LTVRE_ROB | | 95.06 1 | 97.73 1 | 98.39 2 | 96.95 1 | 96.33 47 | 96.94 31 | 98.30 22 | 94.90 15 | 98.61 2 | 97.73 3 | 97.97 32 | 98.57 28 | 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 |
TDRefinement | | | 97.59 2 | 98.32 3 | 96.73 4 | 95.90 60 | 98.10 2 | 99.08 2 | 93.92 32 | 98.24 4 | 96.44 15 | 98.12 27 | 97.86 68 | 96.06 2 | 99.24 1 | 98.93 1 | 99.00 2 | 97.77 5 |
|
WR-MVS | | | 97.53 3 | 98.20 4 | 96.76 3 | 96.93 26 | 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 26 |
|
SixPastTwentyTwo | | | 97.36 4 | 97.73 10 | 96.92 2 | 97.36 12 | 96.15 50 | 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 23 | 97.22 12 |
|
PS-CasMVS | | | 97.22 5 | 97.84 7 | 96.50 5 | 97.08 22 | 97.92 6 | 98.17 28 | 97.02 2 | 94.71 27 | 95.32 24 | 98.52 15 | 98.97 12 | 92.91 42 | 99.04 4 | 98.47 6 | 98.49 19 | 97.24 11 |
|
PEN-MVS | | | 97.16 6 | 97.87 6 | 96.33 12 | 97.20 19 | 97.97 4 | 98.25 25 | 96.86 5 | 95.09 25 | 94.93 36 | 98.66 11 | 99.16 8 | 92.27 52 | 98.98 6 | 98.39 8 | 98.49 19 | 96.83 30 |
|
DTE-MVSNet | | | 97.16 6 | 97.75 9 | 96.47 6 | 97.40 11 | 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 23 | 96.28 40 |
|
COLMAP_ROB | | 93.74 2 | 97.09 8 | 97.98 5 | 96.05 18 | 95.97 57 | 97.78 9 | 98.56 12 | 91.72 72 | 97.53 7 | 96.01 17 | 98.14 26 | 98.76 19 | 95.28 8 | 98.76 11 | 98.23 11 | 98.77 5 | 96.67 35 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
WR-MVS_H | | | 97.06 9 | 97.78 8 | 96.23 14 | 96.74 35 | 98.04 3 | 98.25 25 | 97.32 1 | 94.40 34 | 93.71 64 | 98.55 14 | 98.89 14 | 92.97 39 | 98.91 9 | 98.45 7 | 98.38 28 | 97.19 13 |
|
CP-MVSNet | | | 96.97 10 | 97.42 13 | 96.44 7 | 97.06 23 | 97.82 8 | 98.12 30 | 96.98 3 | 93.50 45 | 95.21 26 | 97.98 31 | 98.44 32 | 92.83 45 | 98.93 8 | 98.37 9 | 98.46 22 | 96.91 27 |
|
ACMH | | 90.17 8 | 96.61 11 | 97.69 11 | 95.35 30 | 95.29 74 | 96.94 31 | 98.43 16 | 92.05 64 | 98.04 5 | 95.38 22 | 98.07 29 | 99.25 7 | 93.23 36 | 98.35 16 | 97.16 35 | 97.72 45 | 96.00 45 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
Anonymous20231211 | | | 96.59 12 | 98.43 1 | 94.44 50 | 95.89 62 | 96.12 51 | 95.23 115 | 95.91 8 | 99.42 1 | 92.75 84 | 98.87 5 | 99.94 1 | 88.19 121 | 98.64 13 | 98.50 5 | 98.66 10 | 97.49 8 |
|
UA-Net | | | 96.56 13 | 96.73 23 | 96.36 10 | 98.99 1 | 97.90 7 | 97.79 41 | 95.64 10 | 92.78 60 | 92.54 89 | 96.23 78 | 95.02 134 | 94.31 21 | 98.43 15 | 98.12 12 | 98.89 3 | 98.58 2 |
|
ACMMPR | | | 96.54 14 | 96.71 24 | 96.35 11 | 97.55 9 | 97.63 11 | 98.62 10 | 94.54 18 | 94.45 31 | 94.19 50 | 95.04 103 | 97.35 75 | 94.92 13 | 97.85 32 | 97.50 23 | 98.26 29 | 97.17 14 |
|
v7n | | | 96.49 15 | 97.20 18 | 95.65 23 | 95.57 70 | 96.04 53 | 97.93 35 | 92.49 50 | 96.40 12 | 97.13 7 | 98.99 4 | 99.41 4 | 93.79 28 | 97.84 34 | 96.15 53 | 97.00 68 | 95.60 53 |
|
DeepC-MVS | | 92.47 4 | 96.44 16 | 96.75 22 | 96.08 17 | 97.57 7 | 97.19 27 | 97.96 34 | 94.28 24 | 95.29 21 | 94.92 37 | 98.31 22 | 96.92 85 | 93.69 29 | 96.81 58 | 96.50 45 | 98.06 38 | 96.27 41 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
v52 | | | 96.35 17 | 97.40 14 | 95.12 39 | 93.83 118 | 95.54 67 | 97.82 39 | 88.95 138 | 96.27 14 | 97.22 5 | 99.11 2 | 99.40 5 | 95.80 5 | 98.16 19 | 96.37 48 | 97.10 63 | 96.96 22 |
|
V4 | | | 96.35 17 | 97.40 14 | 95.12 39 | 93.83 118 | 95.54 67 | 97.82 39 | 88.95 138 | 96.27 14 | 97.21 6 | 99.10 3 | 99.40 5 | 95.79 6 | 98.17 18 | 96.37 48 | 97.10 63 | 96.96 22 |
|
ACMM | | 90.06 9 | 96.31 19 | 96.42 30 | 96.19 15 | 97.21 18 | 97.16 29 | 98.71 5 | 93.79 36 | 94.35 35 | 93.81 60 | 92.80 132 | 98.23 44 | 95.11 9 | 98.07 23 | 97.45 25 | 98.51 18 | 96.86 29 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMH+ | | 89.90 10 | 96.27 20 | 97.52 12 | 94.81 46 | 95.19 76 | 97.18 28 | 97.97 33 | 92.52 48 | 96.72 9 | 90.50 130 | 97.31 59 | 99.11 9 | 94.10 23 | 98.67 12 | 97.90 15 | 98.56 16 | 95.79 49 |
|
APDe-MVS | | | 96.23 21 | 97.22 17 | 95.08 41 | 96.66 39 | 97.56 14 | 98.63 9 | 93.69 38 | 94.62 28 | 89.80 139 | 97.73 43 | 98.13 52 | 93.84 27 | 97.79 36 | 97.63 19 | 97.87 43 | 97.08 18 |
|
CP-MVS | | | 96.21 22 | 96.16 41 | 96.27 13 | 97.56 8 | 97.13 30 | 98.43 16 | 94.70 17 | 92.62 62 | 94.13 52 | 92.71 133 | 98.03 58 | 94.54 19 | 98.00 27 | 97.60 20 | 98.23 30 | 97.05 19 |
|
MPTG | | | 96.18 23 | 96.01 43 | 96.38 8 | 98.30 2 | 96.18 49 | 98.51 14 | 94.48 22 | 94.56 29 | 94.81 42 | 91.73 142 | 96.96 83 | 94.30 22 | 98.09 21 | 97.83 16 | 97.91 42 | 96.73 32 |
|
HFP-MVS | | | 96.18 23 | 96.53 28 | 95.77 21 | 97.34 15 | 97.26 24 | 98.16 29 | 94.54 18 | 94.45 31 | 92.52 90 | 95.05 101 | 96.95 84 | 93.89 26 | 97.28 43 | 97.46 24 | 98.19 31 | 97.25 9 |
|
MP-MVS | | | 96.13 25 | 95.93 46 | 96.37 9 | 98.19 4 | 97.31 22 | 98.49 15 | 94.53 21 | 91.39 96 | 94.38 47 | 94.32 115 | 96.43 100 | 94.59 17 | 97.75 38 | 97.44 26 | 98.04 39 | 96.88 28 |
|
ACMMP | | | 96.12 26 | 96.27 37 | 95.93 19 | 97.20 19 | 97.60 12 | 98.64 8 | 93.74 37 | 92.47 64 | 93.13 79 | 93.23 127 | 98.06 55 | 94.51 20 | 97.99 28 | 97.57 22 | 98.39 27 | 96.99 20 |
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 |
LGP-MVS_train | | | 96.10 27 | 96.29 35 | 95.87 20 | 96.72 36 | 97.35 21 | 98.43 16 | 93.83 35 | 90.81 111 | 92.67 88 | 95.05 101 | 98.86 16 | 95.01 10 | 98.11 20 | 97.37 31 | 98.52 17 | 96.50 37 |
|
CSCG | | | 96.07 28 | 97.15 19 | 94.81 46 | 96.06 55 | 97.58 13 | 96.52 76 | 90.98 88 | 96.51 10 | 93.60 67 | 97.13 64 | 98.55 30 | 93.01 38 | 97.17 46 | 95.36 71 | 98.68 9 | 97.78 4 |
|
v748 | | | 96.05 29 | 97.00 20 | 94.95 44 | 94.41 92 | 94.77 96 | 96.72 65 | 91.03 87 | 96.12 16 | 96.71 11 | 98.74 8 | 99.59 2 | 93.55 31 | 97.97 29 | 95.96 57 | 97.28 56 | 95.84 48 |
|
TSAR-MVS + MP. | | | 95.99 30 | 96.57 27 | 95.31 32 | 96.87 27 | 96.50 42 | 98.71 5 | 91.58 75 | 93.25 51 | 92.71 85 | 96.86 68 | 96.57 96 | 93.92 24 | 98.09 21 | 97.91 14 | 98.08 36 | 96.81 31 |
|
OPM-MVS | | | 95.96 31 | 96.59 26 | 95.23 35 | 96.67 38 | 96.52 41 | 97.86 37 | 93.28 42 | 95.27 23 | 93.46 69 | 96.26 75 | 98.85 17 | 92.89 43 | 97.09 47 | 96.37 48 | 97.22 60 | 95.78 50 |
|
SteuartSystems-ACMMP | | | 95.96 31 | 96.13 42 | 95.76 22 | 97.06 23 | 97.36 20 | 98.40 20 | 94.24 26 | 91.49 88 | 91.91 104 | 94.50 111 | 96.89 86 | 94.99 11 | 98.01 26 | 97.44 26 | 97.97 41 | 97.25 9 |
Skip Steuart: Steuart Systems R&D Blog. |
ACMP | | 89.62 11 | 95.96 31 | 96.28 36 | 95.59 24 | 96.58 41 | 97.23 26 | 98.26 24 | 93.22 43 | 92.33 69 | 92.31 96 | 94.29 116 | 98.73 21 | 94.68 15 | 98.04 24 | 97.14 36 | 98.47 21 | 96.17 43 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
PGM-MVS | | | 95.90 34 | 95.72 49 | 96.10 16 | 97.53 10 | 97.45 19 | 98.55 13 | 94.12 29 | 90.25 114 | 93.71 64 | 93.20 128 | 97.18 79 | 94.63 16 | 97.68 39 | 97.34 32 | 98.08 36 | 96.97 21 |
|
PMVS | | 87.16 16 | 95.88 35 | 96.47 29 | 95.19 37 | 97.00 25 | 96.02 54 | 96.70 66 | 91.57 76 | 94.43 33 | 95.33 23 | 97.16 63 | 95.37 123 | 92.39 49 | 98.89 10 | 98.72 3 | 98.17 33 | 94.71 68 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ACMMP_Plus | | | 95.86 36 | 96.18 38 | 95.47 29 | 97.11 21 | 97.26 24 | 98.37 21 | 93.48 41 | 93.49 46 | 93.99 55 | 95.61 85 | 94.11 143 | 92.49 47 | 97.87 31 | 97.44 26 | 97.40 52 | 97.52 7 |
|
Gipuma | | | 95.86 36 | 96.17 39 | 95.50 28 | 95.92 59 | 94.59 103 | 94.77 122 | 92.50 49 | 97.82 6 | 97.90 2 | 95.56 87 | 97.88 66 | 94.71 14 | 98.02 25 | 94.81 85 | 97.23 59 | 94.48 74 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
LS3D | | | 95.83 38 | 96.35 32 | 95.22 36 | 96.47 44 | 97.49 15 | 97.99 31 | 92.35 53 | 94.92 26 | 94.58 43 | 94.88 106 | 95.11 132 | 91.52 66 | 98.48 14 | 98.05 13 | 98.42 25 | 95.49 54 |
|
SD-MVS | | | 95.77 39 | 96.17 39 | 95.30 33 | 96.72 36 | 96.19 48 | 97.01 51 | 93.04 44 | 94.03 40 | 92.71 85 | 96.45 73 | 96.78 93 | 93.91 25 | 96.79 59 | 95.89 60 | 98.42 25 | 97.09 17 |
|
TranMVSNet+NR-MVSNet | | | 95.72 40 | 96.42 30 | 94.91 45 | 96.21 50 | 96.77 35 | 96.90 58 | 94.99 13 | 92.62 62 | 91.92 103 | 98.51 16 | 98.63 25 | 90.82 92 | 97.27 44 | 96.83 39 | 98.63 13 | 94.31 75 |
|
ESAPD | | | 95.63 41 | 96.35 32 | 94.80 48 | 96.76 34 | 97.29 23 | 97.74 42 | 94.15 28 | 91.69 83 | 90.01 136 | 96.65 70 | 97.29 76 | 92.45 48 | 97.41 41 | 97.18 33 | 97.67 49 | 96.95 24 |
|
DU-MVS | | | 95.51 42 | 95.68 50 | 95.33 31 | 96.45 45 | 96.44 44 | 96.61 73 | 95.32 11 | 89.97 120 | 93.78 61 | 97.46 56 | 98.07 54 | 91.19 76 | 97.03 48 | 96.53 43 | 98.61 14 | 94.22 76 |
|
UniMVSNet (Re) | | | 95.46 43 | 95.86 47 | 95.00 43 | 96.09 52 | 96.60 36 | 96.68 70 | 94.99 13 | 90.36 113 | 92.13 99 | 97.64 51 | 98.13 52 | 91.38 69 | 96.90 53 | 96.74 40 | 98.73 6 | 94.63 71 |
|
RPSCF | | | 95.46 43 | 96.95 21 | 93.73 79 | 95.72 67 | 95.94 57 | 95.58 110 | 88.08 152 | 95.31 19 | 91.34 114 | 96.26 75 | 98.04 57 | 93.63 30 | 98.28 17 | 97.67 18 | 98.01 40 | 97.13 15 |
|
anonymousdsp | | | 95.45 45 | 96.70 25 | 93.99 65 | 88.43 207 | 92.05 160 | 99.18 1 | 85.42 188 | 94.29 36 | 96.10 16 | 98.63 13 | 99.08 11 | 96.11 1 | 97.77 37 | 97.41 29 | 98.70 8 | 97.69 6 |
|
APD-MVS | | | 95.38 46 | 95.68 50 | 95.03 42 | 97.30 16 | 96.90 33 | 97.83 38 | 93.92 32 | 89.40 130 | 90.35 131 | 95.41 91 | 97.69 70 | 92.97 39 | 97.24 45 | 97.17 34 | 97.83 44 | 95.96 46 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
UniMVSNet_NR-MVSNet | | | 95.34 47 | 95.51 54 | 95.14 38 | 95.80 65 | 96.55 37 | 96.61 73 | 94.79 16 | 90.04 119 | 93.78 61 | 97.51 54 | 97.25 77 | 91.19 76 | 96.68 61 | 96.31 51 | 98.65 12 | 94.22 76 |
|
X-MVS | | | 95.33 48 | 95.13 61 | 95.57 26 | 97.35 13 | 97.48 16 | 98.43 16 | 94.28 24 | 92.30 70 | 93.28 72 | 86.89 188 | 96.82 89 | 91.87 57 | 97.85 32 | 97.59 21 | 98.19 31 | 96.95 24 |
|
3Dnovator+ | | 92.82 3 | 95.22 49 | 95.16 60 | 95.29 34 | 96.17 51 | 96.55 37 | 97.64 44 | 94.02 31 | 94.16 39 | 94.29 49 | 92.09 139 | 93.71 148 | 91.90 55 | 96.68 61 | 96.51 44 | 97.70 47 | 96.40 38 |
|
HPM-MVS++ | | | 95.21 50 | 94.89 65 | 95.59 24 | 97.79 6 | 95.39 75 | 97.68 43 | 94.05 30 | 91.91 80 | 94.35 48 | 93.38 126 | 95.07 133 | 92.94 41 | 96.01 72 | 95.88 61 | 96.73 71 | 96.61 36 |
|
TSAR-MVS + ACMM | | | 95.17 51 | 95.95 44 | 94.26 55 | 96.07 54 | 96.46 43 | 95.67 107 | 94.21 27 | 93.84 42 | 90.99 122 | 97.18 62 | 95.24 131 | 93.55 31 | 96.60 64 | 95.61 67 | 95.06 133 | 96.69 34 |
|
HSP-MVS | | | 95.04 52 | 95.45 56 | 94.57 49 | 96.87 27 | 97.77 10 | 98.71 5 | 93.88 34 | 91.21 101 | 91.48 112 | 95.36 92 | 98.37 38 | 90.73 93 | 94.37 106 | 92.98 120 | 95.77 115 | 98.08 3 |
|
CPTT-MVS | | | 95.00 53 | 94.52 76 | 95.57 26 | 96.84 31 | 96.78 34 | 97.88 36 | 93.67 39 | 92.20 72 | 92.35 95 | 85.87 195 | 97.56 72 | 94.98 12 | 96.96 51 | 96.07 56 | 97.70 47 | 96.18 42 |
|
Baseline_NR-MVSNet | | | 94.85 54 | 95.35 58 | 94.26 55 | 96.45 45 | 93.86 125 | 96.70 66 | 94.54 18 | 90.07 118 | 90.17 135 | 98.77 7 | 97.89 63 | 90.64 97 | 97.03 48 | 96.16 52 | 97.04 67 | 93.67 84 |
|
EG-PatchMatch MVS | | | 94.81 55 | 95.53 53 | 93.97 66 | 95.89 62 | 94.62 100 | 95.55 111 | 88.18 148 | 92.77 61 | 94.88 39 | 97.04 66 | 98.61 26 | 93.31 33 | 96.89 54 | 95.19 76 | 95.99 107 | 93.56 88 |
|
OMC-MVS | | | 94.74 56 | 95.46 55 | 93.91 71 | 94.62 87 | 96.26 47 | 96.64 72 | 89.36 128 | 94.20 37 | 94.15 51 | 94.02 121 | 97.73 69 | 91.34 71 | 96.15 70 | 95.04 79 | 97.37 53 | 94.80 65 |
|
DeepC-MVS_fast | | 91.38 6 | 94.73 57 | 94.98 62 | 94.44 50 | 96.83 33 | 96.12 51 | 96.69 68 | 92.17 59 | 92.98 56 | 93.72 63 | 94.14 117 | 95.45 121 | 90.49 102 | 95.73 78 | 95.30 72 | 96.71 72 | 95.13 62 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PHI-MVS | | | 94.65 58 | 94.84 67 | 94.44 50 | 94.95 81 | 96.55 37 | 96.46 79 | 91.10 85 | 88.96 134 | 96.00 18 | 94.55 110 | 95.32 126 | 90.67 95 | 96.97 50 | 96.69 42 | 97.44 51 | 94.84 64 |
|
pmmvs6 | | | 94.58 59 | 97.30 16 | 91.40 123 | 94.84 83 | 94.61 101 | 93.40 147 | 92.43 52 | 98.51 3 | 85.61 165 | 98.73 10 | 99.53 3 | 84.40 142 | 97.88 30 | 97.03 37 | 97.72 45 | 94.79 66 |
|
DeepPCF-MVS | | 90.68 7 | 94.56 60 | 94.92 64 | 94.15 57 | 94.11 102 | 95.71 63 | 97.03 50 | 90.65 94 | 93.39 50 | 94.08 53 | 95.29 95 | 94.15 142 | 93.21 37 | 95.22 90 | 94.92 83 | 95.82 114 | 95.75 51 |
|
NR-MVSNet | | | 94.55 61 | 95.66 52 | 93.25 95 | 94.26 97 | 96.44 44 | 96.69 68 | 95.32 11 | 89.97 120 | 91.79 108 | 97.46 56 | 98.39 37 | 82.85 151 | 96.87 56 | 96.48 46 | 98.57 15 | 93.98 81 |
|
v13 | | | 94.54 62 | 94.93 63 | 94.09 58 | 93.81 120 | 95.44 71 | 96.99 54 | 91.67 73 | 92.43 66 | 95.20 27 | 98.33 19 | 98.73 21 | 91.87 57 | 93.67 124 | 92.26 129 | 95.00 135 | 93.63 86 |
|
v12 | | | 94.44 63 | 94.79 69 | 94.02 62 | 93.75 123 | 95.37 76 | 96.92 55 | 91.61 74 | 92.21 71 | 95.10 29 | 98.27 23 | 98.69 23 | 91.73 61 | 93.49 126 | 92.15 134 | 94.97 139 | 93.37 91 |
|
Vis-MVSNet | | | 94.39 64 | 95.85 48 | 92.68 107 | 90.91 189 | 95.88 58 | 97.62 46 | 91.41 79 | 91.95 78 | 89.20 141 | 97.29 60 | 96.26 103 | 90.60 101 | 96.95 52 | 95.91 58 | 96.32 90 | 96.71 33 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
V9 | | | 94.33 65 | 94.66 72 | 93.94 69 | 93.69 127 | 95.31 77 | 96.84 60 | 91.53 77 | 92.04 77 | 95.00 33 | 98.22 24 | 98.64 24 | 91.62 63 | 93.29 128 | 92.05 136 | 94.93 140 | 93.10 96 |
|
v11 | | | 94.32 66 | 94.62 73 | 93.97 66 | 93.95 110 | 95.31 77 | 96.83 61 | 91.30 81 | 91.95 78 | 95.51 20 | 98.32 21 | 98.61 26 | 91.44 68 | 92.83 135 | 92.23 131 | 94.77 144 | 93.08 97 |
|
TSAR-MVS + GP. | | | 94.25 67 | 94.81 68 | 93.60 80 | 96.52 43 | 95.80 61 | 94.37 129 | 92.47 51 | 90.89 108 | 88.92 142 | 95.34 93 | 94.38 140 | 92.85 44 | 96.36 68 | 95.62 66 | 96.47 77 | 95.28 59 |
|
CNVR-MVS | | | 94.24 68 | 94.47 78 | 93.96 68 | 96.56 42 | 95.67 64 | 96.43 81 | 91.95 66 | 92.08 75 | 91.28 116 | 90.51 154 | 95.35 124 | 91.20 75 | 96.34 69 | 95.50 69 | 96.34 87 | 95.88 47 |
|
V14 | | | 94.21 69 | 94.52 76 | 93.85 72 | 93.62 128 | 95.25 80 | 96.76 64 | 91.42 78 | 91.83 81 | 94.91 38 | 98.15 25 | 98.57 28 | 91.49 67 | 93.06 133 | 91.93 140 | 94.90 141 | 92.82 101 |
|
v15 | | | 94.09 70 | 94.37 80 | 93.77 77 | 93.56 130 | 95.18 81 | 96.68 70 | 91.34 80 | 91.64 85 | 94.83 41 | 98.09 28 | 98.51 31 | 91.37 70 | 92.84 134 | 91.80 142 | 94.85 142 | 92.53 112 |
|
v1192 | | | 93.98 71 | 93.94 89 | 94.01 63 | 93.91 114 | 94.63 99 | 97.00 52 | 89.75 112 | 91.01 105 | 96.50 12 | 97.93 33 | 98.26 42 | 91.74 60 | 92.06 154 | 92.05 136 | 95.18 128 | 91.66 130 |
|
v10 | | | 93.96 72 | 94.12 87 | 93.77 77 | 93.37 137 | 95.45 70 | 96.83 61 | 91.13 84 | 89.70 126 | 95.02 31 | 97.88 37 | 98.23 44 | 91.27 72 | 92.39 144 | 92.18 132 | 94.99 136 | 93.00 99 |
|
CDPH-MVS | | | 93.96 72 | 93.86 91 | 94.08 60 | 96.31 48 | 95.84 59 | 96.92 55 | 91.85 69 | 87.21 159 | 91.25 118 | 92.83 130 | 96.06 111 | 91.05 85 | 95.57 79 | 94.81 85 | 97.12 61 | 94.72 67 |
|
MVS_0304 | | | 93.92 74 | 93.81 96 | 94.05 61 | 96.06 55 | 96.00 55 | 96.43 81 | 92.76 46 | 85.99 168 | 94.43 46 | 94.04 120 | 97.08 80 | 88.12 123 | 94.65 103 | 94.20 103 | 96.47 77 | 94.71 68 |
|
MSLP-MVS++ | | | 93.91 75 | 94.30 84 | 93.45 83 | 95.51 71 | 95.83 60 | 93.12 157 | 91.93 68 | 91.45 93 | 91.40 113 | 87.42 183 | 96.12 110 | 93.27 34 | 96.57 65 | 96.40 47 | 95.49 119 | 96.29 39 |
|
v1921920 | | | 93.90 76 | 93.82 94 | 94.00 64 | 93.74 124 | 94.31 107 | 97.12 47 | 89.33 129 | 91.13 102 | 96.77 10 | 97.90 34 | 98.06 55 | 91.95 54 | 91.93 161 | 91.54 149 | 95.10 131 | 91.85 125 |
|
train_agg | | | 93.89 77 | 93.46 111 | 94.40 53 | 97.35 13 | 93.78 126 | 97.63 45 | 92.19 58 | 88.12 144 | 90.52 129 | 93.57 125 | 95.78 115 | 92.31 51 | 94.78 100 | 93.46 113 | 96.36 82 | 94.70 70 |
|
v144192 | | | 93.89 77 | 93.85 92 | 93.94 69 | 93.50 131 | 94.33 106 | 97.12 47 | 89.49 123 | 90.89 108 | 96.49 13 | 97.78 42 | 98.27 41 | 91.89 56 | 92.17 153 | 91.70 144 | 95.19 127 | 91.78 128 |
|
v1240 | | | 93.89 77 | 93.72 99 | 94.09 58 | 93.98 107 | 94.31 107 | 97.12 47 | 89.37 127 | 90.74 112 | 96.92 9 | 98.05 30 | 97.89 63 | 92.15 53 | 91.53 165 | 91.60 147 | 94.99 136 | 91.93 124 |
|
NCCC | | | 93.87 80 | 93.42 112 | 94.40 53 | 96.84 31 | 95.42 72 | 96.47 78 | 92.62 47 | 92.36 68 | 92.05 100 | 83.83 204 | 95.55 117 | 91.84 59 | 95.89 74 | 95.23 75 | 96.56 75 | 95.63 52 |
|
v1144 | | | 93.83 81 | 93.87 90 | 93.78 76 | 93.72 125 | 94.57 104 | 96.85 59 | 89.98 105 | 91.31 98 | 95.90 19 | 97.89 35 | 98.40 36 | 91.13 80 | 92.01 157 | 92.01 138 | 95.10 131 | 90.94 134 |
|
MVS_111021_HR | | | 93.82 82 | 94.26 86 | 93.31 89 | 95.01 79 | 93.97 122 | 95.73 104 | 89.75 112 | 92.06 76 | 92.49 91 | 94.01 122 | 96.05 112 | 90.61 100 | 95.95 73 | 94.78 88 | 96.28 92 | 93.04 98 |
|
TAPA-MVS | | 88.94 13 | 93.78 83 | 94.31 83 | 93.18 98 | 94.14 100 | 95.99 56 | 95.74 103 | 86.98 170 | 93.43 48 | 93.88 59 | 90.16 158 | 96.88 87 | 91.05 85 | 94.33 107 | 93.95 104 | 97.28 56 | 95.40 55 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
v7 | | | 93.65 84 | 93.73 98 | 93.57 81 | 93.38 136 | 94.60 102 | 96.83 61 | 89.92 108 | 89.69 127 | 95.02 31 | 97.89 35 | 98.24 43 | 91.27 72 | 92.38 145 | 92.18 132 | 94.99 136 | 91.12 132 |
|
EPP-MVSNet | | | 93.63 85 | 93.95 88 | 93.26 93 | 95.15 77 | 96.54 40 | 96.18 96 | 91.97 65 | 91.74 82 | 85.76 162 | 94.95 105 | 84.27 186 | 91.60 65 | 97.61 40 | 97.38 30 | 98.87 4 | 95.18 61 |
|
v17 | | | 93.60 86 | 93.85 92 | 93.30 91 | 93.15 142 | 94.99 90 | 96.46 79 | 90.81 90 | 89.58 129 | 93.61 66 | 97.66 50 | 98.15 51 | 91.19 76 | 92.60 141 | 91.61 146 | 94.61 155 | 92.37 114 |
|
v8 | | | 93.60 86 | 93.82 94 | 93.34 87 | 93.13 143 | 95.06 85 | 96.39 87 | 90.75 92 | 89.90 122 | 94.03 54 | 97.70 48 | 98.21 47 | 91.08 84 | 92.36 146 | 91.47 154 | 94.63 151 | 92.07 120 |
|
MCST-MVS | | | 93.60 86 | 93.40 115 | 93.83 73 | 95.30 73 | 95.40 74 | 96.49 77 | 90.87 89 | 90.08 117 | 91.72 109 | 90.28 156 | 95.99 113 | 91.69 62 | 93.94 120 | 92.99 119 | 96.93 70 | 95.13 62 |
|
PVSNet_Blended_VisFu | | | 93.60 86 | 93.41 113 | 93.83 73 | 96.31 48 | 95.65 65 | 95.71 105 | 90.58 97 | 88.08 147 | 93.17 77 | 95.29 95 | 92.20 157 | 90.72 94 | 94.69 102 | 93.41 116 | 96.51 76 | 94.54 72 |
|
TransMVSNet (Re) | | | 93.55 90 | 96.32 34 | 90.32 135 | 94.38 93 | 94.05 117 | 93.30 154 | 89.53 121 | 97.15 8 | 85.12 167 | 98.83 6 | 97.89 63 | 82.21 157 | 96.75 60 | 96.14 54 | 97.35 54 | 93.46 89 |
|
v16 | | | 93.53 91 | 93.80 97 | 93.20 96 | 93.10 148 | 94.98 91 | 96.43 81 | 90.81 90 | 89.39 132 | 93.12 80 | 97.63 52 | 98.01 59 | 91.19 76 | 92.60 141 | 91.65 145 | 94.58 157 | 92.36 115 |
|
v1 | | | 93.48 92 | 93.57 105 | 93.37 84 | 93.48 132 | 94.18 114 | 96.41 86 | 89.61 117 | 91.46 91 | 95.03 30 | 97.82 39 | 98.43 33 | 90.95 89 | 92.00 158 | 91.37 158 | 94.75 145 | 89.70 146 |
|
v1141 | | | 93.47 93 | 93.56 106 | 93.36 86 | 93.48 132 | 94.17 115 | 96.42 84 | 89.62 115 | 91.44 94 | 94.99 35 | 97.81 40 | 98.42 34 | 90.94 90 | 92.00 158 | 91.38 156 | 94.74 147 | 89.69 148 |
|
divwei89l23v2f112 | | | 93.47 93 | 93.56 106 | 93.37 84 | 93.48 132 | 94.17 115 | 96.42 84 | 89.62 115 | 91.46 91 | 95.00 33 | 97.81 40 | 98.42 34 | 90.94 90 | 92.00 158 | 91.38 156 | 94.75 145 | 89.70 146 |
|
v2v482 | | | 93.42 95 | 93.49 110 | 93.32 88 | 93.44 135 | 94.05 117 | 96.36 93 | 89.76 111 | 91.41 95 | 95.24 25 | 97.63 52 | 98.34 39 | 90.44 103 | 91.65 163 | 91.76 143 | 94.69 148 | 89.62 149 |
|
canonicalmvs | | | 93.38 96 | 94.36 81 | 92.24 113 | 93.94 112 | 96.41 46 | 94.18 135 | 90.47 98 | 93.07 55 | 88.47 148 | 88.66 169 | 93.78 147 | 88.80 114 | 95.74 77 | 95.75 63 | 97.57 50 | 97.13 15 |
|
3Dnovator | | 91.81 5 | 93.36 97 | 94.27 85 | 92.29 112 | 92.99 150 | 95.03 86 | 95.76 102 | 87.79 157 | 93.82 43 | 92.38 94 | 92.19 138 | 93.37 152 | 88.14 122 | 95.26 89 | 94.85 84 | 96.69 73 | 95.40 55 |
|
v18 | | | 93.33 98 | 93.59 104 | 93.04 104 | 92.94 151 | 94.87 93 | 96.31 94 | 90.59 96 | 88.96 134 | 92.89 83 | 97.51 54 | 97.90 62 | 91.01 88 | 92.33 150 | 91.48 153 | 94.50 158 | 92.05 121 |
|
v1neww | | | 93.27 99 | 93.40 115 | 93.12 99 | 93.13 143 | 94.20 111 | 96.39 87 | 89.56 118 | 89.87 124 | 93.95 56 | 97.71 46 | 98.21 47 | 91.09 82 | 92.36 146 | 91.49 150 | 94.62 153 | 89.96 141 |
|
v7new | | | 93.27 99 | 93.40 115 | 93.12 99 | 93.13 143 | 94.20 111 | 96.39 87 | 89.56 118 | 89.87 124 | 93.95 56 | 97.71 46 | 98.21 47 | 91.09 82 | 92.36 146 | 91.49 150 | 94.62 153 | 89.96 141 |
|
pm-mvs1 | | | 93.27 99 | 95.94 45 | 90.16 136 | 94.13 101 | 93.66 128 | 92.61 167 | 89.91 109 | 95.73 18 | 84.28 173 | 98.51 16 | 98.29 40 | 82.80 152 | 96.44 66 | 95.76 62 | 97.25 58 | 93.21 94 |
|
v6 | | | 93.27 99 | 93.41 113 | 93.12 99 | 93.13 143 | 94.20 111 | 96.39 87 | 89.55 120 | 89.89 123 | 93.93 58 | 97.72 44 | 98.22 46 | 91.10 81 | 92.36 146 | 91.49 150 | 94.63 151 | 89.95 143 |
|
TinyColmap | | | 93.17 103 | 93.33 118 | 93.00 105 | 93.84 117 | 92.76 145 | 94.75 124 | 88.90 140 | 93.97 41 | 97.48 4 | 95.28 97 | 95.29 127 | 88.37 119 | 95.31 88 | 91.58 148 | 94.65 150 | 89.10 154 |
|
MVS_111021_LR | | | 93.15 104 | 93.65 101 | 92.56 108 | 93.89 116 | 92.28 156 | 95.09 116 | 86.92 172 | 91.26 100 | 92.99 82 | 94.46 113 | 96.22 106 | 90.64 97 | 95.11 93 | 93.45 114 | 95.85 112 | 92.74 105 |
|
CNLPA | | | 93.14 105 | 93.67 100 | 92.53 109 | 94.62 87 | 94.73 97 | 95.00 118 | 86.57 176 | 92.85 59 | 92.43 92 | 90.94 147 | 94.67 137 | 90.35 104 | 95.41 81 | 93.70 108 | 96.23 97 | 93.37 91 |
|
PLC | | 87.27 15 | 93.08 106 | 92.92 122 | 93.26 93 | 94.67 84 | 95.03 86 | 94.38 128 | 90.10 100 | 91.69 83 | 92.14 98 | 87.24 184 | 93.91 145 | 91.61 64 | 95.05 94 | 94.73 94 | 96.67 74 | 92.80 102 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
CANet | | | 93.07 107 | 93.05 121 | 93.10 102 | 95.90 60 | 95.41 73 | 95.88 99 | 91.94 67 | 84.77 175 | 93.36 70 | 94.05 119 | 95.25 130 | 86.25 133 | 94.33 107 | 93.94 105 | 95.30 122 | 93.58 87 |
|
TSAR-MVS + COLMAP | | | 93.06 108 | 93.65 101 | 92.36 110 | 94.62 87 | 94.28 109 | 95.36 114 | 89.46 125 | 92.18 73 | 91.64 110 | 95.55 88 | 95.27 129 | 88.60 117 | 93.24 129 | 92.50 126 | 94.46 159 | 92.55 111 |
|
Effi-MVS+ | | | 92.93 109 | 92.16 132 | 93.83 73 | 94.29 95 | 93.53 136 | 95.04 117 | 92.98 45 | 85.27 172 | 94.46 44 | 90.24 157 | 95.34 125 | 89.99 106 | 93.72 122 | 94.23 102 | 96.22 98 | 92.79 103 |
|
Fast-Effi-MVS+ | | | 92.93 109 | 92.64 126 | 93.27 92 | 93.81 120 | 93.88 124 | 95.90 98 | 90.61 95 | 83.98 180 | 92.71 85 | 92.81 131 | 96.22 106 | 90.67 95 | 94.90 99 | 93.92 106 | 95.92 109 | 92.77 104 |
|
HQP-MVS | | | 92.87 111 | 92.49 127 | 93.31 89 | 95.75 66 | 95.01 89 | 95.64 108 | 91.06 86 | 88.54 141 | 91.62 111 | 88.16 174 | 96.25 104 | 89.47 109 | 92.26 152 | 91.81 141 | 96.34 87 | 95.40 55 |
|
FMVSNet1 | | | 92.86 112 | 95.26 59 | 90.06 138 | 92.40 162 | 95.16 82 | 94.37 129 | 92.22 55 | 93.18 54 | 82.16 187 | 96.76 69 | 97.48 73 | 81.85 162 | 95.32 85 | 94.98 80 | 97.34 55 | 93.93 82 |
|
CLD-MVS | | | 92.81 113 | 94.32 82 | 91.05 125 | 95.39 72 | 95.31 77 | 95.82 101 | 81.44 210 | 89.40 130 | 91.94 102 | 95.86 82 | 97.36 74 | 85.83 135 | 95.35 83 | 94.59 97 | 95.85 112 | 92.34 117 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
IS_MVSNet | | | 92.76 114 | 93.25 119 | 92.19 114 | 94.91 82 | 95.56 66 | 95.86 100 | 92.12 61 | 88.10 145 | 82.71 182 | 93.15 129 | 88.30 175 | 88.86 113 | 97.29 42 | 96.95 38 | 98.66 10 | 93.38 90 |
|
FC-MVSNet-train | | | 92.75 115 | 95.40 57 | 89.66 148 | 95.21 75 | 94.82 94 | 97.00 52 | 89.40 126 | 91.13 102 | 81.71 188 | 97.72 44 | 96.43 100 | 77.57 195 | 96.89 54 | 96.72 41 | 97.05 66 | 94.09 79 |
|
V42 | | | 92.67 116 | 93.50 109 | 91.71 120 | 91.41 180 | 92.96 143 | 95.71 105 | 85.00 189 | 89.67 128 | 93.22 75 | 97.67 49 | 98.01 59 | 91.02 87 | 92.65 138 | 92.12 135 | 93.86 167 | 91.42 131 |
|
PM-MVS | | | 92.65 117 | 93.20 120 | 92.00 116 | 92.11 173 | 90.16 176 | 95.99 97 | 84.81 192 | 91.31 98 | 92.41 93 | 95.87 81 | 96.64 95 | 92.35 50 | 93.65 125 | 92.91 121 | 94.34 162 | 91.85 125 |
|
QAPM | | | 92.57 118 | 93.51 108 | 91.47 121 | 92.91 153 | 94.82 94 | 93.01 159 | 87.51 161 | 91.49 88 | 91.21 119 | 92.24 136 | 91.70 159 | 88.74 115 | 94.54 104 | 94.39 101 | 95.41 120 | 95.37 58 |
|
MIMVSNet1 | | | 92.52 119 | 94.88 66 | 89.77 144 | 96.09 52 | 91.99 161 | 96.92 55 | 89.68 114 | 95.92 17 | 84.55 170 | 96.64 71 | 98.21 47 | 78.44 188 | 96.08 71 | 95.10 77 | 92.91 179 | 90.22 139 |
|
tfpnnormal | | | 92.45 120 | 94.77 70 | 89.74 145 | 93.95 110 | 93.44 138 | 93.25 155 | 88.49 146 | 95.27 23 | 83.20 177 | 96.51 72 | 96.23 105 | 83.17 149 | 95.47 80 | 94.52 99 | 96.38 81 | 91.97 123 |
|
PCF-MVS | | 87.46 14 | 92.44 121 | 91.80 135 | 93.19 97 | 94.66 85 | 95.80 61 | 96.37 91 | 90.19 99 | 87.57 152 | 92.23 97 | 89.26 165 | 93.97 144 | 89.24 110 | 91.32 167 | 90.82 163 | 96.46 79 | 93.86 83 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
AdaColmap | | | 92.41 122 | 91.49 139 | 93.48 82 | 95.96 58 | 95.02 88 | 95.37 113 | 91.73 71 | 87.97 150 | 91.28 116 | 82.82 209 | 91.04 163 | 90.62 99 | 95.82 76 | 95.07 78 | 95.95 108 | 92.67 106 |
|
v148 | | | 92.38 123 | 92.78 124 | 91.91 117 | 92.86 154 | 92.13 159 | 94.84 120 | 87.03 169 | 91.47 90 | 93.07 81 | 96.92 67 | 98.89 14 | 90.10 105 | 92.05 155 | 89.69 171 | 93.56 170 | 88.27 167 |
|
pmmvs-eth3d | | | 92.34 124 | 92.33 128 | 92.34 111 | 92.67 157 | 90.67 172 | 96.37 91 | 89.06 132 | 90.98 106 | 93.60 67 | 97.13 64 | 97.02 82 | 88.29 120 | 90.20 174 | 91.42 155 | 94.07 165 | 88.89 157 |
|
DELS-MVS | | | 92.33 125 | 93.61 103 | 90.83 128 | 92.84 155 | 95.13 84 | 94.76 123 | 87.22 168 | 87.78 151 | 88.42 150 | 95.78 84 | 95.28 128 | 85.71 136 | 94.44 105 | 93.91 107 | 96.01 106 | 92.97 100 |
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 |
Effi-MVS+-dtu | | | 92.32 126 | 91.66 137 | 93.09 103 | 95.13 78 | 94.73 97 | 94.57 127 | 92.14 60 | 81.74 188 | 90.33 132 | 88.13 175 | 95.91 114 | 89.24 110 | 94.23 116 | 93.65 112 | 97.12 61 | 93.23 93 |
|
UGNet | | | 92.31 127 | 94.70 71 | 89.53 150 | 90.99 188 | 95.53 69 | 96.19 95 | 92.10 63 | 91.35 97 | 85.76 162 | 95.31 94 | 95.48 120 | 76.84 199 | 95.22 90 | 94.79 87 | 95.32 121 | 95.19 60 |
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 |
USDC | | | 92.17 128 | 92.17 131 | 92.18 115 | 92.93 152 | 92.22 157 | 93.66 142 | 87.41 163 | 93.49 46 | 97.99 1 | 94.10 118 | 96.68 94 | 86.46 131 | 92.04 156 | 89.18 177 | 94.61 155 | 87.47 170 |
|
IterMVS-LS | | | 92.10 129 | 92.33 128 | 91.82 119 | 93.18 140 | 93.66 128 | 92.80 165 | 92.27 54 | 90.82 110 | 90.59 128 | 97.19 61 | 90.97 164 | 87.76 124 | 89.60 180 | 90.94 162 | 94.34 162 | 93.16 95 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MSDG | | | 92.09 130 | 92.84 123 | 91.22 124 | 92.55 158 | 92.97 142 | 93.42 146 | 85.43 187 | 90.24 115 | 91.83 106 | 94.70 107 | 94.59 138 | 88.48 118 | 94.91 98 | 93.31 118 | 95.59 118 | 89.15 153 |
|
no-one | | | 92.05 131 | 94.57 75 | 89.12 154 | 85.55 219 | 87.65 189 | 94.21 134 | 77.34 216 | 93.43 48 | 89.64 140 | 95.11 100 | 99.11 9 | 95.86 4 | 95.38 82 | 95.24 74 | 92.08 183 | 96.11 44 |
|
MAR-MVS | | | 91.86 132 | 91.14 142 | 92.71 106 | 94.29 95 | 94.24 110 | 94.91 119 | 91.82 70 | 81.66 189 | 93.32 71 | 84.51 202 | 93.42 151 | 86.86 129 | 95.16 92 | 94.44 100 | 95.05 134 | 94.53 73 |
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 |
EU-MVSNet | | | 91.63 133 | 92.73 125 | 90.35 134 | 88.36 208 | 87.89 186 | 96.53 75 | 81.51 209 | 92.45 65 | 91.82 107 | 96.44 74 | 97.05 81 | 93.26 35 | 94.10 118 | 88.94 181 | 90.61 186 | 92.24 118 |
|
FC-MVSNet-test | | | 91.49 134 | 94.43 79 | 88.07 173 | 94.97 80 | 90.53 175 | 95.42 112 | 91.18 83 | 93.24 52 | 72.94 218 | 98.37 18 | 93.86 146 | 78.78 182 | 97.82 35 | 96.13 55 | 95.13 129 | 91.05 133 |
|
conf0.05thres1000 | | | 91.24 135 | 91.85 134 | 90.53 131 | 94.59 90 | 94.56 105 | 94.33 132 | 89.52 122 | 93.67 44 | 83.77 175 | 91.04 145 | 79.10 203 | 83.98 143 | 96.66 63 | 95.56 68 | 96.98 69 | 92.36 115 |
|
OpenMVS | | 89.22 12 | 91.09 136 | 91.42 140 | 90.71 129 | 92.79 156 | 93.61 133 | 92.74 166 | 85.47 186 | 86.10 167 | 90.73 123 | 85.71 196 | 93.07 155 | 86.69 130 | 94.07 119 | 93.34 117 | 95.86 110 | 94.02 80 |
|
FPMVS | | | 90.81 137 | 91.60 138 | 89.88 142 | 92.52 159 | 88.18 181 | 93.31 153 | 83.62 198 | 91.59 87 | 88.45 149 | 88.96 167 | 89.73 170 | 86.96 127 | 96.42 67 | 95.69 65 | 94.43 160 | 90.65 135 |
|
DI_MVS_plusplus_trai | | | 90.68 138 | 90.40 146 | 91.00 126 | 92.43 161 | 92.61 150 | 94.17 136 | 88.98 134 | 88.32 143 | 88.76 146 | 93.67 124 | 87.58 177 | 86.44 132 | 89.74 178 | 90.33 165 | 95.24 126 | 90.56 138 |
|
Vis-MVSNet (Re-imp) | | | 90.68 138 | 92.18 130 | 88.92 157 | 94.63 86 | 92.75 146 | 92.91 161 | 91.20 82 | 89.21 133 | 75.01 213 | 93.96 123 | 89.07 173 | 82.72 154 | 95.88 75 | 95.30 72 | 97.08 65 | 89.08 155 |
|
FMVSNet2 | | | 90.28 140 | 92.04 133 | 88.23 171 | 91.22 184 | 94.05 117 | 92.88 162 | 90.69 93 | 86.53 163 | 79.89 198 | 94.38 114 | 92.73 156 | 78.54 185 | 91.64 164 | 92.26 129 | 96.17 101 | 92.67 106 |
|
MVS_Test | | | 90.19 141 | 90.58 143 | 89.74 145 | 92.12 172 | 91.74 163 | 92.51 168 | 88.54 145 | 82.80 185 | 87.50 154 | 94.62 108 | 95.02 134 | 83.97 144 | 88.69 187 | 89.32 175 | 93.79 168 | 91.85 125 |
|
EPNet | | | 90.17 142 | 89.07 159 | 91.45 122 | 97.25 17 | 90.62 174 | 94.84 120 | 93.54 40 | 80.96 191 | 91.85 105 | 86.98 187 | 85.88 182 | 77.79 192 | 92.30 151 | 92.58 125 | 93.41 172 | 94.20 78 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
PVSNet_BlendedMVS | | | 90.09 143 | 90.12 148 | 90.05 139 | 92.40 162 | 92.74 147 | 91.74 178 | 85.89 181 | 80.54 198 | 90.30 133 | 88.54 170 | 95.51 118 | 84.69 140 | 92.64 139 | 90.25 167 | 95.28 124 | 90.61 136 |
|
PVSNet_Blended | | | 90.09 143 | 90.12 148 | 90.05 139 | 92.40 162 | 92.74 147 | 91.74 178 | 85.89 181 | 80.54 198 | 90.30 133 | 88.54 170 | 95.51 118 | 84.69 140 | 92.64 139 | 90.25 167 | 95.28 124 | 90.61 136 |
|
pmmvs4 | | | 89.95 145 | 89.32 157 | 90.69 130 | 91.60 179 | 89.17 180 | 94.37 129 | 87.63 160 | 88.07 148 | 91.02 121 | 94.50 111 | 90.50 167 | 86.13 134 | 86.33 202 | 89.40 174 | 93.39 173 | 87.29 172 |
|
MDA-MVSNet-bldmvs | | | 89.75 146 | 91.67 136 | 87.50 178 | 74.25 233 | 90.88 170 | 94.68 125 | 85.89 181 | 91.64 85 | 91.03 120 | 95.86 82 | 94.35 141 | 89.10 112 | 96.87 56 | 86.37 191 | 90.04 187 | 85.72 181 |
|
PatchMatch-RL | | | 89.59 147 | 88.80 164 | 90.51 132 | 92.20 171 | 88.00 185 | 91.72 180 | 86.64 173 | 84.75 176 | 88.25 151 | 87.10 186 | 90.66 166 | 89.85 108 | 93.23 130 | 92.28 128 | 94.41 161 | 85.60 183 |
|
Fast-Effi-MVS+-dtu | | | 89.57 148 | 88.42 170 | 90.92 127 | 93.35 138 | 91.57 164 | 93.01 159 | 95.71 9 | 78.94 212 | 87.65 153 | 84.68 201 | 93.14 154 | 82.00 159 | 90.84 170 | 91.01 161 | 93.78 169 | 88.77 158 |
|
view800 | | | 89.42 149 | 89.11 158 | 89.78 143 | 94.00 103 | 93.71 127 | 93.96 138 | 88.47 147 | 88.10 145 | 82.91 178 | 82.61 210 | 79.85 201 | 83.10 150 | 94.92 97 | 95.38 70 | 96.26 96 | 89.19 152 |
|
GBi-Net | | | 89.35 150 | 90.58 143 | 87.91 174 | 91.22 184 | 94.05 117 | 92.88 162 | 90.05 102 | 79.40 204 | 78.60 204 | 90.58 151 | 87.05 178 | 78.54 185 | 95.32 85 | 94.98 80 | 96.17 101 | 92.67 106 |
|
test1 | | | 89.35 150 | 90.58 143 | 87.91 174 | 91.22 184 | 94.05 117 | 92.88 162 | 90.05 102 | 79.40 204 | 78.60 204 | 90.58 151 | 87.05 178 | 78.54 185 | 95.32 85 | 94.98 80 | 96.17 101 | 92.67 106 |
|
thres600view7 | | | 89.14 152 | 88.83 162 | 89.51 151 | 93.71 126 | 93.55 134 | 93.93 139 | 88.02 153 | 87.30 156 | 82.40 183 | 81.18 213 | 80.63 199 | 82.69 155 | 94.27 111 | 95.90 59 | 96.27 94 | 88.94 156 |
|
view600 | | | 89.09 153 | 88.78 165 | 89.46 152 | 93.59 129 | 93.33 140 | 93.92 140 | 87.76 158 | 87.40 153 | 82.79 179 | 81.29 212 | 80.71 198 | 82.59 156 | 94.28 110 | 95.72 64 | 96.12 104 | 88.70 159 |
|
tfpn_n400 | | | 89.03 154 | 89.39 155 | 88.61 161 | 93.98 107 | 92.33 153 | 91.83 176 | 88.97 135 | 92.97 57 | 78.90 200 | 84.93 198 | 78.24 205 | 81.77 165 | 95.00 95 | 93.67 109 | 96.22 98 | 88.59 160 |
|
tfpnconf | | | 89.03 154 | 89.39 155 | 88.61 161 | 93.98 107 | 92.33 153 | 91.83 176 | 88.97 135 | 92.97 57 | 78.90 200 | 84.93 198 | 78.24 205 | 81.77 165 | 95.00 95 | 93.67 109 | 96.22 98 | 88.59 160 |
|
CVMVSNet | | | 88.97 156 | 89.73 151 | 88.10 172 | 87.33 215 | 85.22 195 | 94.68 125 | 78.68 212 | 88.94 136 | 86.98 158 | 95.55 88 | 85.71 183 | 89.87 107 | 91.19 168 | 89.69 171 | 91.05 184 | 91.78 128 |
|
CANet_DTU | | | 88.95 157 | 89.51 154 | 88.29 170 | 93.12 147 | 91.22 167 | 93.61 143 | 83.47 201 | 80.07 203 | 90.71 127 | 89.19 166 | 93.68 149 | 76.27 203 | 91.44 166 | 91.17 160 | 92.59 180 | 89.83 144 |
|
GA-MVS | | | 88.76 158 | 88.04 173 | 89.59 149 | 92.32 165 | 91.46 165 | 92.28 172 | 86.62 174 | 83.82 182 | 89.84 138 | 92.51 135 | 81.94 193 | 83.53 147 | 89.41 182 | 89.27 176 | 92.95 178 | 87.90 168 |
|
tfpnview11 | | | 88.74 159 | 88.95 160 | 88.50 163 | 93.91 114 | 92.43 152 | 91.70 182 | 88.90 140 | 90.93 107 | 78.90 200 | 84.93 198 | 78.24 205 | 81.71 167 | 94.32 109 | 94.60 96 | 95.86 110 | 87.23 173 |
|
pmmvs5 | | | 88.63 160 | 89.70 152 | 87.39 180 | 89.24 201 | 90.64 173 | 91.87 175 | 82.13 205 | 83.34 183 | 87.86 152 | 94.58 109 | 96.15 109 | 79.87 174 | 87.33 198 | 89.07 180 | 93.39 173 | 86.76 176 |
|
thres400 | | | 88.54 161 | 88.15 172 | 88.98 155 | 93.17 141 | 92.84 144 | 93.56 144 | 86.93 171 | 86.45 164 | 82.37 184 | 79.96 215 | 81.46 196 | 81.83 163 | 93.21 131 | 94.76 89 | 96.04 105 | 88.39 165 |
|
CDS-MVSNet | | | 88.41 162 | 89.79 150 | 86.79 185 | 94.55 91 | 90.82 171 | 92.50 169 | 89.85 110 | 83.26 184 | 80.52 194 | 91.05 144 | 89.93 168 | 69.11 213 | 93.17 132 | 92.71 124 | 94.21 164 | 87.63 169 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
gg-mvs-nofinetune | | | 88.32 163 | 88.81 163 | 87.75 176 | 93.07 149 | 89.37 179 | 89.06 206 | 95.94 7 | 95.29 21 | 87.15 155 | 97.38 58 | 76.38 209 | 68.05 216 | 91.04 169 | 89.10 179 | 93.24 175 | 83.10 191 |
|
IterMVS | | | 88.32 163 | 88.25 171 | 88.41 165 | 90.83 190 | 91.24 166 | 93.07 158 | 81.69 207 | 86.77 161 | 88.55 147 | 95.61 85 | 86.91 181 | 87.01 126 | 87.38 197 | 83.77 198 | 89.29 190 | 86.06 180 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
thres200 | | | 88.29 165 | 87.88 174 | 88.76 158 | 92.50 160 | 93.55 134 | 92.47 170 | 88.02 153 | 84.80 173 | 81.44 189 | 79.28 217 | 82.20 192 | 81.83 163 | 94.27 111 | 93.67 109 | 96.27 94 | 87.40 171 |
|
diffmvs | | | 88.28 166 | 88.88 161 | 87.58 177 | 89.51 199 | 88.07 184 | 91.88 174 | 85.83 184 | 87.31 154 | 86.34 160 | 96.01 80 | 88.90 174 | 81.90 160 | 85.49 209 | 86.61 190 | 90.04 187 | 89.77 145 |
|
IB-MVS | | 86.01 17 | 88.24 167 | 87.63 176 | 88.94 156 | 92.03 176 | 91.77 162 | 92.40 171 | 85.58 185 | 78.24 214 | 84.85 168 | 71.99 230 | 93.45 150 | 83.96 145 | 93.48 127 | 92.33 127 | 94.84 143 | 92.15 119 |
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 |
MDTV_nov1_ep13_2view | | | 88.22 168 | 87.85 175 | 88.65 160 | 91.40 181 | 86.75 191 | 94.07 137 | 84.97 190 | 88.86 138 | 93.20 76 | 96.11 79 | 96.21 108 | 83.70 146 | 87.29 199 | 80.29 209 | 84.56 208 | 79.46 206 |
|
test20.03 | | | 88.20 169 | 91.26 141 | 84.63 199 | 96.64 40 | 89.39 178 | 90.73 193 | 89.97 106 | 91.07 104 | 72.02 220 | 94.98 104 | 95.45 121 | 69.35 212 | 92.70 137 | 91.19 159 | 89.06 192 | 84.02 184 |
|
HyFIR lowres test | | | 88.19 170 | 86.56 183 | 90.09 137 | 91.24 183 | 92.17 158 | 94.30 133 | 88.79 142 | 84.06 178 | 85.45 166 | 89.52 163 | 85.64 184 | 88.64 116 | 85.40 210 | 87.28 186 | 92.14 182 | 81.87 194 |
|
tfpn1000 | | | 88.13 171 | 88.68 167 | 87.49 179 | 93.94 112 | 92.64 149 | 91.50 184 | 88.70 144 | 90.12 116 | 74.35 215 | 86.74 190 | 75.27 211 | 80.14 173 | 94.16 117 | 94.66 95 | 96.33 89 | 87.16 174 |
|
tfpn200view9 | | | 87.94 172 | 87.51 177 | 88.44 164 | 92.28 166 | 93.63 132 | 93.35 152 | 88.11 151 | 80.90 192 | 80.89 190 | 78.25 218 | 82.25 188 | 79.65 177 | 94.27 111 | 94.76 89 | 96.36 82 | 88.48 162 |
|
conf200view11 | | | 87.93 173 | 87.51 177 | 88.41 165 | 92.28 166 | 93.64 130 | 93.36 148 | 88.12 149 | 80.90 192 | 80.71 192 | 78.25 218 | 82.25 188 | 79.65 177 | 94.27 111 | 94.76 89 | 96.36 82 | 88.48 162 |
|
FMVSNet3 | | | 87.90 174 | 88.63 168 | 87.04 182 | 89.78 198 | 93.46 137 | 91.62 183 | 90.05 102 | 79.40 204 | 78.60 204 | 90.58 151 | 87.05 178 | 77.07 198 | 88.03 194 | 89.86 170 | 95.12 130 | 92.04 122 |
|
MS-PatchMatch | | | 87.72 175 | 88.62 169 | 86.66 187 | 90.81 191 | 88.18 181 | 90.92 189 | 82.25 204 | 85.86 169 | 80.40 197 | 90.14 159 | 89.29 172 | 84.93 137 | 89.39 183 | 89.12 178 | 90.67 185 | 88.34 166 |
|
tfpn | | | 87.65 176 | 85.66 188 | 89.96 141 | 94.36 94 | 93.94 123 | 93.85 141 | 89.02 133 | 88.71 140 | 82.78 180 | 83.79 205 | 53.79 232 | 83.43 148 | 95.35 83 | 94.54 98 | 96.35 86 | 89.51 150 |
|
tfpn111 | | | 87.59 177 | 86.89 180 | 88.41 165 | 92.28 166 | 93.64 130 | 93.36 148 | 88.12 149 | 80.90 192 | 80.71 192 | 73.93 227 | 82.25 188 | 79.65 177 | 94.27 111 | 94.76 89 | 96.36 82 | 88.48 162 |
|
Anonymous20231206 | | | 87.45 178 | 89.66 153 | 84.87 196 | 94.00 103 | 87.73 188 | 91.36 185 | 86.41 179 | 88.89 137 | 75.03 212 | 92.59 134 | 96.82 89 | 72.48 210 | 89.72 179 | 88.06 183 | 89.93 189 | 83.81 186 |
|
EPNet_dtu | | | 87.40 179 | 86.27 185 | 88.72 159 | 95.68 68 | 83.37 204 | 92.09 173 | 90.08 101 | 78.11 217 | 91.29 115 | 86.33 191 | 89.74 169 | 75.39 204 | 89.07 184 | 87.89 184 | 87.81 197 | 89.38 151 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CHOSEN 1792x2688 | | | 86.64 180 | 86.62 182 | 86.65 188 | 90.33 194 | 87.86 187 | 93.19 156 | 83.30 202 | 83.95 181 | 82.32 185 | 87.93 177 | 89.34 171 | 86.92 128 | 85.64 208 | 84.95 195 | 83.85 214 | 86.68 177 |
|
testgi | | | 86.49 181 | 90.31 147 | 82.03 204 | 95.63 69 | 88.18 181 | 93.47 145 | 84.89 191 | 93.23 53 | 69.54 227 | 87.16 185 | 97.96 61 | 60.66 221 | 91.90 162 | 89.90 169 | 87.99 195 | 83.84 185 |
|
thres100view900 | | | 86.46 182 | 86.00 187 | 86.99 183 | 92.28 166 | 91.03 168 | 91.09 187 | 84.49 194 | 80.90 192 | 80.89 190 | 78.25 218 | 82.25 188 | 77.57 195 | 90.17 175 | 92.84 122 | 95.63 116 | 86.57 178 |
|
gm-plane-assit | | | 86.15 183 | 82.51 199 | 90.40 133 | 95.81 64 | 92.29 155 | 97.99 31 | 84.66 193 | 92.15 74 | 93.15 78 | 97.84 38 | 44.65 237 | 78.60 184 | 88.02 195 | 85.95 192 | 92.20 181 | 76.69 213 |
|
tfpn_ndepth | | | 85.89 184 | 86.40 184 | 85.30 194 | 91.31 182 | 92.47 151 | 90.78 191 | 87.75 159 | 84.79 174 | 71.04 222 | 76.95 222 | 78.80 204 | 74.52 207 | 92.72 136 | 93.43 115 | 96.39 80 | 85.65 182 |
|
conf0.01 | | | 85.72 185 | 83.49 196 | 88.32 168 | 92.11 173 | 93.35 139 | 93.36 148 | 88.02 153 | 80.90 192 | 80.51 195 | 74.83 225 | 59.86 230 | 79.65 177 | 93.80 121 | 94.76 89 | 96.29 91 | 86.94 175 |
|
CMPMVS | | 66.55 18 | 85.55 186 | 87.46 179 | 83.32 202 | 84.99 220 | 81.97 209 | 79.19 231 | 75.93 218 | 79.32 207 | 88.82 144 | 85.09 197 | 91.07 162 | 82.12 158 | 92.56 143 | 89.63 173 | 88.84 193 | 92.56 110 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
CR-MVSNet | | | 85.32 187 | 81.58 203 | 89.69 147 | 90.36 193 | 84.79 198 | 86.72 217 | 92.22 55 | 75.38 222 | 90.73 123 | 90.41 155 | 67.88 220 | 84.86 138 | 83.76 212 | 85.74 193 | 93.24 175 | 83.14 189 |
|
conf0.002 | | | 84.82 188 | 81.84 202 | 88.30 169 | 92.05 175 | 93.28 141 | 93.36 148 | 88.00 156 | 80.90 192 | 80.48 196 | 73.43 229 | 52.48 235 | 79.65 177 | 93.72 122 | 92.82 123 | 96.28 92 | 86.22 179 |
|
MVSTER | | | 84.79 189 | 83.79 194 | 85.96 190 | 89.14 202 | 89.80 177 | 89.39 204 | 82.99 203 | 74.16 226 | 82.78 180 | 85.97 194 | 66.81 222 | 76.84 199 | 90.77 171 | 88.83 182 | 94.66 149 | 90.19 140 |
|
MIMVSNet | | | 84.76 190 | 86.75 181 | 82.44 203 | 91.71 178 | 85.95 193 | 89.74 202 | 89.49 123 | 85.28 171 | 69.69 226 | 87.93 177 | 90.88 165 | 64.85 218 | 88.26 192 | 87.74 185 | 89.18 191 | 81.24 195 |
|
new-patchmatchnet | | | 84.45 191 | 88.75 166 | 79.43 211 | 93.28 139 | 81.87 210 | 81.68 228 | 83.48 200 | 94.47 30 | 71.53 221 | 98.33 19 | 97.88 66 | 58.61 224 | 90.35 173 | 77.33 216 | 87.99 195 | 81.05 197 |
|
thresconf0.02 | | | 84.34 192 | 82.02 201 | 87.06 181 | 92.23 170 | 90.93 169 | 91.05 188 | 86.43 178 | 88.83 139 | 77.65 210 | 73.93 227 | 55.81 231 | 79.68 176 | 90.62 172 | 90.28 166 | 95.30 122 | 83.73 187 |
|
LP | | | 84.09 193 | 84.31 191 | 83.85 201 | 79.40 228 | 84.34 201 | 90.26 196 | 84.02 195 | 87.99 149 | 84.66 169 | 91.61 143 | 79.13 202 | 80.58 171 | 85.90 207 | 81.59 204 | 84.16 213 | 79.59 205 |
|
PatchT | | | 83.44 194 | 81.10 205 | 86.18 189 | 77.92 230 | 82.58 208 | 89.87 200 | 87.39 164 | 75.88 221 | 90.73 123 | 89.86 160 | 66.71 223 | 84.86 138 | 83.76 212 | 85.74 193 | 86.33 204 | 83.14 189 |
|
RPMNet | | | 83.42 195 | 78.40 214 | 89.28 153 | 89.79 197 | 84.79 198 | 90.64 194 | 92.11 62 | 75.38 222 | 87.10 156 | 79.80 216 | 61.99 229 | 82.79 153 | 81.88 218 | 82.07 203 | 93.23 177 | 82.87 192 |
|
TAMVS | | | 82.96 196 | 86.15 186 | 79.24 214 | 90.57 192 | 83.12 207 | 87.29 212 | 75.12 221 | 84.06 178 | 65.81 229 | 92.22 137 | 88.27 176 | 69.11 213 | 88.72 185 | 87.26 188 | 87.56 200 | 79.38 207 |
|
PatchmatchNet | | | 82.44 197 | 78.69 213 | 86.83 184 | 89.81 196 | 81.55 211 | 90.78 191 | 87.27 167 | 82.39 187 | 88.85 143 | 88.31 173 | 70.96 216 | 81.90 160 | 78.58 225 | 74.33 225 | 82.35 220 | 74.69 217 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MDTV_nov1_ep13 | | | 82.33 198 | 79.66 208 | 85.45 192 | 88.83 204 | 83.88 202 | 90.09 199 | 81.98 206 | 79.07 211 | 88.82 144 | 88.70 168 | 73.77 212 | 78.41 189 | 80.29 222 | 76.08 219 | 84.56 208 | 75.83 214 |
|
tpmp4_e23 | | | 82.16 199 | 78.26 216 | 86.70 186 | 89.92 195 | 84.82 197 | 91.17 186 | 89.95 107 | 81.21 190 | 87.10 156 | 81.91 211 | 64.01 226 | 77.88 191 | 79.89 223 | 74.99 223 | 84.18 212 | 81.00 198 |
|
CostFormer | | | 82.15 200 | 79.54 209 | 85.20 195 | 88.92 203 | 85.70 194 | 90.87 190 | 86.26 180 | 79.19 210 | 83.87 174 | 87.89 179 | 69.20 218 | 76.62 201 | 77.50 228 | 75.28 221 | 84.69 207 | 82.02 193 |
|
PMMVS | | | 81.93 201 | 83.48 197 | 80.12 209 | 72.35 234 | 75.05 227 | 88.54 208 | 64.01 227 | 77.02 219 | 82.22 186 | 87.51 182 | 91.12 161 | 79.70 175 | 86.59 200 | 86.64 189 | 93.88 166 | 80.41 199 |
|
pmmvs3 | | | 81.69 202 | 83.83 193 | 79.19 215 | 78.33 229 | 78.57 217 | 89.53 203 | 58.71 231 | 78.88 213 | 84.34 172 | 88.36 172 | 91.96 158 | 77.69 194 | 87.48 196 | 82.42 202 | 86.54 203 | 79.18 208 |
|
tpm | | | 81.58 203 | 78.84 211 | 84.79 198 | 91.11 187 | 79.50 214 | 89.79 201 | 83.75 196 | 79.30 208 | 92.05 100 | 90.98 146 | 64.78 225 | 74.54 205 | 80.50 221 | 76.67 218 | 77.49 225 | 80.15 202 |
|
test0.0.03 1 | | | 81.51 204 | 83.30 198 | 79.42 212 | 93.99 105 | 86.50 192 | 85.93 223 | 87.32 165 | 78.16 215 | 61.62 230 | 80.78 214 | 81.78 194 | 59.87 222 | 88.40 191 | 87.27 187 | 87.78 199 | 80.19 201 |
|
test1235678 | | | 81.50 205 | 84.78 189 | 77.67 221 | 87.67 211 | 80.27 212 | 90.12 197 | 77.62 214 | 80.36 200 | 69.71 224 | 90.93 148 | 96.51 97 | 56.57 226 | 88.60 189 | 84.93 196 | 84.34 210 | 71.87 226 |
|
testmv | | | 81.49 206 | 84.76 190 | 77.67 221 | 87.67 211 | 80.25 213 | 90.12 197 | 77.62 214 | 80.34 201 | 69.71 224 | 90.92 149 | 96.47 98 | 56.57 226 | 88.58 190 | 84.92 197 | 84.33 211 | 71.86 227 |
|
dps | | | 81.42 207 | 77.88 221 | 85.56 191 | 87.67 211 | 85.17 196 | 88.37 210 | 87.46 162 | 74.37 225 | 84.55 170 | 86.80 189 | 62.18 228 | 80.20 172 | 81.13 220 | 77.52 215 | 85.10 205 | 77.98 211 |
|
test-LLR | | | 80.62 208 | 77.20 224 | 84.62 200 | 93.99 105 | 75.11 225 | 87.04 213 | 87.32 165 | 70.11 230 | 78.59 207 | 83.17 207 | 71.60 214 | 73.88 208 | 82.32 216 | 79.20 212 | 86.91 201 | 78.87 209 |
|
tpm cat1 | | | 80.03 209 | 75.93 227 | 84.81 197 | 89.31 200 | 83.26 206 | 88.86 207 | 86.55 177 | 79.24 209 | 86.10 161 | 84.22 203 | 63.62 227 | 77.37 197 | 73.43 230 | 70.88 228 | 80.67 221 | 76.87 212 |
|
N_pmnet | | | 79.33 210 | 84.22 192 | 73.62 226 | 91.72 177 | 73.72 229 | 86.11 221 | 76.36 217 | 92.38 67 | 53.38 234 | 95.54 90 | 95.62 116 | 59.14 223 | 84.23 211 | 74.84 224 | 75.03 230 | 73.25 223 |
|
EPMVS | | | 79.26 211 | 78.20 218 | 80.49 207 | 87.04 216 | 78.86 216 | 86.08 222 | 83.51 199 | 82.63 186 | 73.94 216 | 89.59 161 | 68.67 219 | 72.03 211 | 78.17 226 | 75.08 222 | 80.37 222 | 74.37 219 |
|
CHOSEN 280x420 | | | 79.24 212 | 78.26 216 | 80.38 208 | 79.60 227 | 68.80 234 | 89.32 205 | 75.38 219 | 77.25 218 | 78.02 209 | 75.57 224 | 76.17 210 | 81.19 169 | 88.61 188 | 81.39 205 | 78.79 223 | 80.03 203 |
|
DWT-MVSNet_training | | | 79.22 213 | 73.99 229 | 85.33 193 | 88.57 205 | 84.41 200 | 90.56 195 | 80.96 211 | 73.90 227 | 85.72 164 | 75.62 223 | 50.09 236 | 81.30 168 | 76.91 229 | 77.02 217 | 84.88 206 | 79.97 204 |
|
ADS-MVSNet | | | 79.11 214 | 79.38 210 | 78.80 217 | 81.90 225 | 75.59 223 | 84.36 224 | 83.69 197 | 87.31 154 | 76.76 211 | 87.58 181 | 76.90 208 | 68.55 215 | 78.70 224 | 75.56 220 | 77.53 224 | 74.07 221 |
|
FMVSNet5 | | | 79.08 215 | 78.83 212 | 79.38 213 | 87.52 214 | 86.78 190 | 87.64 211 | 78.15 213 | 69.54 232 | 70.64 223 | 65.97 234 | 65.44 224 | 63.87 219 | 90.17 175 | 90.46 164 | 88.48 194 | 83.45 188 |
|
tpmrst | | | 78.81 216 | 76.18 226 | 81.87 205 | 88.56 206 | 77.45 220 | 86.74 216 | 81.52 208 | 80.08 202 | 83.48 176 | 90.84 150 | 66.88 221 | 74.54 205 | 73.04 231 | 71.02 227 | 76.38 227 | 73.95 222 |
|
test-mter | | | 78.71 217 | 78.35 215 | 79.12 216 | 84.03 222 | 76.58 221 | 88.51 209 | 59.06 230 | 71.06 228 | 78.87 203 | 83.73 206 | 71.83 213 | 76.44 202 | 83.41 215 | 80.61 207 | 87.79 198 | 81.24 195 |
|
MVS-HIRNet | | | 78.28 218 | 75.28 228 | 81.79 206 | 80.33 226 | 69.38 233 | 76.83 232 | 86.59 175 | 70.76 229 | 86.66 159 | 89.57 162 | 81.04 197 | 77.74 193 | 77.81 227 | 71.65 226 | 82.62 217 | 66.73 229 |
|
testus | | | 78.20 219 | 81.50 204 | 74.36 225 | 85.59 218 | 79.36 215 | 86.99 215 | 65.76 225 | 76.01 220 | 73.00 217 | 77.98 221 | 93.35 153 | 51.30 232 | 86.33 202 | 82.79 201 | 83.50 216 | 74.68 218 |
|
E-PMN | | | 77.81 220 | 77.88 221 | 77.73 220 | 88.26 209 | 70.48 232 | 80.19 230 | 71.20 223 | 86.66 162 | 72.89 219 | 88.09 176 | 81.74 195 | 78.75 183 | 90.02 177 | 68.30 229 | 75.10 229 | 59.85 232 |
|
EMVS | | | 77.65 221 | 77.49 223 | 77.83 218 | 87.75 210 | 71.02 231 | 81.13 229 | 70.54 224 | 86.38 165 | 74.52 214 | 89.38 164 | 80.19 200 | 78.22 190 | 89.48 181 | 67.13 230 | 74.83 231 | 58.84 233 |
|
TESTMET0.1,1 | | | 77.47 222 | 77.20 224 | 77.78 219 | 81.94 224 | 75.11 225 | 87.04 213 | 58.33 232 | 70.11 230 | 78.59 207 | 83.17 207 | 71.60 214 | 73.88 208 | 82.32 216 | 79.20 212 | 86.91 201 | 78.87 209 |
|
1111 | | | 76.85 223 | 78.03 219 | 75.46 223 | 94.16 98 | 78.29 218 | 86.40 219 | 89.12 130 | 87.23 157 | 61.26 231 | 95.15 98 | 44.14 238 | 51.46 230 | 86.04 205 | 81.00 206 | 70.40 233 | 74.37 219 |
|
new_pmnet | | | 76.65 224 | 83.52 195 | 68.63 228 | 82.60 223 | 72.08 230 | 76.76 233 | 64.17 226 | 84.41 177 | 49.73 236 | 91.77 140 | 91.53 160 | 56.16 228 | 86.59 200 | 83.26 200 | 82.37 219 | 75.02 215 |
|
test12356 | | | 75.40 225 | 80.89 206 | 69.01 227 | 77.43 231 | 75.75 222 | 83.03 226 | 61.48 228 | 78.13 216 | 59.08 233 | 87.69 180 | 94.95 136 | 57.37 225 | 88.18 193 | 80.59 208 | 75.65 228 | 60.93 231 |
|
MVE | | 60.41 19 | 73.21 226 | 80.84 207 | 64.30 229 | 56.34 235 | 57.24 236 | 75.28 235 | 72.76 222 | 87.14 160 | 41.39 237 | 86.31 192 | 85.30 185 | 80.66 170 | 86.17 204 | 83.36 199 | 59.35 234 | 80.38 200 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test2356 | | | 72.95 227 | 71.24 230 | 74.95 224 | 84.89 221 | 75.49 224 | 82.67 227 | 75.38 219 | 68.02 233 | 68.65 228 | 74.40 226 | 52.81 234 | 55.61 229 | 81.50 219 | 79.80 210 | 82.50 218 | 66.70 230 |
|
testpf | | | 72.68 228 | 66.81 231 | 79.53 210 | 86.52 217 | 73.89 228 | 83.56 225 | 88.74 143 | 58.70 235 | 79.68 199 | 71.31 231 | 53.64 233 | 62.23 220 | 68.68 232 | 66.64 231 | 76.46 226 | 74.82 216 |
|
PMMVS2 | | | 69.86 229 | 82.14 200 | 55.52 231 | 75.19 232 | 63.08 235 | 75.52 234 | 60.97 229 | 88.50 142 | 25.11 239 | 91.77 140 | 96.44 99 | 25.43 233 | 88.70 186 | 79.34 211 | 70.93 232 | 67.17 228 |
|
.test1245 | | | 60.07 230 | 56.75 232 | 63.93 230 | 94.16 98 | 78.29 218 | 86.40 219 | 89.12 130 | 87.23 157 | 61.26 231 | 95.15 98 | 44.14 238 | 51.46 230 | 86.04 205 | 2.51 234 | 1.21 238 | 3.92 235 |
|
GG-mvs-BLEND | | | 54.28 231 | 77.89 220 | 26.72 233 | 0.37 239 | 83.31 205 | 70.04 236 | 0.39 237 | 74.71 224 | 5.36 240 | 68.78 232 | 83.06 187 | 0.62 237 | 83.73 214 | 78.99 214 | 83.55 215 | 72.68 225 |
|
testmvs | | | 2.38 232 | 3.35 233 | 1.26 235 | 0.83 237 | 0.96 240 | 1.53 240 | 0.83 235 | 3.59 237 | 1.63 242 | 6.03 236 | 2.93 241 | 1.55 236 | 3.49 235 | 2.51 234 | 1.21 238 | 3.92 235 |
|
test123 | | | 2.16 233 | 2.82 234 | 1.41 234 | 0.62 238 | 1.18 239 | 1.53 240 | 0.82 236 | 2.78 238 | 2.27 241 | 4.18 237 | 1.98 242 | 1.64 235 | 2.58 236 | 3.01 233 | 1.56 237 | 4.00 234 |
|
sosnet-low-res | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 240 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 243 | 0.00 238 | 0.00 243 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
sosnet | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 240 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 243 | 0.00 238 | 0.00 243 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
ambc | | | | 94.61 74 | | 98.09 5 | 95.14 83 | 91.71 181 | | 94.18 38 | 96.46 14 | 96.26 75 | 96.30 102 | 91.26 74 | 94.70 101 | 92.00 139 | 93.45 171 | 93.67 84 |
|
MTAPA | | | | | | | | | | | 94.88 39 | | 96.88 87 | | | | | |
|
MTMP | | | | | | | | | | | 95.43 21 | | 97.25 77 | | | | | |
|
Patchmatch-RL test | | | | | | | | 8.96 239 | | | | | | | | | | |
|
tmp_tt | | | | | 28.44 232 | 36.05 236 | 15.86 238 | 21.29 238 | 6.40 234 | 54.52 236 | 51.96 235 | 50.37 235 | 38.68 240 | 9.55 234 | 61.75 234 | 59.66 232 | 45.36 236 | |
|
XVS | | | | | | 96.86 29 | 97.48 16 | 98.73 3 | | | 93.28 72 | | 96.82 89 | | | | 98.17 33 | |
|
X-MVStestdata | | | | | | 96.86 29 | 97.48 16 | 98.73 3 | | | 93.28 72 | | 96.82 89 | | | | 98.17 33 | |
|
abl_6 | | | | | 91.88 118 | 93.76 122 | 94.98 91 | 95.64 108 | 88.97 135 | 86.20 166 | 90.00 137 | 86.31 192 | 94.50 139 | 87.31 125 | | | 95.60 117 | 92.48 113 |
|
mPP-MVS | | | | | | 98.24 3 | | | | | | | 97.65 71 | | | | | |
|
NP-MVS | | | | | | | | | | 85.48 170 | | | | | | | | |
|
Patchmtry | | | | | | | 83.74 203 | 86.72 217 | 92.22 55 | | 90.73 123 | | | | | | | |
|
DeepMVS_CX | | | | | | | 47.68 237 | 53.20 237 | 19.21 233 | 63.24 234 | 26.96 238 | 66.50 233 | 69.82 217 | 66.91 217 | 64.27 233 | | 54.91 235 | 72.72 224 |
|