.46 44 | 90.24 159 | 95.34 126 | 89.99 108 | 93.72 124 | 94.23 104 | 96.22 100 | 92.79 105 |
|
MAR-MVS | | | 91.86 134 | 91.14 144 | 92.71 108 | 94.29 97 | 94.24 112 | 94.91 121 | 91.82 72 | 81.66 191 | 93.32 72 | 84.51 204 | 93.42 153 | 86.86 131 | 95.16 94 | 94.44 102 | 95.05 136 | 94.53 75 |
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 |
NR-MVSNet | | | 94.55 63 | 95.66 54 | 93.25 97 | 94.26 99 | 96.44 45 | 96.69 69 | 95.32 11 | 89.97 122 | 91.79 109 | 97.46 57 | 98.39 38 | 82.85 153 | 96.87 58 | 96.48 48 | 98.57 15 | 93.98 83 |
|
1111 | | | 76.85 225 | 78.03 221 | 75.46 225 | 94.16 100 | 78.29 221 | 86.40 222 | 89.12 132 | 87.23 159 | 61.26 233 | 95.15 100 | 44.14 240 | 51.46 232 | 86.04 207 | 81.00 208 | 70.40 235 | 74.37 221 |
|
.test1245 | | | 60.07 232 | 56.75 234 | 63.93 232 | 94.16 100 | 78.29 221 | 86.40 222 | 89.12 132 | 87.23 159 | 61.26 233 | 95.15 100 | 44.14 240 | 51.46 232 | 86.04 207 | 2.51 236 | 1.21 240 | 3.92 237 |
|
TAPA-MVS | | 88.94 13 | 93.78 85 | 94.31 85 | 93.18 100 | 94.14 102 | 95.99 57 | 95.74 105 | 86.98 172 | 93.43 50 | 93.88 60 | 90.16 160 | 96.88 88 | 91.05 86 | 94.33 109 | 93.95 106 | 97.28 58 | 95.40 57 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
pm-mvs1 | | | 93.27 101 | 95.94 47 | 90.16 138 | 94.13 103 | 93.66 130 | 92.61 170 | 89.91 111 | 95.73 18 | 84.28 175 | 98.51 16 | 98.29 41 | 82.80 154 | 96.44 68 | 95.76 64 | 97.25 60 | 93.21 96 |
|
DeepPCF-MVS | | 90.68 7 | 94.56 62 | 94.92 66 | 94.15 58 | 94.11 104 | 95.71 65 | 97.03 51 | 90.65 96 | 93.39 52 | 94.08 54 | 95.29 97 | 94.15 144 | 93.21 37 | 95.22 92 | 94.92 85 | 95.82 116 | 95.75 52 |
|
view800 | | | 89.42 151 | 89.11 160 | 89.78 145 | 94.00 105 | 93.71 129 | 93.96 141 | 88.47 149 | 88.10 147 | 82.91 180 | 82.61 212 | 79.85 203 | 83.10 152 | 94.92 99 | 95.38 72 | 96.26 98 | 89.19 154 |
|
Anonymous20231206 | | | 87.45 180 | 89.66 155 | 84.87 198 | 94.00 105 | 87.73 191 | 91.36 188 | 86.41 181 | 88.89 139 | 75.03 214 | 92.59 136 | 96.82 90 | 72.48 212 | 89.72 181 | 88.06 185 | 89.93 191 | 83.81 188 |
|
test-LLR | | | 80.62 210 | 77.20 226 | 84.62 202 | 93.99 107 | 75.11 228 | 87.04 216 | 87.32 167 | 70.11 232 | 78.59 209 | 83.17 209 | 71.60 216 | 73.88 210 | 82.32 218 | 79.20 214 | 86.91 203 | 78.87 211 |
|
test0.0.03 1 | | | 81.51 206 | 83.30 200 | 79.42 214 | 93.99 107 | 86.50 195 | 85.93 226 | 87.32 167 | 78.16 217 | 61.62 232 | 80.78 216 | 81.78 196 | 59.87 224 | 88.40 193 | 87.27 189 | 87.78 201 | 80.19 203 |
|
tfpn_n400 | | | 89.03 156 | 89.39 157 | 88.61 163 | 93.98 109 | 92.33 155 | 91.83 179 | 88.97 137 | 92.97 59 | 78.90 202 | 84.93 200 | 78.24 207 | 81.77 167 | 95.00 97 | 93.67 111 | 96.22 100 | 88.59 162 |
|
tfpnconf | | | 89.03 156 | 89.39 157 | 88.61 163 | 93.98 109 | 92.33 155 | 91.83 179 | 88.97 137 | 92.97 59 | 78.90 202 | 84.93 200 | 78.24 207 | 81.77 167 | 95.00 97 | 93.67 111 | 96.22 100 | 88.59 162 |
|
v1240 | | | 93.89 79 | 93.72 101 | 94.09 59 | 93.98 109 | 94.31 109 | 97.12 48 | 89.37 129 | 90.74 114 | 96.92 9 | 98.05 31 | 97.89 64 | 92.15 54 | 91.53 167 | 91.60 149 | 94.99 138 | 91.93 126 |
|
tfpnnormal | | | 92.45 122 | 94.77 72 | 89.74 147 | 93.95 112 | 93.44 140 | 93.25 158 | 88.49 148 | 95.27 23 | 83.20 179 | 96.51 74 | 96.23 106 | 83.17 151 | 95.47 82 | 94.52 101 | 96.38 83 | 91.97 125 |
|
v11 | | | 94.32 68 | 94.62 75 | 93.97 67 | 93.95 112 | 95.31 79 | 96.83 62 | 91.30 83 | 91.95 80 | 95.51 20 | 98.32 22 | 98.61 27 | 91.44 69 | 92.83 137 | 92.23 133 | 94.77 146 | 93.08 99 |
|
tfpn1000 | | | 88.13 173 | 88.68 169 | 87.49 181 | 93.94 114 | 92.64 151 | 91.50 187 | 88.70 146 | 90.12 118 | 74.35 217 | 86.74 192 | 75.27 213 | 80.14 175 | 94.16 119 | 94.66 97 | 96.33 91 | 87.16 176 |
|
canonicalmvs | | | 93.38 98 | 94.36 83 | 92.24 115 | 93.94 114 | 96.41 47 | 94.18 138 | 90.47 100 | 93.07 57 | 88.47 150 | 88.66 171 | 93.78 149 | 88.80 116 | 95.74 79 | 95.75 65 | 97.57 52 | 97.13 16 |
|
tfpnview11 | | | 88.74 161 | 88.95 162 | 88.50 165 | 93.91 116 | 92.43 154 | 91.70 185 | 88.90 142 | 90.93 109 | 78.90 202 | 84.93 200 | 78.24 207 | 81.71 169 | 94.32 111 | 94.60 98 | 95.86 112 | 87.23 175 |
|
v1192 | | | 93.98 73 | 93.94 91 | 94.01 64 | 93.91 116 | 94.63 101 | 97.00 53 | 89.75 114 | 91.01 107 | 96.50 12 | 97.93 34 | 98.26 43 | 91.74 61 | 92.06 156 | 92.05 138 | 95.18 130 | 91.66 132 |
|
MVS_111021_LR | | | 93.15 106 | 93.65 103 | 92.56 110 | 93.89 118 | 92.28 158 | 95.09 118 | 86.92 174 | 91.26 102 | 92.99 83 | 94.46 115 | 96.22 107 | 90.64 98 | 95.11 95 | 93.45 116 | 95.85 114 | 92.74 107 |
|
TinyColmap | | | 93.17 105 | 93.33 120 | 93.00 107 | 93.84 119 | 92.76 147 | 94.75 126 | 88.90 142 | 93.97 43 | 97.48 4 | 95.28 99 | 95.29 128 | 88.37 121 | 95.31 90 | 91.58 150 | 94.65 152 | 89.10 156 |
|
v52 | | | 96.35 17 | 97.40 14 | 95.12 40 | 93.83 120 | 95.54 69 | 97.82 39 | 88.95 140 | 96.27 14 | 97.22 5 | 99.11 2 | 99.40 5 | 95.80 5 | 98.16 20 | 96.37 50 | 97.10 65 | 96.96 23 |
|
V4 | | | 96.35 17 | 97.40 14 | 95.12 40 | 93.83 120 | 95.54 69 | 97.82 39 | 88.95 140 | 96.27 14 | 97.21 6 | 99.10 3 | 99.40 5 | 95.79 6 | 98.17 19 | 96.37 50 | 97.10 65 | 96.96 23 |
|
Fast-Effi-MVS+ | | | 92.93 111 | 92.64 128 | 93.27 94 | 93.81 122 | 93.88 126 | 95.90 100 | 90.61 97 | 83.98 182 | 92.71 86 | 92.81 133 | 96.22 107 | 90.67 96 | 94.90 101 | 93.92 108 | 95.92 111 | 92.77 106 |
|
v13 | | | 94.54 64 | 94.93 65 | 94.09 59 | 93.81 122 | 95.44 73 | 96.99 55 | 91.67 75 | 92.43 68 | 95.20 27 | 98.33 20 | 98.73 21 | 91.87 58 | 93.67 126 | 92.26 131 | 95.00 137 | 93.63 88 |
|
abl_6 | | | | | 91.88 120 | 93.76 124 | 94.98 93 | 95.64 110 | 88.97 137 | 86.20 168 | 90.00 138 | 86.31 194 | 94.50 141 | 87.31 127 | | | 95.60 119 | 92.48 115 |
|
v12 | | | 94.44 65 | 94.79 71 | 94.02 63 | 93.75 125 | 95.37 78 | 96.92 56 | 91.61 76 | 92.21 73 | 95.10 29 | 98.27 24 | 98.69 24 | 91.73 62 | 93.49 128 | 92.15 136 | 94.97 141 | 93.37 93 |
|
v1921920 | | | 93.90 78 | 93.82 96 | 94.00 65 | 93.74 126 | 94.31 109 | 97.12 48 | 89.33 131 | 91.13 104 | 96.77 10 | 97.90 35 | 98.06 56 | 91.95 55 | 91.93 163 | 91.54 151 | 95.10 133 | 91.85 127 |
|
v1144 | | | 93.83 83 | 93.87 92 | 93.78 77 | 93.72 127 | 94.57 106 | 96.85 60 | 89.98 107 | 91.31 100 | 95.90 19 | 97.89 36 | 98.40 37 | 91.13 81 | 92.01 159 | 92.01 140 | 95.10 133 | 90.94 136 |
|
thres600view7 | | | 89.14 154 | 88.83 164 | 89.51 153 | 93.71 128 | 93.55 136 | 93.93 142 | 88.02 155 | 87.30 158 | 82.40 185 | 81.18 215 | 80.63 201 | 82.69 157 | 94.27 113 | 95.90 61 | 96.27 96 | 88.94 158 |
|
V9 | | | 94.33 67 | 94.66 74 | 93.94 70 | 93.69 129 | 95.31 79 | 96.84 61 | 91.53 79 | 92.04 79 | 95.00 33 | 98.22 25 | 98.64 25 | 91.62 64 | 93.29 130 | 92.05 138 | 94.93 142 | 93.10 98 |
|
V14 | | | 94.21 71 | 94.52 78 | 93.85 73 | 93.62 130 | 95.25 82 | 96.76 65 | 91.42 80 | 91.83 83 | 94.91 38 | 98.15 26 | 98.57 29 | 91.49 68 | 93.06 135 | 91.93 142 | 94.90 143 | 92.82 103 |
|
view600 | | | 89.09 155 | 88.78 167 | 89.46 154 | 93.59 131 | 93.33 142 | 93.92 143 | 87.76 160 | 87.40 155 | 82.79 181 | 81.29 214 | 80.71 200 | 82.59 158 | 94.28 112 | 95.72 66 | 96.12 106 | 88.70 161 |
|
v15 | | | 94.09 72 | 94.37 82 | 93.77 78 | 93.56 132 | 95.18 83 | 96.68 71 | 91.34 82 | 91.64 87 | 94.83 41 | 98.09 29 | 98.51 32 | 91.37 71 | 92.84 136 | 91.80 144 | 94.85 144 | 92.53 114 |
|
v144192 | | | 93.89 79 | 93.85 94 | 93.94 70 | 93.50 133 | 94.33 108 | 97.12 48 | 89.49 125 | 90.89 110 | 96.49 13 | 97.78 43 | 98.27 42 | 91.89 57 | 92.17 155 | 91.70 146 | 95.19 129 | 91.78 130 |
|
v1141 | | | 93.47 95 | 93.56 108 | 93.36 88 | 93.48 134 | 94.17 117 | 96.42 85 | 89.62 117 | 91.44 96 | 94.99 35 | 97.81 41 | 98.42 35 | 90.94 91 | 92.00 160 | 91.38 158 | 94.74 149 | 89.69 150 |
|
divwei89l23v2f112 | | | 93.47 95 | 93.56 108 | 93.37 86 | 93.48 134 | 94.17 117 | 96.42 85 | 89.62 117 | 91.46 93 | 95.00 33 | 97.81 41 | 98.42 35 | 90.94 91 | 92.00 160 | 91.38 158 | 94.75 147 | 89.70 148 |
|
v1 | | | 93.48 94 | 93.57 107 | 93.37 86 | 93.48 134 | 94.18 116 | 96.41 87 | 89.61 119 | 91.46 93 | 95.03 30 | 97.82 40 | 98.43 34 | 90.95 90 | 92.00 160 | 91.37 160 | 94.75 147 | 89.70 148 |
|
v2v482 | | | 93.42 97 | 93.49 112 | 93.32 90 | 93.44 137 | 94.05 119 | 96.36 94 | 89.76 113 | 91.41 97 | 95.24 25 | 97.63 53 | 98.34 40 | 90.44 104 | 91.65 165 | 91.76 145 | 94.69 150 | 89.62 151 |
|
v7 | | | 93.65 86 | 93.73 100 | 93.57 83 | 93.38 138 | 94.60 104 | 96.83 62 | 89.92 110 | 89.69 129 | 95.02 31 | 97.89 36 | 98.24 44 | 91.27 73 | 92.38 147 | 92.18 134 | 94.99 138 | 91.12 134 |
|
v10 | | | 93.96 74 | 94.12 89 | 93.77 78 | 93.37 139 | 95.45 72 | 96.83 62 | 91.13 86 | 89.70 128 | 95.02 31 | 97.88 38 | 98.23 45 | 91.27 73 | 92.39 146 | 92.18 134 | 94.99 138 | 93.00 101 |
|
Fast-Effi-MVS+-dtu | | | 89.57 150 | 88.42 172 | 90.92 129 | 93.35 140 | 91.57 166 | 93.01 162 | 95.71 9 | 78.94 214 | 87.65 155 | 84.68 203 | 93.14 156 | 82.00 161 | 90.84 172 | 91.01 163 | 93.78 171 | 88.77 160 |
|
new-patchmatchnet | | | 84.45 193 | 88.75 168 | 79.43 213 | 93.28 141 | 81.87 213 | 81.68 231 | 83.48 202 | 94.47 31 | 71.53 223 | 98.33 20 | 97.88 67 | 58.61 226 | 90.35 175 | 77.33 218 | 87.99 197 | 81.05 199 |
|
IterMVS-LS | | | 92.10 131 | 92.33 130 | 91.82 121 | 93.18 142 | 93.66 130 | 92.80 168 | 92.27 56 | 90.82 112 | 90.59 129 | 97.19 62 | 90.97 166 | 87.76 126 | 89.60 182 | 90.94 164 | 94.34 164 | 93.16 97 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
thres400 | | | 88.54 163 | 88.15 174 | 88.98 157 | 93.17 143 | 92.84 146 | 93.56 147 | 86.93 173 | 86.45 166 | 82.37 186 | 79.96 217 | 81.46 198 | 81.83 165 | 93.21 133 | 94.76 91 | 96.04 107 | 88.39 167 |
|
v17 | | | 93.60 88 | 93.85 94 | 93.30 93 | 93.15 144 | 94.99 92 | 96.46 80 | 90.81 92 | 89.58 131 | 93.61 67 | 97.66 51 | 98.15 52 | 91.19 77 | 92.60 143 | 91.61 148 | 94.61 157 | 92.37 116 |
|
v1neww | | | 93.27 101 | 93.40 117 | 93.12 101 | 93.13 145 | 94.20 113 | 96.39 88 | 89.56 120 | 89.87 126 | 93.95 57 | 97.71 47 | 98.21 48 | 91.09 83 | 92.36 148 | 91.49 152 | 94.62 155 | 89.96 143 |
|
v7new | | | 93.27 101 | 93.40 117 | 93.12 101 | 93.13 145 | 94.20 113 | 96.39 88 | 89.56 120 | 89.87 126 | 93.95 57 | 97.71 47 | 98.21 48 | 91.09 83 | 92.36 148 | 91.49 152 | 94.62 155 | 89.96 143 |
|
v8 | | | 93.60 88 | 93.82 96 | 93.34 89 | 93.13 145 | 95.06 87 | 96.39 88 | 90.75 94 | 89.90 124 | 94.03 55 | 97.70 49 | 98.21 48 | 91.08 85 | 92.36 148 | 91.47 156 | 94.63 153 | 92.07 122 |
|
v6 | | | 93.27 101 | 93.41 115 | 93.12 101 | 93.13 145 | 94.20 113 | 96.39 88 | 89.55 122 | 89.89 125 | 93.93 59 | 97.72 45 | 98.22 47 | 91.10 82 | 92.36 148 | 91.49 152 | 94.63 153 | 89.95 145 |
|
CANet_DTU | | | 88.95 159 | 89.51 156 | 88.29 172 | 93.12 149 | 91.22 169 | 93.61 146 | 83.47 203 | 80.07 205 | 90.71 128 | 89.19 168 | 93.68 151 | 76.27 205 | 91.44 168 | 91.17 162 | 92.59 182 | 89.83 146 |
|
v16 | | | 93.53 93 | 93.80 99 | 93.20 98 | 93.10 150 | 94.98 93 | 96.43 82 | 90.81 92 | 89.39 134 | 93.12 81 | 97.63 53 | 98.01 60 | 91.19 77 | 92.60 143 | 91.65 147 | 94.58 159 | 92.36 117 |
|
gg-mvs-nofinetune | | | 88.32 165 | 88.81 165 | 87.75 178 | 93.07 151 | 89.37 181 | 89.06 209 | 95.94 7 | 95.29 21 | 87.15 157 | 97.38 59 | 76.38 211 | 68.05 218 | 91.04 171 | 89.10 181 | 93.24 177 | 83.10 193 |
|
3Dnovator | | 91.81 5 | 93.36 99 | 94.27 87 | 92.29 114 | 92.99 152 | 95.03 88 | 95.76 104 | 87.79 159 | 93.82 45 | 92.38 95 | 92.19 140 | 93.37 154 | 88.14 124 | 95.26 91 | 94.85 86 | 96.69 75 | 95.40 57 |
|
v18 | | | 93.33 100 | 93.59 106 | 93.04 106 | 92.94 153 | 94.87 95 | 96.31 95 | 90.59 98 | 88.96 136 | 92.89 84 | 97.51 55 | 97.90 63 | 91.01 89 | 92.33 152 | 91.48 155 | 94.50 160 | 92.05 123 |
|
USDC | | | 92.17 130 | 92.17 133 | 92.18 117 | 92.93 154 | 92.22 159 | 93.66 145 | 87.41 165 | 93.49 48 | 97.99 1 | 94.10 120 | 96.68 95 | 86.46 133 | 92.04 158 | 89.18 179 | 94.61 157 | 87.47 172 |
|
QAPM | | | 92.57 120 | 93.51 110 | 91.47 123 | 92.91 155 | 94.82 96 | 93.01 162 | 87.51 163 | 91.49 90 | 91.21 120 | 92.24 138 | 91.70 161 | 88.74 117 | 94.54 106 | 94.39 103 | 95.41 122 | 95.37 60 |
|
v148 | | | 92.38 125 | 92.78 126 | 91.91 119 | 92.86 156 | 92.13 161 | 94.84 122 | 87.03 171 | 91.47 92 | 93.07 82 | 96.92 69 | 98.89 14 | 90.10 106 | 92.05 157 | 89.69 173 | 93.56 172 | 88.27 169 |
|
DELS-MVS | | | 92.33 127 | 93.61 105 | 90.83 130 | 92.84 157 | 95.13 86 | 94.76 125 | 87.22 170 | 87.78 153 | 88.42 152 | 95.78 86 | 95.28 129 | 85.71 138 | 94.44 107 | 93.91 109 | 96.01 108 | 92.97 102 |
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 | | 89.22 12 | 91.09 138 | 91.42 142 | 90.71 131 | 92.79 158 | 93.61 135 | 92.74 169 | 85.47 188 | 86.10 169 | 90.73 124 | 85.71 198 | 93.07 157 | 86.69 132 | 94.07 121 | 93.34 119 | 95.86 112 | 94.02 82 |
|
pmmvs-eth3d | | | 92.34 126 | 92.33 130 | 92.34 113 | 92.67 159 | 90.67 174 | 96.37 92 | 89.06 134 | 90.98 108 | 93.60 68 | 97.13 66 | 97.02 83 | 88.29 122 | 90.20 176 | 91.42 157 | 94.07 167 | 88.89 159 |
|
MSDG | | | 92.09 132 | 92.84 125 | 91.22 126 | 92.55 160 | 92.97 144 | 93.42 149 | 85.43 189 | 90.24 117 | 91.83 107 | 94.70 109 | 94.59 140 | 88.48 120 | 94.91 100 | 93.31 120 | 95.59 120 | 89.15 155 |
|
FPMVS | | | 90.81 139 | 91.60 140 | 89.88 144 | 92.52 161 | 88.18 184 | 93.31 156 | 83.62 200 | 91.59 89 | 88.45 151 | 88.96 169 | 89.73 172 | 86.96 129 | 96.42 69 | 95.69 67 | 94.43 162 | 90.65 137 |
|
thres200 | | | 88.29 167 | 87.88 176 | 88.76 160 | 92.50 162 | 93.55 136 | 92.47 173 | 88.02 155 | 84.80 175 | 81.44 191 | 79.28 219 | 82.20 194 | 81.83 165 | 94.27 113 | 93.67 111 | 96.27 96 | 87.40 173 |
|
DI_MVS_plusplus_trai | | | 90.68 140 | 90.40 148 | 91.00 128 | 92.43 163 | 92.61 152 | 94.17 139 | 88.98 136 | 88.32 145 | 88.76 147 | 93.67 126 | 87.58 179 | 86.44 134 | 89.74 180 | 90.33 167 | 95.24 128 | 90.56 140 |
|
PVSNet_BlendedMVS | | | 90.09 145 | 90.12 150 | 90.05 141 | 92.40 164 | 92.74 149 | 91.74 181 | 85.89 183 | 80.54 200 | 90.30 134 | 88.54 172 | 95.51 119 | 84.69 142 | 92.64 141 | 90.25 169 | 95.28 126 | 90.61 138 |
|
PVSNet_Blended | | | 90.09 145 | 90.12 150 | 90.05 141 | 92.40 164 | 92.74 149 | 91.74 181 | 85.89 183 | 80.54 200 | 90.30 134 | 88.54 172 | 95.51 119 | 84.69 142 | 92.64 141 | 90.25 169 | 95.28 126 | 90.61 138 |
|
FMVSNet1 | | | 92.86 114 | 95.26 61 | 90.06 140 | 92.40 164 | 95.16 84 | 94.37 131 | 92.22 57 | 93.18 56 | 82.16 189 | 96.76 71 | 97.48 74 | 81.85 164 | 95.32 87 | 94.98 82 | 97.34 57 | 93.93 84 |
|
GA-MVS | | | 88.76 160 | 88.04 175 | 89.59 151 | 92.32 167 | 91.46 167 | 92.28 175 | 86.62 176 | 83.82 184 | 89.84 139 | 92.51 137 | 81.94 195 | 83.53 149 | 89.41 184 | 89.27 178 | 92.95 180 | 87.90 170 |
|
tfpn111 | | | 87.59 179 | 86.89 182 | 88.41 167 | 92.28 168 | 93.64 132 | 93.36 151 | 88.12 151 | 80.90 194 | 80.71 194 | 73.93 229 | 82.25 190 | 79.65 179 | 94.27 113 | 94.76 91 | 96.36 84 | 88.48 164 |
|
conf200view11 | | | 87.93 175 | 87.51 179 | 88.41 167 | 92.28 168 | 93.64 132 | 93.36 151 | 88.12 151 | 80.90 194 | 80.71 194 | 78.25 220 | 82.25 190 | 79.65 179 | 94.27 113 | 94.76 91 | 96.36 84 | 88.48 164 |
|
thres100view900 | | | 86.46 184 | 86.00 189 | 86.99 185 | 92.28 168 | 91.03 170 | 91.09 190 | 84.49 196 | 80.90 194 | 80.89 192 | 78.25 220 | 82.25 190 | 77.57 197 | 90.17 177 | 92.84 124 | 95.63 118 | 86.57 180 |
|
tfpn200view9 | | | 87.94 174 | 87.51 179 | 88.44 166 | 92.28 168 | 93.63 134 | 93.35 155 | 88.11 153 | 80.90 194 | 80.89 192 | 78.25 220 | 82.25 190 | 79.65 179 | 94.27 113 | 94.76 91 | 96.36 84 | 88.48 164 |
|
thresconf0.02 | | | 84.34 194 | 82.02 203 | 87.06 183 | 92.23 172 | 90.93 171 | 91.05 191 | 86.43 180 | 88.83 141 | 77.65 212 | 73.93 229 | 55.81 233 | 79.68 178 | 90.62 174 | 90.28 168 | 95.30 124 | 83.73 189 |
|
PatchMatch-RL | | | 89.59 149 | 88.80 166 | 90.51 134 | 92.20 173 | 88.00 188 | 91.72 183 | 86.64 175 | 84.75 178 | 88.25 153 | 87.10 188 | 90.66 168 | 89.85 110 | 93.23 132 | 92.28 130 | 94.41 163 | 85.60 185 |
|
MVS_Test | | | 90.19 143 | 90.58 145 | 89.74 147 | 92.12 174 | 91.74 165 | 92.51 171 | 88.54 147 | 82.80 187 | 87.50 156 | 94.62 110 | 95.02 135 | 83.97 146 | 88.69 189 | 89.32 177 | 93.79 170 | 91.85 127 |
|
conf0.01 | | | 85.72 187 | 83.49 198 | 88.32 170 | 92.11 175 | 93.35 141 | 93.36 151 | 88.02 155 | 80.90 194 | 80.51 197 | 74.83 227 | 59.86 232 | 79.65 179 | 93.80 123 | 94.76 91 | 96.29 93 | 86.94 177 |
|
PM-MVS | | | 92.65 119 | 93.20 122 | 92.00 118 | 92.11 175 | 90.16 178 | 95.99 99 | 84.81 194 | 91.31 100 | 92.41 94 | 95.87 83 | 96.64 96 | 92.35 51 | 93.65 127 | 92.91 123 | 94.34 164 | 91.85 127 |
|
conf0.002 | | | 84.82 190 | 81.84 204 | 88.30 171 | 92.05 177 | 93.28 143 | 93.36 151 | 88.00 158 | 80.90 194 | 80.48 198 | 73.43 231 | 52.48 237 | 79.65 179 | 93.72 124 | 92.82 125 | 96.28 94 | 86.22 181 |
|
IB-MVS | | 86.01 17 | 88.24 169 | 87.63 178 | 88.94 158 | 92.03 178 | 91.77 164 | 92.40 174 | 85.58 187 | 78.24 216 | 84.85 170 | 71.99 232 | 93.45 152 | 83.96 147 | 93.48 129 | 92.33 129 | 94.84 145 | 92.15 121 |
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 |
our_test_3 | | | | | | 91.78 179 | 88.87 183 | 94.37 131 | | | | | | | | | | |
|
N_pmnet | | | 79.33 212 | 84.22 194 | 73.62 228 | 91.72 180 | 73.72 232 | 86.11 224 | 76.36 219 | 92.38 69 | 53.38 236 | 95.54 92 | 95.62 117 | 59.14 225 | 84.23 213 | 74.84 226 | 75.03 232 | 73.25 225 |
|
MIMVSNet | | | 84.76 192 | 86.75 183 | 82.44 205 | 91.71 181 | 85.95 196 | 89.74 205 | 89.49 125 | 85.28 173 | 69.69 228 | 87.93 179 | 90.88 167 | 64.85 220 | 88.26 194 | 87.74 187 | 89.18 193 | 81.24 197 |
|
pmmvs4 | | | 89.95 147 | 89.32 159 | 90.69 132 | 91.60 182 | 89.17 182 | 94.37 131 | 87.63 162 | 88.07 150 | 91.02 122 | 94.50 113 | 90.50 169 | 86.13 136 | 86.33 204 | 89.40 176 | 93.39 175 | 87.29 174 |
|
V42 | | | 92.67 118 | 93.50 111 | 91.71 122 | 91.41 183 | 92.96 145 | 95.71 107 | 85.00 191 | 89.67 130 | 93.22 76 | 97.67 50 | 98.01 60 | 91.02 88 | 92.65 140 | 92.12 137 | 93.86 169 | 91.42 133 |
|
MDTV_nov1_ep13_2view | | | 88.22 170 | 87.85 177 | 88.65 162 | 91.40 184 | 86.75 194 | 94.07 140 | 84.97 192 | 88.86 140 | 93.20 77 | 96.11 81 | 96.21 109 | 83.70 148 | 87.29 201 | 80.29 211 | 84.56 210 | 79.46 208 |
|
tfpn_ndepth | | | 85.89 186 | 86.40 186 | 85.30 196 | 91.31 185 | 92.47 153 | 90.78 194 | 87.75 161 | 84.79 176 | 71.04 224 | 76.95 224 | 78.80 206 | 74.52 209 | 92.72 138 | 93.43 117 | 96.39 82 | 85.65 184 |
|
HyFIR lowres test | | | 88.19 172 | 86.56 185 | 90.09 139 | 91.24 186 | 92.17 160 | 94.30 136 | 88.79 144 | 84.06 180 | 85.45 168 | 89.52 165 | 85.64 186 | 88.64 118 | 85.40 212 | 87.28 188 | 92.14 184 | 81.87 196 |
|
GBi-Net | | | 89.35 152 | 90.58 145 | 87.91 176 | 91.22 187 | 94.05 119 | 92.88 165 | 90.05 104 | 79.40 206 | 78.60 206 | 90.58 153 | 87.05 180 | 78.54 187 | 95.32 87 | 94.98 82 | 96.17 103 | 92.67 108 |
|
test1 | | | 89.35 152 | 90.58 145 | 87.91 176 | 91.22 187 | 94.05 119 | 92.88 165 | 90.05 104 | 79.40 206 | 78.60 206 | 90.58 153 | 87.05 180 | 78.54 187 | 95.32 87 | 94.98 82 | 96.17 103 | 92.67 108 |
|
FMVSNet2 | | | 90.28 142 | 92.04 135 | 88.23 173 | 91.22 187 | 94.05 119 | 92.88 165 | 90.69 95 | 86.53 165 | 79.89 200 | 94.38 116 | 92.73 158 | 78.54 187 | 91.64 166 | 92.26 131 | 96.17 103 | 92.67 108 |
|
tpm | | | 81.58 205 | 78.84 213 | 84.79 200 | 91.11 190 | 79.50 217 | 89.79 204 | 83.75 198 | 79.30 210 | 92.05 101 | 90.98 148 | 64.78 227 | 74.54 207 | 80.50 223 | 76.67 220 | 77.49 227 | 80.15 204 |
|
UGNet | | | 92.31 129 | 94.70 73 | 89.53 152 | 90.99 191 | 95.53 71 | 96.19 97 | 92.10 65 | 91.35 99 | 85.76 164 | 95.31 96 | 95.48 121 | 76.84 201 | 95.22 92 | 94.79 89 | 95.32 123 | 95.19 62 |
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022 |
Vis-MVSNet | | | 94.39 66 | 95.85 50 | 92.68 109 | 90.91 192 | 95.88 60 | 97.62 46 | 91.41 81 | 91.95 80 | 89.20 142 | 97.29 61 | 96.26 104 | 90.60 102 | 96.95 54 | 95.91 60 | 96.32 92 | 96.71 34 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
IterMVS | | | 88.32 165 | 88.25 173 | 88.41 167 | 90.83 193 | 91.24 168 | 93.07 161 | 81.69 209 | 86.77 163 | 88.55 148 | 95.61 87 | 86.91 183 | 87.01 128 | 87.38 199 | 83.77 200 | 89.29 192 | 86.06 182 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MS-PatchMatch | | | 87.72 177 | 88.62 171 | 86.66 189 | 90.81 194 | 88.18 184 | 90.92 192 | 82.25 206 | 85.86 171 | 80.40 199 | 90.14 161 | 89.29 174 | 84.93 139 | 89.39 185 | 89.12 180 | 90.67 187 | 88.34 168 |
|
TAMVS | | | 82.96 198 | 86.15 188 | 79.24 216 | 90.57 195 | 83.12 210 | 87.29 215 | 75.12 223 | 84.06 180 | 65.81 231 | 92.22 139 | 88.27 178 | 69.11 215 | 88.72 187 | 87.26 190 | 87.56 202 | 79.38 209 |
|
CR-MVSNet | | | 85.32 189 | 81.58 205 | 89.69 149 | 90.36 196 | 84.79 201 | 86.72 220 | 92.22 57 | 75.38 224 | 90.73 124 | 90.41 157 | 67.88 222 | 84.86 140 | 83.76 214 | 85.74 195 | 93.24 177 | 83.14 191 |
|
CHOSEN 1792x2688 | | | 86.64 182 | 86.62 184 | 86.65 190 | 90.33 197 | 87.86 190 | 93.19 159 | 83.30 204 | 83.95 183 | 82.32 187 | 87.93 179 | 89.34 173 | 86.92 130 | 85.64 210 | 84.95 197 | 83.85 216 | 86.68 179 |
|
tpmp4_e23 | | | 82.16 201 | 78.26 218 | 86.70 188 | 89.92 198 | 84.82 200 | 91.17 189 | 89.95 109 | 81.21 192 | 87.10 158 | 81.91 213 | 64.01 228 | 77.88 193 | 79.89 225 | 74.99 225 | 84.18 214 | 81.00 200 |
|
PatchmatchNet | | | 82.44 199 | 78.69 215 | 86.83 186 | 89.81 199 | 81.55 214 | 90.78 194 | 87.27 169 | 82.39 189 | 88.85 144 | 88.31 175 | 70.96 218 | 81.90 162 | 78.58 227 | 74.33 227 | 82.35 222 | 74.69 219 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
RPMNet | | | 83.42 197 | 78.40 216 | 89.28 155 | 89.79 200 | 84.79 201 | 90.64 197 | 92.11 64 | 75.38 224 | 87.10 158 | 79.80 218 | 61.99 231 | 82.79 155 | 81.88 220 | 82.07 205 | 93.23 179 | 82.87 194 |
|
FMVSNet3 | | | 87.90 176 | 88.63 170 | 87.04 184 | 89.78 201 | 93.46 139 | 91.62 186 | 90.05 104 | 79.40 206 | 78.60 206 | 90.58 153 | 87.05 180 | 77.07 200 | 88.03 196 | 89.86 172 | 95.12 132 | 92.04 124 |
|
diffmvs | | | 88.28 168 | 88.88 163 | 87.58 179 | 89.51 202 | 88.07 187 | 91.88 177 | 85.83 186 | 87.31 156 | 86.34 162 | 96.01 82 | 88.90 176 | 81.90 162 | 85.49 211 | 86.61 192 | 90.04 189 | 89.77 147 |
|
tpm cat1 | | | 80.03 211 | 75.93 229 | 84.81 199 | 89.31 203 | 83.26 209 | 88.86 210 | 86.55 179 | 79.24 211 | 86.10 163 | 84.22 205 | 63.62 229 | 77.37 199 | 73.43 232 | 70.88 230 | 80.67 223 | 76.87 214 |
|
pmmvs5 | | | 88.63 162 | 89.70 154 | 87.39 182 | 89.24 204 | 90.64 175 | 91.87 178 | 82.13 207 | 83.34 185 | 87.86 154 | 94.58 111 | 96.15 110 | 79.87 176 | 87.33 200 | 89.07 182 | 93.39 175 | 86.76 178 |
|
MVSTER | | | 84.79 191 | 83.79 196 | 85.96 192 | 89.14 205 | 89.80 179 | 89.39 207 | 82.99 205 | 74.16 228 | 82.78 182 | 85.97 196 | 66.81 224 | 76.84 201 | 90.77 173 | 88.83 184 | 94.66 151 | 90.19 142 |
|
CostFormer | | | 82.15 202 | 79.54 211 | 85.20 197 | 88.92 206 | 85.70 197 | 90.87 193 | 86.26 182 | 79.19 212 | 83.87 176 | 87.89 181 | 69.20 220 | 76.62 203 | 77.50 230 | 75.28 223 | 84.69 209 | 82.02 195 |
|
MDTV_nov1_ep13 | | | 82.33 200 | 79.66 210 | 85.45 194 | 88.83 207 | 83.88 205 | 90.09 202 | 81.98 208 | 79.07 213 | 88.82 145 | 88.70 170 | 73.77 214 | 78.41 191 | 80.29 224 | 76.08 221 | 84.56 210 | 75.83 216 |
|
DWT-MVSNet_training | | | 79.22 215 | 73.99 231 | 85.33 195 | 88.57 208 | 84.41 203 | 90.56 198 | 80.96 213 | 73.90 229 | 85.72 166 | 75.62 225 | 50.09 238 | 81.30 170 | 76.91 231 | 77.02 219 | 84.88 208 | 79.97 206 |
|
tpmrst | | | 78.81 218 | 76.18 228 | 81.87 207 | 88.56 209 | 77.45 223 | 86.74 219 | 81.52 210 | 80.08 204 | 83.48 178 | 90.84 152 | 66.88 223 | 74.54 207 | 73.04 233 | 71.02 229 | 76.38 229 | 73.95 224 |
|
anonymousdsp | | | 95.45 47 | 96.70 26 | 93.99 66 | 88.43 210 | 92.05 162 | 99.18 1 | 85.42 190 | 94.29 37 | 96.10 16 | 98.63 13 | 99.08 11 | 96.11 1 | 97.77 38 | 97.41 29 | 98.70 8 | 97.69 7 |
|
EU-MVSNet | | | 91.63 135 | 92.73 127 | 90.35 136 | 88.36 211 | 87.89 189 | 96.53 76 | 81.51 211 | 92.45 67 | 91.82 108 | 96.44 76 | 97.05 82 | 93.26 35 | 94.10 120 | 88.94 183 | 90.61 188 | 92.24 120 |
|
E-PMN | | | 77.81 222 | 77.88 223 | 77.73 222 | 88.26 212 | 70.48 235 | 80.19 233 | 71.20 225 | 86.66 164 | 72.89 221 | 88.09 178 | 81.74 197 | 78.75 185 | 90.02 179 | 68.30 231 | 75.10 231 | 59.85 234 |
|
EMVS | | | 77.65 223 | 77.49 225 | 77.83 220 | 87.75 213 | 71.02 234 | 81.13 232 | 70.54 226 | 86.38 167 | 74.52 216 | 89.38 166 | 80.19 202 | 78.22 192 | 89.48 183 | 67.13 232 | 74.83 233 | 58.84 235 |
|
testmv | | | 81.49 208 | 84.76 192 | 77.67 223 | 87.67 214 | 80.25 216 | 90.12 200 | 77.62 216 | 80.34 203 | 69.71 226 | 90.92 151 | 96.47 99 | 56.57 228 | 88.58 192 | 84.92 199 | 84.33 213 | 71.86 229 |
|
test1235678 | | | 81.50 207 | 84.78 191 | 77.67 223 | 87.67 214 | 80.27 215 | 90.12 200 | 77.62 216 | 80.36 202 | 69.71 226 | 90.93 150 | 96.51 98 | 56.57 228 | 88.60 191 | 84.93 198 | 84.34 212 | 71.87 228 |
|
dps | | | 81.42 209 | 77.88 223 | 85.56 193 | 87.67 214 | 85.17 199 | 88.37 213 | 87.46 164 | 74.37 227 | 84.55 172 | 86.80 191 | 62.18 230 | 80.20 174 | 81.13 222 | 77.52 217 | 85.10 207 | 77.98 213 |
|
FMVSNet5 | | | 79.08 217 | 78.83 214 | 79.38 215 | 87.52 217 | 86.78 193 | 87.64 214 | 78.15 215 | 69.54 234 | 70.64 225 | 65.97 236 | 65.44 226 | 63.87 221 | 90.17 177 | 90.46 166 | 88.48 196 | 83.45 190 |
|
CVMVSNet | | | 88.97 158 | 89.73 153 | 88.10 174 | 87.33 218 | 85.22 198 | 94.68 127 | 78.68 214 | 88.94 138 | 86.98 160 | 95.55 90 | 85.71 185 | 89.87 109 | 91.19 170 | 89.69 173 | 91.05 186 | 91.78 130 |
|
EPMVS | | | 79.26 213 | 78.20 220 | 80.49 209 | 87.04 219 | 78.86 219 | 86.08 225 | 83.51 201 | 82.63 188 | 73.94 218 | 89.59 163 | 68.67 221 | 72.03 213 | 78.17 228 | 75.08 224 | 80.37 224 | 74.37 221 |
|
testpf | | | 72.68 230 | 66.81 233 | 79.53 212 | 86.52 220 | 73.89 231 | 83.56 228 | 88.74 145 | 58.70 237 | 79.68 201 | 71.31 233 | 53.64 235 | 62.23 222 | 68.68 234 | 66.64 233 | 76.46 228 | 74.82 218 |
|
testus | | | 78.20 221 | 81.50 206 | 74.36 227 | 85.59 221 | 79.36 218 | 86.99 218 | 65.76 227 | 76.01 222 | 73.00 219 | 77.98 223 | 93.35 155 | 51.30 234 | 86.33 204 | 82.79 203 | 83.50 218 | 74.68 220 |
|
no-one | | | 92.05 133 | 94.57 77 | 89.12 156 | 85.55 222 | 87.65 192 | 94.21 137 | 77.34 218 | 93.43 50 | 89.64 141 | 95.11 102 | 99.11 9 | 95.86 4 | 95.38 84 | 95.24 76 | 92.08 185 | 96.11 45 |
|
CMPMVS | | 66.55 18 | 85.55 188 | 87.46 181 | 83.32 204 | 84.99 223 | 81.97 212 | 79.19 234 | 75.93 220 | 79.32 209 | 88.82 145 | 85.09 199 | 91.07 164 | 82.12 160 | 92.56 145 | 89.63 175 | 88.84 195 | 92.56 112 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
test2356 | | | 72.95 229 | 71.24 232 | 74.95 226 | 84.89 224 | 75.49 227 | 82.67 230 | 75.38 221 | 68.02 235 | 68.65 230 | 74.40 228 | 52.81 236 | 55.61 231 | 81.50 221 | 79.80 212 | 82.50 220 | 66.70 232 |
|
test-mter | | | 78.71 219 | 78.35 217 | 79.12 218 | 84.03 225 | 76.58 224 | 88.51 212 | 59.06 232 | 71.06 230 | 78.87 205 | 83.73 208 | 71.83 215 | 76.44 204 | 83.41 217 | 80.61 209 | 87.79 200 | 81.24 197 |
|
new_pmnet | | | 76.65 226 | 83.52 197 | 68.63 230 | 82.60 226 | 72.08 233 | 76.76 236 | 64.17 228 | 84.41 179 | 49.73 238 | 91.77 142 | 91.53 162 | 56.16 230 | 86.59 202 | 83.26 202 | 82.37 221 | 75.02 217 |
|
TESTMET0.1,1 | | | 77.47 224 | 77.20 226 | 77.78 221 | 81.94 227 | 75.11 228 | 87.04 216 | 58.33 234 | 70.11 232 | 78.59 209 | 83.17 209 | 71.60 216 | 73.88 210 | 82.32 218 | 79.20 214 | 86.91 203 | 78.87 211 |
|
ADS-MVSNet | | | 79.11 216 | 79.38 212 | 78.80 219 | 81.90 228 | 75.59 226 | 84.36 227 | 83.69 199 | 87.31 156 | 76.76 213 | 87.58 183 | 76.90 210 | 68.55 217 | 78.70 226 | 75.56 222 | 77.53 226 | 74.07 223 |
|
MVS-HIRNet | | | 78.28 220 | 75.28 230 | 81.79 208 | 80.33 229 | 69.38 236 | 76.83 235 | 86.59 177 | 70.76 231 | 86.66 161 | 89.57 164 | 81.04 199 | 77.74 195 | 77.81 229 | 71.65 228 | 82.62 219 | 66.73 231 |
|
CHOSEN 280x420 | | | 79.24 214 | 78.26 218 | 80.38 210 | 79.60 230 | 68.80 237 | 89.32 208 | 75.38 221 | 77.25 220 | 78.02 211 | 75.57 226 | 76.17 212 | 81.19 171 | 88.61 190 | 81.39 207 | 78.79 225 | 80.03 205 |
|
LP | | | 84.09 195 | 84.31 193 | 83.85 203 | 79.40 231 | 84.34 204 | 90.26 199 | 84.02 197 | 87.99 151 | 84.66 171 | 91.61 145 | 79.13 204 | 80.58 173 | 85.90 209 | 81.59 206 | 84.16 215 | 79.59 207 |
|
pmmvs3 | | | 81.69 204 | 83.83 195 | 79.19 217 | 78.33 232 | 78.57 220 | 89.53 206 | 58.71 233 | 78.88 215 | 84.34 174 | 88.36 174 | 91.96 160 | 77.69 196 | 87.48 198 | 82.42 204 | 86.54 205 | 79.18 210 |
|
PatchT | | | 83.44 196 | 81.10 207 | 86.18 191 | 77.92 233 | 82.58 211 | 89.87 203 | 87.39 166 | 75.88 223 | 90.73 124 | 89.86 162 | 66.71 225 | 84.86 140 | 83.76 214 | 85.74 195 | 86.33 206 | 83.14 191 |
|
test12356 | | | 75.40 227 | 80.89 208 | 69.01 229 | 77.43 234 | 75.75 225 | 83.03 229 | 61.48 230 | 78.13 218 | 59.08 235 | 87.69 182 | 94.95 137 | 57.37 227 | 88.18 195 | 80.59 210 | 75.65 230 | 60.93 233 |
|
PMMVS2 | | | 69.86 231 | 82.14 202 | 55.52 233 | 75.19 235 | 63.08 238 | 75.52 237 | 60.97 231 | 88.50 144 | 25.11 241 | 91.77 142 | 96.44 100 | 25.43 235 | 88.70 188 | 79.34 213 | 70.93 234 | 67.17 230 |
|
MDA-MVSNet-bldmvs | | | 89.75 148 | 91.67 138 | 87.50 180 | 74.25 236 | 90.88 172 | 94.68 127 | 85.89 183 | 91.64 87 | 91.03 121 | 95.86 84 | 94.35 143 | 89.10 114 | 96.87 58 | 86.37 193 | 90.04 189 | 85.72 183 |
|
PMMVS | | | 81.93 203 | 83.48 199 | 80.12 211 | 72.35 237 | 75.05 230 | 88.54 211 | 64.01 229 | 77.02 221 | 82.22 188 | 87.51 184 | 91.12 163 | 79.70 177 | 86.59 202 | 86.64 191 | 93.88 168 | 80.41 201 |
|
MVE | | 60.41 19 | 73.21 228 | 80.84 209 | 64.30 231 | 56.34 238 | 57.24 239 | 75.28 238 | 72.76 224 | 87.14 162 | 41.39 239 | 86.31 194 | 85.30 187 | 80.66 172 | 86.17 206 | 83.36 201 | 59.35 236 | 80.38 202 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | | | 28.44 234 | 36.05 239 | 15.86 241 | 21.29 241 | 6.40 236 | 54.52 238 | 51.96 237 | 50.37 237 | 38.68 242 | 9.55 236 | 61.75 236 | 59.66 234 | 45.36 238 | |
|
testmvs | | | 2.38 234 | 3.35 235 | 1.26 237 | 0.83 240 | 0.96 243 | 1.53 243 | 0.83 237 | 3.59 239 | 1.63 244 | 6.03 238 | 2.93 243 | 1.55 238 | 3.49 237 | 2.51 236 | 1.21 240 | 3.92 237 |
|
test123 | | | 2.16 235 | 2.82 236 | 1.41 236 | 0.62 241 | 1.18 242 | 1.53 243 | 0.82 238 | 2.78 240 | 2.27 243 | 4.18 239 | 1.98 244 | 1.64 237 | 2.58 238 | 3.01 235 | 1.56 239 | 4.00 236 |
|
GG-mvs-BLEND | | | 54.28 233 | 77.89 222 | 26.72 235 | 0.37 242 | 83.31 208 | 70.04 239 | 0.39 239 | 74.71 226 | 5.36 242 | 68.78 234 | 83.06 189 | 0.62 239 | 83.73 216 | 78.99 216 | 83.55 217 | 72.68 227 |
|
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 | | | | | | | | | | | 94.88 39 | | 96.88 88 | | | | | |
|
MTMP | | | | | | | | | | | 95.43 21 | | 97.25 78 | | | | | |
|
Patchmatch-RL test | | | | | | | | 8.96 242 | | | | | | | | | | |
|
NP-MVS | | | | | | | | | | 85.48 172 | | | | | | | | |
|
Patchmtry | | | | | | | 83.74 206 | 86.72 220 | 92.22 57 | | 90.73 124 | | | | | | | |
|
DeepMVS_CX | | | | | | | 47.68 240 | 53.20 240 | 19.21 235 | 63.24 236 | 26.96 240 | 66.50 235 | 69.82 219 | 66.91 219 | 64.27 235 | | 54.91 237 | 72.72 226 |
|