TDRefinement | | | 93.16 1 | 95.57 1 | 90.36 1 | 88.79 48 | 93.57 1 | 97.27 1 | 78.23 20 | 95.55 2 | 93.00 1 | 93.98 17 | 96.01 49 | 87.53 1 | 97.69 1 | 96.81 1 | 97.33 1 | 95.34 4 |
|
COLMAP_ROB | | 85.66 2 | 91.85 2 | 95.01 2 | 88.16 12 | 88.98 47 | 92.86 2 | 95.51 20 | 72.17 55 | 94.95 5 | 91.27 3 | 94.11 16 | 97.77 14 | 84.22 8 | 96.49 4 | 95.27 5 | 96.79 2 | 93.60 10 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
LTVRE_ROB | | 86.82 1 | 91.55 3 | 94.43 3 | 88.19 11 | 83.19 102 | 86.35 61 | 93.60 34 | 78.79 17 | 95.48 4 | 91.79 2 | 93.08 25 | 97.21 23 | 86.34 3 | 97.06 2 | 96.27 3 | 95.46 23 | 95.56 3 |
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 |
ACMMPR | | | 91.30 4 | 92.88 10 | 89.46 4 | 91.92 11 | 91.61 5 | 96.60 5 | 79.46 12 | 90.08 29 | 88.53 14 | 89.54 76 | 95.57 61 | 84.25 7 | 95.24 20 | 94.27 13 | 95.97 11 | 93.85 7 |
|
CP-MVS | | | 91.09 5 | 92.33 21 | 89.65 2 | 92.16 10 | 90.41 25 | 96.46 10 | 80.38 6 | 88.26 42 | 89.17 11 | 87.00 103 | 96.34 38 | 83.95 10 | 95.77 11 | 94.72 8 | 95.81 17 | 93.78 9 |
|
MP-MVS | | | 90.84 6 | 91.95 29 | 89.55 3 | 92.92 5 | 90.90 18 | 96.56 6 | 79.60 9 | 86.83 56 | 88.75 13 | 89.00 84 | 94.38 88 | 84.01 9 | 94.94 25 | 94.34 11 | 95.45 24 | 93.24 20 |
|
ACMM | | 80.67 7 | 90.67 7 | 92.46 17 | 88.57 8 | 91.35 20 | 89.93 30 | 96.34 12 | 77.36 30 | 90.17 27 | 86.88 30 | 87.32 97 | 96.63 28 | 83.32 14 | 95.79 10 | 94.49 10 | 96.19 9 | 92.91 23 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMMP | | | 90.63 8 | 92.40 18 | 88.56 9 | 91.24 26 | 91.60 6 | 96.49 9 | 77.53 25 | 87.89 44 | 86.87 31 | 87.24 99 | 96.46 32 | 82.87 19 | 95.59 15 | 94.50 9 | 96.35 6 | 93.51 15 |
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 | | | 90.56 9 | 92.38 19 | 88.43 10 | 90.88 30 | 91.15 11 | 95.35 22 | 77.65 24 | 86.26 62 | 87.23 24 | 90.45 68 | 97.35 20 | 83.20 15 | 95.44 16 | 93.41 20 | 96.28 8 | 92.63 24 |
|
PGM-MVS | | | 90.42 10 | 91.58 34 | 89.05 6 | 91.77 13 | 91.06 13 | 96.51 7 | 78.94 15 | 85.41 70 | 87.67 18 | 87.02 102 | 95.26 69 | 83.62 13 | 95.01 24 | 93.94 16 | 95.79 19 | 93.40 18 |
|
MPTG | | | 90.38 11 | 91.35 37 | 89.25 5 | 93.08 3 | 86.59 58 | 96.45 11 | 79.00 14 | 90.23 26 | 89.30 10 | 85.87 113 | 94.97 79 | 82.54 21 | 95.05 23 | 94.83 7 | 95.14 27 | 91.94 32 |
|
DeepC-MVS | | 83.59 4 | 90.37 12 | 92.56 16 | 87.82 15 | 91.26 25 | 92.33 3 | 94.72 28 | 80.04 7 | 90.01 30 | 84.61 44 | 93.33 21 | 94.22 90 | 80.59 29 | 92.90 40 | 92.52 28 | 95.69 21 | 92.57 25 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
HFP-MVS | | | 90.32 13 | 92.37 20 | 87.94 14 | 91.46 19 | 90.91 17 | 95.69 19 | 79.49 10 | 89.94 32 | 83.50 62 | 89.06 83 | 94.44 87 | 81.68 26 | 94.17 31 | 94.19 14 | 95.81 17 | 93.87 6 |
|
PMVS | | 79.51 9 | 90.23 14 | 92.67 12 | 87.39 20 | 90.16 37 | 88.75 38 | 93.64 33 | 75.78 40 | 90.00 31 | 83.70 56 | 92.97 27 | 92.22 113 | 86.13 4 | 97.01 3 | 96.79 2 | 94.94 29 | 90.96 43 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ACMP | | 80.00 8 | 90.12 15 | 92.30 22 | 87.58 18 | 90.83 32 | 91.10 12 | 94.96 26 | 76.06 38 | 87.47 49 | 85.33 40 | 88.91 86 | 97.65 18 | 82.13 23 | 95.31 17 | 93.44 19 | 96.14 10 | 92.22 29 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
SteuartSystems-ACMMP | | | 90.00 16 | 91.73 31 | 87.97 13 | 91.21 27 | 90.29 27 | 96.51 7 | 78.00 22 | 86.33 60 | 85.32 41 | 88.23 89 | 94.67 83 | 82.08 24 | 95.13 22 | 93.88 17 | 94.72 34 | 93.59 11 |
Skip Steuart: Steuart Systems R&D Blog. |
SD-MVS | | | 89.91 17 | 92.23 25 | 87.19 21 | 91.31 22 | 89.79 32 | 94.31 30 | 75.34 42 | 89.26 34 | 81.79 76 | 92.68 30 | 95.08 75 | 83.88 11 | 93.10 37 | 92.69 25 | 96.54 4 | 93.02 21 |
|
ACMMP_Plus | | | 89.86 18 | 91.96 28 | 87.42 19 | 91.00 28 | 90.08 28 | 96.00 17 | 76.61 34 | 89.28 33 | 87.73 17 | 90.04 70 | 91.80 120 | 78.71 38 | 94.36 29 | 93.82 18 | 94.48 35 | 94.32 5 |
|
APDe-MVS | | | 89.85 19 | 92.91 9 | 86.29 26 | 90.47 36 | 91.34 7 | 96.04 16 | 76.41 37 | 91.11 15 | 78.50 99 | 93.44 20 | 95.82 53 | 81.55 27 | 93.16 36 | 91.90 37 | 94.77 33 | 93.58 13 |
|
OPM-MVS | | | 89.82 20 | 92.24 24 | 86.99 22 | 90.86 31 | 89.35 34 | 95.07 25 | 75.91 39 | 91.16 14 | 86.87 31 | 91.07 60 | 97.29 21 | 79.13 36 | 93.32 34 | 91.99 36 | 94.12 39 | 91.49 39 |
|
WR-MVS | | | 89.79 21 | 93.66 4 | 85.27 36 | 91.32 21 | 88.27 42 | 93.49 35 | 79.86 8 | 92.75 8 | 75.37 110 | 96.86 1 | 98.38 6 | 75.10 66 | 95.93 8 | 94.07 15 | 96.46 5 | 89.39 55 |
|
TSAR-MVS + MP. | | | 89.67 22 | 92.25 23 | 86.65 24 | 91.53 16 | 90.98 16 | 96.15 14 | 73.30 52 | 87.88 45 | 81.83 75 | 92.92 28 | 95.15 73 | 82.23 22 | 93.58 33 | 92.25 33 | 94.87 30 | 93.01 22 |
|
CPTT-MVS | | | 89.63 23 | 90.52 45 | 88.59 7 | 90.95 29 | 90.74 20 | 95.71 18 | 79.13 13 | 87.70 46 | 85.68 39 | 80.05 145 | 95.74 56 | 84.77 6 | 94.28 30 | 92.68 26 | 95.28 26 | 92.45 27 |
|
ACMH+ | | 79.05 11 | 89.62 24 | 93.08 7 | 85.58 31 | 88.58 50 | 89.26 35 | 92.18 42 | 74.23 48 | 93.55 7 | 82.66 67 | 92.32 40 | 98.35 8 | 80.29 31 | 95.28 18 | 92.34 31 | 95.52 22 | 90.43 46 |
|
X-MVS | | | 89.36 25 | 90.73 42 | 87.77 17 | 91.50 18 | 91.23 8 | 96.76 4 | 78.88 16 | 87.29 51 | 87.14 27 | 78.98 149 | 94.53 84 | 76.47 53 | 95.25 19 | 94.28 12 | 95.85 14 | 93.55 14 |
|
ESAPD | | | 89.27 26 | 91.76 30 | 86.36 25 | 90.60 35 | 90.40 26 | 95.08 24 | 77.43 28 | 87.49 48 | 80.35 88 | 92.38 38 | 94.32 89 | 80.59 29 | 92.69 45 | 91.58 40 | 94.13 38 | 93.44 16 |
|
TSAR-MVS + ACMM | | | 89.14 27 | 92.11 27 | 85.67 30 | 89.27 44 | 90.61 23 | 90.98 48 | 79.48 11 | 88.86 37 | 79.80 90 | 93.01 26 | 93.53 98 | 83.17 16 | 92.75 44 | 92.45 29 | 91.32 73 | 93.59 11 |
|
SixPastTwentyTwo | | | 89.14 27 | 92.19 26 | 85.58 31 | 84.62 78 | 82.56 84 | 90.53 60 | 71.93 56 | 91.95 10 | 85.89 36 | 94.22 14 | 97.25 22 | 85.42 5 | 95.73 12 | 91.71 39 | 95.08 28 | 91.89 33 |
|
APD-MVS | | | 89.14 27 | 91.25 39 | 86.67 23 | 91.73 14 | 91.02 15 | 95.50 21 | 77.74 23 | 84.04 82 | 79.47 94 | 91.48 49 | 94.85 80 | 81.14 28 | 92.94 39 | 92.20 35 | 94.47 36 | 92.24 28 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
PS-CasMVS | | | 89.07 30 | 93.23 6 | 84.21 46 | 92.44 8 | 88.23 44 | 90.54 59 | 82.95 3 | 90.50 21 | 75.31 111 | 95.80 5 | 98.37 7 | 71.16 106 | 96.30 5 | 93.32 21 | 92.88 54 | 90.11 49 |
|
UA-Net | | | 89.02 31 | 91.44 36 | 86.20 27 | 94.88 1 | 89.84 31 | 94.76 27 | 77.45 27 | 85.41 70 | 74.79 114 | 88.83 87 | 88.90 142 | 78.67 40 | 96.06 7 | 95.45 4 | 96.66 3 | 95.58 2 |
|
LS3D | | | 89.02 31 | 91.69 32 | 85.91 29 | 89.72 41 | 90.81 19 | 92.56 41 | 71.69 57 | 90.83 19 | 87.24 22 | 89.71 74 | 92.07 116 | 78.37 41 | 94.43 28 | 92.59 27 | 95.86 13 | 91.35 40 |
|
DTE-MVSNet | | | 88.99 33 | 92.77 11 | 84.59 40 | 93.31 2 | 88.10 45 | 90.96 49 | 83.09 2 | 91.38 12 | 76.21 104 | 96.03 2 | 98.04 11 | 70.78 112 | 95.65 14 | 92.32 32 | 93.18 49 | 87.84 68 |
|
WR-MVS_H | | | 88.99 33 | 93.28 5 | 83.99 49 | 91.92 11 | 89.13 36 | 91.95 43 | 83.23 1 | 90.14 28 | 71.92 130 | 95.85 4 | 98.01 13 | 71.83 103 | 95.82 9 | 93.19 22 | 93.07 52 | 90.83 45 |
|
ACMH | | 78.40 12 | 88.94 35 | 92.62 14 | 84.65 39 | 86.45 65 | 87.16 54 | 91.47 45 | 68.79 76 | 95.49 3 | 89.74 6 | 93.55 19 | 98.50 3 | 77.96 44 | 94.14 32 | 89.57 56 | 93.49 43 | 89.94 51 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PEN-MVS | | | 88.86 36 | 92.92 8 | 84.11 48 | 92.92 5 | 88.05 47 | 90.83 51 | 82.67 5 | 91.04 16 | 74.83 113 | 95.97 3 | 98.47 4 | 70.38 113 | 95.70 13 | 92.43 30 | 93.05 53 | 88.78 61 |
|
HPM-MVS++ | | | 88.74 37 | 89.54 51 | 87.80 16 | 92.58 7 | 85.69 66 | 95.10 23 | 78.01 21 | 87.08 53 | 87.66 19 | 87.89 92 | 92.07 116 | 80.28 32 | 90.97 67 | 91.41 41 | 93.17 50 | 91.69 34 |
|
CP-MVSNet | | | 88.71 38 | 92.63 13 | 84.13 47 | 92.39 9 | 88.09 46 | 90.47 64 | 82.86 4 | 88.79 39 | 75.16 112 | 94.87 7 | 97.68 17 | 71.05 108 | 96.16 6 | 93.18 23 | 92.85 55 | 89.64 53 |
|
HSP-MVS | | | 88.32 39 | 90.71 43 | 85.53 33 | 90.63 34 | 92.01 4 | 96.15 14 | 77.52 26 | 86.02 63 | 81.39 83 | 90.21 69 | 96.08 46 | 76.38 55 | 88.30 88 | 86.70 80 | 91.12 77 | 95.64 1 |
|
OMC-MVS | | | 88.16 40 | 91.34 38 | 84.46 43 | 86.85 62 | 90.63 22 | 93.01 38 | 67.00 89 | 90.35 25 | 87.40 21 | 86.86 105 | 96.35 37 | 77.66 46 | 92.63 46 | 90.84 42 | 94.84 31 | 91.68 35 |
|
3Dnovator+ | | 83.71 3 | 88.13 41 | 90.00 48 | 85.94 28 | 86.82 63 | 91.06 13 | 94.26 31 | 75.39 41 | 88.85 38 | 85.76 38 | 85.74 115 | 86.92 152 | 78.02 43 | 93.03 38 | 92.21 34 | 95.39 25 | 92.21 30 |
|
CSCG | | | 88.12 42 | 91.45 35 | 84.23 45 | 88.12 56 | 90.59 24 | 90.57 55 | 68.60 78 | 91.37 13 | 83.45 64 | 89.94 71 | 95.14 74 | 78.71 38 | 91.45 55 | 88.21 67 | 95.96 12 | 93.44 16 |
|
RPSCF | | | 88.05 43 | 92.61 15 | 82.73 61 | 84.24 83 | 88.40 40 | 90.04 69 | 66.29 93 | 91.46 11 | 82.29 69 | 88.93 85 | 96.01 49 | 79.38 34 | 95.15 21 | 94.90 6 | 94.15 37 | 93.40 18 |
|
DeepPCF-MVS | | 81.61 6 | 87.95 44 | 90.29 47 | 85.22 37 | 87.48 59 | 90.01 29 | 93.79 32 | 73.54 50 | 88.93 36 | 83.89 53 | 89.40 78 | 90.84 129 | 80.26 33 | 90.62 71 | 90.19 49 | 92.36 62 | 92.03 31 |
|
DeepC-MVS_fast | | 81.78 5 | 87.38 45 | 89.64 49 | 84.75 38 | 89.89 40 | 90.70 21 | 92.74 40 | 74.45 46 | 86.02 63 | 82.16 73 | 86.05 111 | 91.99 119 | 75.84 61 | 91.16 60 | 90.44 45 | 93.41 45 | 91.09 42 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
v7n | | | 87.11 46 | 90.46 46 | 83.19 52 | 85.22 74 | 83.69 75 | 90.03 70 | 68.20 83 | 91.01 17 | 86.71 34 | 94.80 8 | 98.46 5 | 77.69 45 | 91.10 62 | 85.98 85 | 91.30 74 | 88.19 64 |
|
CNVR-MVS | | | 86.93 47 | 88.98 55 | 84.54 41 | 90.11 38 | 87.41 52 | 93.23 37 | 73.47 51 | 86.31 61 | 82.25 70 | 82.96 131 | 92.15 114 | 76.04 58 | 91.69 51 | 90.69 43 | 92.17 64 | 91.64 37 |
|
NCCC | | | 86.74 48 | 87.97 67 | 85.31 35 | 90.64 33 | 87.25 53 | 93.27 36 | 74.59 45 | 86.50 58 | 83.72 55 | 75.92 176 | 92.39 111 | 77.08 50 | 91.72 50 | 90.68 44 | 92.57 60 | 91.30 41 |
|
train_agg | | | 86.67 49 | 87.73 68 | 85.43 34 | 91.51 17 | 82.72 81 | 94.47 29 | 74.22 49 | 81.71 104 | 81.54 82 | 89.20 82 | 92.87 104 | 78.33 42 | 90.12 74 | 88.47 63 | 92.51 61 | 89.04 58 |
|
CDPH-MVS | | | 86.66 50 | 88.52 58 | 84.48 42 | 89.61 42 | 88.27 42 | 92.86 39 | 72.69 54 | 80.55 120 | 82.71 66 | 86.92 104 | 93.32 100 | 75.55 63 | 91.00 65 | 89.85 51 | 93.47 44 | 89.71 52 |
|
Gipuma | | | 86.47 51 | 89.25 53 | 83.23 51 | 83.88 89 | 78.78 123 | 85.35 122 | 68.42 80 | 92.69 9 | 89.03 12 | 91.94 43 | 96.32 40 | 81.80 25 | 94.45 27 | 86.86 76 | 90.91 78 | 83.69 94 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PHI-MVS | | | 86.37 52 | 88.14 64 | 84.30 44 | 86.65 64 | 87.56 50 | 90.76 52 | 70.16 64 | 82.55 92 | 89.65 7 | 84.89 123 | 92.40 110 | 75.97 59 | 90.88 69 | 89.70 53 | 92.58 58 | 89.03 59 |
|
MSLP-MVS++ | | | 86.29 53 | 89.10 54 | 83.01 54 | 85.71 72 | 89.79 32 | 87.04 112 | 74.39 47 | 85.17 72 | 78.92 97 | 77.59 156 | 93.57 96 | 82.60 20 | 93.23 35 | 91.88 38 | 89.42 93 | 92.46 26 |
|
v52 | | | 86.26 54 | 90.85 40 | 80.91 73 | 72.49 188 | 81.25 103 | 90.55 57 | 60.30 170 | 90.43 24 | 87.24 22 | 94.64 11 | 98.30 10 | 83.16 18 | 92.86 42 | 86.82 78 | 91.69 68 | 91.65 36 |
|
V4 | | | 86.26 54 | 90.85 40 | 80.91 73 | 72.49 188 | 81.25 103 | 90.55 57 | 60.31 169 | 90.44 23 | 87.23 24 | 94.64 11 | 98.31 9 | 83.17 16 | 92.87 41 | 86.82 78 | 91.69 68 | 91.64 37 |
|
TAPA-MVS | | 78.00 13 | 85.88 56 | 88.37 60 | 82.96 56 | 84.69 77 | 88.62 39 | 90.62 53 | 64.22 125 | 89.15 35 | 88.05 15 | 78.83 150 | 93.71 93 | 76.20 57 | 90.11 75 | 88.22 66 | 94.00 40 | 89.97 50 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
anonymousdsp | | | 85.62 57 | 90.53 44 | 79.88 93 | 64.64 214 | 76.35 147 | 96.28 13 | 53.53 203 | 85.63 67 | 81.59 81 | 92.81 29 | 97.71 16 | 86.88 2 | 94.56 26 | 92.83 24 | 96.35 6 | 93.84 8 |
|
TSAR-MVS + COLMAP | | | 85.51 58 | 88.36 61 | 82.19 62 | 86.05 69 | 87.69 49 | 90.50 62 | 70.60 63 | 86.40 59 | 82.33 68 | 89.69 75 | 92.52 108 | 74.01 81 | 87.53 92 | 86.84 77 | 89.63 89 | 87.80 69 |
|
CNLPA | | | 85.50 59 | 88.58 56 | 81.91 64 | 84.55 80 | 87.52 51 | 90.89 50 | 63.56 134 | 88.18 43 | 84.06 49 | 83.85 128 | 91.34 126 | 76.46 54 | 91.27 57 | 89.00 61 | 91.96 65 | 88.88 60 |
|
PLC | | 76.06 15 | 85.38 60 | 87.46 70 | 82.95 57 | 85.79 71 | 88.84 37 | 88.86 79 | 68.70 77 | 87.06 54 | 83.60 58 | 79.02 148 | 90.05 134 | 77.37 49 | 90.88 69 | 89.66 54 | 93.37 46 | 86.74 75 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
TSAR-MVS + GP. | | | 85.32 61 | 87.41 72 | 82.89 58 | 90.07 39 | 85.69 66 | 89.07 77 | 72.99 53 | 82.45 94 | 74.52 117 | 85.09 121 | 87.67 149 | 79.24 35 | 91.11 61 | 90.41 46 | 91.45 71 | 89.45 54 |
|
TranMVSNet+NR-MVSNet | | | 85.23 62 | 89.38 52 | 80.39 90 | 88.78 49 | 83.77 74 | 87.40 100 | 76.75 32 | 85.47 68 | 68.99 143 | 95.18 6 | 97.55 19 | 67.13 130 | 91.61 52 | 89.13 60 | 93.26 47 | 82.95 106 |
|
v748 | | | 85.21 63 | 89.62 50 | 80.08 92 | 80.71 131 | 80.27 116 | 85.05 125 | 63.79 132 | 90.47 22 | 83.54 61 | 94.21 15 | 98.52 2 | 76.84 52 | 90.97 67 | 84.25 100 | 90.53 81 | 88.62 62 |
|
Anonymous20231211 | | | 85.16 64 | 91.64 33 | 77.61 116 | 88.54 51 | 79.81 119 | 83.12 133 | 74.68 44 | 98.37 1 | 66.79 155 | 94.56 13 | 99.60 1 | 61.64 149 | 91.49 54 | 89.82 52 | 90.91 78 | 87.80 69 |
|
HQP-MVS | | | 85.02 65 | 86.41 78 | 83.40 50 | 89.19 45 | 86.59 58 | 91.28 46 | 71.60 58 | 82.79 90 | 83.48 63 | 78.65 152 | 93.54 97 | 72.55 98 | 86.49 101 | 85.89 87 | 92.28 63 | 90.95 44 |
|
UniMVSNet (Re) | | | 84.95 66 | 88.53 57 | 80.78 77 | 87.82 58 | 84.21 71 | 88.03 89 | 76.50 35 | 81.18 115 | 69.29 140 | 92.63 34 | 96.83 25 | 69.07 120 | 91.23 59 | 89.60 55 | 93.97 41 | 84.00 92 |
|
DU-MVS | | | 84.88 67 | 88.27 63 | 80.92 72 | 88.30 53 | 83.59 76 | 87.06 110 | 78.35 18 | 80.64 118 | 70.49 136 | 92.67 31 | 96.91 24 | 68.13 124 | 91.79 48 | 89.29 59 | 93.20 48 | 83.02 103 |
|
MCST-MVS | | | 84.79 68 | 86.48 76 | 82.83 59 | 87.30 60 | 87.03 56 | 90.46 65 | 69.33 72 | 83.14 86 | 82.21 72 | 81.69 139 | 92.14 115 | 75.09 67 | 87.27 95 | 84.78 96 | 92.58 58 | 89.30 56 |
|
MVS_0304 | | | 84.73 69 | 86.19 81 | 83.02 53 | 88.32 52 | 86.71 57 | 91.55 44 | 70.87 61 | 73.79 162 | 82.88 65 | 85.13 120 | 93.35 99 | 72.55 98 | 88.62 84 | 87.69 69 | 91.93 66 | 88.05 67 |
|
UniMVSNet_NR-MVSNet | | | 84.62 70 | 88.00 66 | 80.68 82 | 88.18 55 | 83.83 73 | 87.06 110 | 76.47 36 | 81.46 110 | 70.49 136 | 93.24 22 | 95.56 63 | 68.13 124 | 90.43 72 | 88.47 63 | 93.78 42 | 83.02 103 |
|
EG-PatchMatch MVS | | | 84.35 71 | 87.55 69 | 80.62 85 | 86.38 66 | 82.24 86 | 86.75 114 | 64.02 129 | 84.24 78 | 78.17 101 | 89.38 79 | 95.03 77 | 78.78 37 | 89.95 76 | 86.33 82 | 89.59 90 | 85.65 82 |
|
AdaColmap | | | 84.15 72 | 85.14 98 | 83.00 55 | 89.08 46 | 87.14 55 | 90.56 56 | 70.90 60 | 82.40 95 | 80.41 86 | 73.82 188 | 84.69 160 | 75.19 65 | 91.58 53 | 89.90 50 | 91.87 67 | 86.48 76 |
|
PCF-MVS | | 76.59 14 | 84.11 73 | 85.27 95 | 82.76 60 | 86.12 68 | 88.30 41 | 91.24 47 | 69.10 73 | 82.36 96 | 84.45 45 | 77.56 157 | 90.40 133 | 72.91 97 | 85.88 108 | 83.88 103 | 92.72 57 | 88.53 63 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVS_111021_HR | | | 83.95 74 | 86.10 83 | 81.44 69 | 84.62 78 | 80.29 115 | 90.51 61 | 68.05 84 | 84.07 81 | 80.38 87 | 84.74 124 | 91.37 125 | 74.23 75 | 90.37 73 | 87.25 71 | 90.86 80 | 84.59 85 |
|
TinyColmap | | | 83.79 75 | 86.12 82 | 81.07 71 | 83.42 95 | 81.44 98 | 85.42 120 | 68.55 79 | 88.71 40 | 89.46 8 | 87.60 94 | 92.72 105 | 70.34 114 | 89.29 79 | 81.94 124 | 89.20 94 | 81.12 124 |
|
v13 | | | 83.75 76 | 86.20 80 | 80.89 75 | 83.38 96 | 81.93 89 | 88.58 82 | 66.09 96 | 83.55 83 | 84.28 46 | 92.67 31 | 96.79 26 | 74.67 71 | 84.42 130 | 79.72 145 | 88.36 105 | 84.31 88 |
|
v1192 | | | 83.61 77 | 85.23 96 | 81.72 66 | 84.05 85 | 82.15 87 | 89.54 72 | 66.20 94 | 81.38 112 | 86.76 33 | 91.79 46 | 96.03 48 | 74.88 69 | 81.81 160 | 80.92 132 | 88.91 97 | 82.50 111 |
|
v12 | | | 83.59 78 | 86.00 86 | 80.77 80 | 83.30 98 | 81.83 90 | 88.45 83 | 65.95 99 | 83.20 85 | 84.15 47 | 92.54 36 | 96.71 27 | 74.50 73 | 84.19 132 | 79.64 146 | 88.30 106 | 83.93 93 |
|
v1240 | | | 83.57 79 | 84.94 102 | 81.97 63 | 84.05 85 | 81.27 102 | 89.46 74 | 66.06 97 | 81.31 114 | 87.50 20 | 91.88 45 | 95.46 66 | 76.25 56 | 81.16 165 | 80.51 137 | 88.52 103 | 82.98 105 |
|
v1921920 | | | 83.49 80 | 84.94 102 | 81.80 65 | 83.78 90 | 81.20 106 | 89.50 73 | 65.91 100 | 81.64 106 | 87.18 26 | 91.70 47 | 95.39 67 | 75.85 60 | 81.56 163 | 80.27 140 | 88.60 101 | 82.80 107 |
|
v144192 | | | 83.43 81 | 84.97 101 | 81.63 68 | 83.43 94 | 81.23 105 | 89.42 75 | 66.04 98 | 81.45 111 | 86.40 35 | 91.46 51 | 95.70 60 | 75.76 62 | 82.14 156 | 80.23 141 | 88.74 98 | 82.57 110 |
|
V9 | | | 83.42 82 | 85.81 88 | 80.63 84 | 83.20 101 | 81.73 93 | 88.29 87 | 65.78 103 | 82.87 89 | 83.99 52 | 92.38 38 | 96.60 29 | 74.30 74 | 83.93 133 | 79.58 148 | 88.24 109 | 83.55 97 |
|
Vis-MVSNet | | | 83.32 83 | 88.12 65 | 77.71 114 | 77.91 162 | 83.44 78 | 90.58 54 | 69.49 69 | 81.11 116 | 67.10 153 | 89.85 72 | 91.48 124 | 71.71 104 | 91.34 56 | 89.37 57 | 89.48 92 | 90.26 47 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v11 | | | 83.30 84 | 85.58 91 | 80.64 83 | 83.53 93 | 81.74 92 | 88.30 86 | 65.46 108 | 82.75 91 | 84.63 43 | 92.49 37 | 96.17 44 | 73.90 82 | 82.69 149 | 79.59 147 | 88.04 114 | 83.66 95 |
|
V14 | | | 83.23 85 | 85.59 90 | 80.48 88 | 83.09 104 | 81.63 95 | 88.13 88 | 65.61 105 | 82.53 93 | 83.81 54 | 92.17 41 | 96.50 30 | 74.07 79 | 83.66 135 | 79.51 150 | 88.17 111 | 83.16 101 |
|
v1144 | | | 83.22 86 | 85.01 99 | 81.14 70 | 83.76 91 | 81.60 96 | 88.95 78 | 65.58 106 | 81.89 100 | 85.80 37 | 91.68 48 | 95.84 52 | 74.04 80 | 82.12 157 | 80.56 136 | 88.70 100 | 81.41 121 |
|
MVS_111021_LR | | | 83.20 87 | 85.33 93 | 80.73 81 | 82.88 107 | 78.23 127 | 89.61 71 | 65.23 111 | 82.08 99 | 81.19 84 | 85.31 118 | 92.04 118 | 75.22 64 | 89.50 77 | 85.90 86 | 90.24 83 | 84.23 89 |
|
v10 | | | 83.17 88 | 85.22 97 | 80.78 77 | 83.26 100 | 82.99 80 | 88.66 80 | 66.49 92 | 79.24 135 | 83.60 58 | 91.46 51 | 95.47 64 | 74.12 76 | 82.60 151 | 80.66 133 | 88.53 102 | 84.11 91 |
|
v15 | | | 83.06 89 | 85.39 92 | 80.35 91 | 83.01 105 | 81.53 97 | 87.98 91 | 65.47 107 | 82.19 98 | 83.66 57 | 92.00 42 | 96.40 36 | 73.87 83 | 83.39 137 | 79.44 151 | 88.10 113 | 82.76 108 |
|
PVSNet_Blended_VisFu | | | 83.00 90 | 84.16 117 | 81.65 67 | 82.17 121 | 86.01 62 | 88.03 89 | 71.23 59 | 76.05 154 | 79.54 93 | 83.88 127 | 83.44 161 | 77.49 48 | 87.38 93 | 84.93 95 | 91.41 72 | 87.40 73 |
|
NR-MVSNet | | | 82.89 91 | 87.43 71 | 77.59 117 | 83.91 88 | 83.59 76 | 87.10 109 | 78.35 18 | 80.64 118 | 68.85 144 | 92.67 31 | 96.50 30 | 54.19 179 | 87.19 98 | 88.68 62 | 93.16 51 | 82.75 109 |
|
CANet | | | 82.84 92 | 84.60 106 | 80.78 77 | 87.30 60 | 85.20 68 | 90.23 67 | 69.00 74 | 72.16 170 | 78.73 98 | 84.49 125 | 90.70 131 | 69.54 118 | 87.65 91 | 86.17 83 | 89.87 87 | 85.84 81 |
|
Baseline_NR-MVSNet | | | 82.79 93 | 86.51 75 | 78.44 111 | 88.30 53 | 75.62 158 | 87.81 92 | 74.97 43 | 81.53 108 | 66.84 154 | 94.71 10 | 96.46 32 | 66.90 131 | 91.79 48 | 83.37 111 | 85.83 160 | 82.09 116 |
|
v7 | | | 82.76 94 | 84.65 105 | 80.55 86 | 83.27 99 | 81.77 91 | 88.66 80 | 65.10 112 | 79.23 136 | 83.60 58 | 91.47 50 | 95.47 64 | 74.12 76 | 82.61 150 | 80.66 133 | 88.52 103 | 81.35 122 |
|
EPP-MVSNet | | | 82.76 94 | 86.47 77 | 78.45 110 | 86.00 70 | 84.47 70 | 85.39 121 | 68.42 80 | 84.17 79 | 62.97 164 | 89.26 81 | 76.84 185 | 72.13 101 | 92.56 47 | 90.40 47 | 95.76 20 | 87.56 72 |
|
CLD-MVS | | | 82.75 96 | 87.22 73 | 77.54 118 | 88.01 57 | 85.76 65 | 90.23 67 | 54.52 196 | 82.28 97 | 82.11 74 | 88.48 88 | 95.27 68 | 63.95 140 | 89.41 78 | 88.29 65 | 86.45 148 | 81.01 125 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
Effi-MVS+ | | | 82.33 97 | 83.87 123 | 80.52 87 | 84.51 81 | 81.32 99 | 87.53 98 | 68.05 84 | 74.94 159 | 79.67 92 | 82.37 135 | 92.31 112 | 72.21 100 | 85.06 117 | 86.91 75 | 91.18 75 | 84.20 90 |
|
v1 | | | 82.27 98 | 84.32 110 | 79.87 94 | 82.86 108 | 80.32 112 | 87.57 97 | 63.47 138 | 81.87 102 | 84.13 48 | 91.34 53 | 96.29 41 | 73.23 93 | 82.39 152 | 79.08 162 | 87.94 116 | 78.98 139 |
|
v1141 | | | 82.26 99 | 84.32 110 | 79.85 95 | 82.86 108 | 80.31 113 | 87.58 95 | 63.48 136 | 81.86 103 | 84.03 51 | 91.33 54 | 96.28 42 | 73.23 93 | 82.39 152 | 79.08 162 | 87.93 117 | 78.97 140 |
|
divwei89l23v2f112 | | | 82.26 99 | 84.32 110 | 79.85 95 | 82.86 108 | 80.31 113 | 87.58 95 | 63.48 136 | 81.88 101 | 84.05 50 | 91.33 54 | 96.27 43 | 73.23 93 | 82.39 152 | 79.08 162 | 87.93 117 | 78.97 140 |
|
3Dnovator | | 79.41 10 | 82.21 101 | 86.07 84 | 77.71 114 | 79.31 146 | 84.61 69 | 87.18 107 | 61.02 166 | 85.65 66 | 76.11 105 | 85.07 122 | 85.38 158 | 70.96 110 | 87.22 96 | 86.47 81 | 91.66 70 | 88.12 66 |
|
v8 | | | 82.20 102 | 84.56 107 | 79.45 98 | 82.42 112 | 81.65 94 | 87.26 101 | 64.27 123 | 79.36 131 | 81.70 77 | 91.04 63 | 95.75 55 | 73.30 91 | 82.82 145 | 79.18 159 | 87.74 121 | 82.09 116 |
|
v2v482 | | | 82.20 102 | 84.26 114 | 79.81 97 | 82.67 111 | 80.18 117 | 87.67 94 | 63.96 131 | 81.69 105 | 84.73 42 | 91.27 57 | 96.33 39 | 72.05 102 | 81.94 159 | 79.56 149 | 87.79 120 | 78.84 142 |
|
v17 | | | 82.09 104 | 84.45 108 | 79.33 100 | 82.41 113 | 81.31 100 | 87.26 101 | 64.50 122 | 78.72 138 | 80.73 85 | 90.90 64 | 95.57 61 | 73.37 87 | 83.06 138 | 79.25 155 | 87.70 125 | 82.35 114 |
|
Effi-MVS+-dtu | | | 82.04 105 | 83.39 129 | 80.48 88 | 85.48 73 | 86.57 60 | 88.40 84 | 68.28 82 | 69.04 182 | 73.13 124 | 76.26 168 | 91.11 128 | 74.74 70 | 88.40 86 | 87.76 68 | 92.84 56 | 84.57 86 |
|
MAR-MVS | | | 81.98 106 | 82.92 131 | 80.88 76 | 85.18 75 | 85.85 63 | 89.13 76 | 69.52 67 | 71.21 174 | 82.25 70 | 71.28 198 | 88.89 143 | 69.69 115 | 88.71 83 | 86.96 73 | 89.52 91 | 87.57 71 |
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 |
v16 | | | 81.92 107 | 84.32 110 | 79.12 106 | 82.31 118 | 81.29 101 | 87.20 106 | 64.51 121 | 78.16 142 | 79.76 91 | 90.86 65 | 95.23 70 | 73.29 92 | 83.05 139 | 79.29 154 | 87.63 126 | 82.34 115 |
|
v6 | | | 81.77 108 | 83.96 120 | 79.22 103 | 82.41 113 | 80.45 111 | 87.26 101 | 62.91 148 | 79.29 132 | 81.65 78 | 91.08 59 | 95.74 56 | 73.32 88 | 82.84 142 | 79.21 158 | 87.73 122 | 79.07 136 |
|
v1neww | | | 81.76 109 | 83.95 121 | 79.21 104 | 82.41 113 | 80.46 109 | 87.26 101 | 62.93 144 | 79.28 133 | 81.62 79 | 91.06 61 | 95.72 58 | 73.31 89 | 82.83 143 | 79.22 156 | 87.73 122 | 79.07 136 |
|
v7new | | | 81.76 109 | 83.95 121 | 79.21 104 | 82.41 113 | 80.46 109 | 87.26 101 | 62.93 144 | 79.28 133 | 81.62 79 | 91.06 61 | 95.72 58 | 73.31 89 | 82.83 143 | 79.22 156 | 87.73 122 | 79.07 136 |
|
IS_MVSNet | | | 81.72 111 | 85.01 99 | 77.90 113 | 86.19 67 | 82.64 83 | 85.56 119 | 70.02 65 | 80.11 124 | 63.52 161 | 87.28 98 | 81.18 170 | 67.26 128 | 91.08 64 | 89.33 58 | 94.82 32 | 83.42 99 |
|
v18 | | | 81.62 112 | 83.99 119 | 78.86 107 | 82.08 122 | 81.12 107 | 86.93 113 | 64.24 124 | 77.44 144 | 79.47 94 | 90.53 66 | 94.99 78 | 72.99 96 | 82.72 148 | 79.18 159 | 87.48 129 | 81.91 119 |
|
FPMVS | | | 81.56 113 | 84.04 118 | 78.66 108 | 82.92 106 | 75.96 153 | 86.48 117 | 65.66 104 | 84.67 75 | 71.47 132 | 77.78 154 | 83.22 163 | 77.57 47 | 91.24 58 | 90.21 48 | 87.84 119 | 85.21 83 |
|
Fast-Effi-MVS+ | | | 81.42 114 | 83.82 124 | 78.62 109 | 82.24 120 | 80.62 108 | 87.72 93 | 63.51 135 | 73.01 163 | 74.75 115 | 83.80 129 | 92.70 106 | 73.44 86 | 88.15 90 | 85.26 91 | 90.05 84 | 83.17 100 |
|
USDC | | | 81.39 115 | 83.07 130 | 79.43 99 | 81.48 127 | 78.95 122 | 82.62 137 | 66.17 95 | 87.45 50 | 90.73 4 | 82.40 134 | 93.65 95 | 66.57 133 | 83.63 136 | 77.97 166 | 89.00 96 | 77.45 149 |
|
MSDG | | | 81.39 115 | 84.23 116 | 78.09 112 | 82.40 117 | 82.47 85 | 85.31 124 | 60.91 167 | 79.73 127 | 80.26 89 | 86.30 108 | 88.27 147 | 69.67 116 | 87.20 97 | 84.98 94 | 89.97 86 | 80.67 126 |
|
canonicalmvs | | | 81.22 117 | 86.04 85 | 75.60 125 | 83.17 103 | 83.18 79 | 80.29 148 | 65.82 102 | 85.97 65 | 67.98 151 | 77.74 155 | 91.51 123 | 65.17 136 | 88.62 84 | 86.15 84 | 91.17 76 | 89.09 57 |
|
pmmvs6 | | | 80.46 118 | 88.34 62 | 71.26 142 | 81.96 123 | 77.51 132 | 77.54 166 | 68.83 75 | 93.72 6 | 55.92 176 | 93.94 18 | 98.03 12 | 55.94 169 | 89.21 80 | 85.61 88 | 87.36 133 | 80.38 127 |
|
QAPM | | | 80.43 119 | 84.34 109 | 75.86 123 | 79.40 145 | 82.06 88 | 79.86 153 | 61.94 161 | 83.28 84 | 74.73 116 | 81.74 138 | 85.44 157 | 70.97 109 | 84.99 124 | 84.71 97 | 88.29 107 | 88.14 65 |
|
PM-MVS | | | 80.42 120 | 83.63 126 | 76.67 120 | 78.04 157 | 72.37 173 | 87.14 108 | 60.18 172 | 80.13 123 | 71.75 131 | 86.12 110 | 93.92 92 | 77.08 50 | 86.56 100 | 85.12 93 | 85.83 160 | 81.18 123 |
|
IterMVS-LS | | | 79.79 121 | 82.56 133 | 76.56 122 | 81.83 125 | 77.85 130 | 79.90 152 | 69.42 71 | 78.93 137 | 71.21 133 | 90.47 67 | 85.20 159 | 70.86 111 | 80.54 170 | 80.57 135 | 86.15 151 | 84.36 87 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DELS-MVS | | | 79.71 122 | 83.74 125 | 75.01 128 | 79.31 146 | 82.68 82 | 84.79 127 | 60.06 173 | 75.43 157 | 69.09 142 | 86.13 109 | 89.38 136 | 67.16 129 | 85.12 116 | 83.87 104 | 89.65 88 | 83.57 96 |
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 |
pmmvs-eth3d | | | 79.64 123 | 82.06 136 | 76.83 119 | 80.05 137 | 72.64 171 | 87.47 99 | 66.59 91 | 80.83 117 | 73.50 121 | 89.32 80 | 93.20 101 | 67.78 126 | 80.78 168 | 81.64 126 | 85.58 163 | 76.01 151 |
|
UGNet | | | 79.62 124 | 85.91 87 | 72.28 140 | 73.52 182 | 83.91 72 | 86.64 115 | 69.51 68 | 79.85 126 | 62.57 166 | 85.82 114 | 89.63 135 | 53.18 186 | 88.39 87 | 87.35 70 | 88.28 108 | 86.43 77 |
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 |
V42 | | | 79.59 125 | 83.59 127 | 74.93 130 | 69.61 200 | 77.05 140 | 86.59 116 | 55.84 192 | 78.42 141 | 77.29 102 | 89.84 73 | 95.08 75 | 74.12 76 | 83.05 139 | 80.11 143 | 86.12 152 | 81.59 120 |
|
EPNet | | | 79.36 126 | 79.44 143 | 79.27 102 | 89.51 43 | 77.20 137 | 88.35 85 | 77.35 31 | 68.27 184 | 74.29 118 | 76.31 166 | 79.22 174 | 59.63 154 | 85.02 123 | 85.45 90 | 86.49 147 | 84.61 84 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v148 | | | 79.33 127 | 82.32 135 | 75.84 124 | 80.14 136 | 75.74 155 | 81.98 140 | 57.06 188 | 81.51 109 | 79.36 96 | 89.42 77 | 96.42 34 | 71.32 105 | 81.54 164 | 75.29 180 | 85.20 166 | 76.32 150 |
|
FC-MVSNet-train | | | 79.20 128 | 86.29 79 | 70.94 147 | 84.06 84 | 77.67 131 | 85.68 118 | 64.11 128 | 82.90 88 | 52.22 195 | 92.57 35 | 93.69 94 | 49.52 203 | 88.30 88 | 86.93 74 | 90.03 85 | 81.95 118 |
|
TransMVSNet (Re) | | | 79.05 129 | 86.66 74 | 70.18 155 | 83.32 97 | 75.99 152 | 77.54 166 | 63.98 130 | 90.68 20 | 55.84 177 | 94.80 8 | 96.06 47 | 53.73 185 | 86.27 104 | 83.22 112 | 86.65 142 | 79.61 134 |
|
no-one | | | 78.59 130 | 85.28 94 | 70.79 148 | 59.01 221 | 68.77 186 | 76.62 176 | 46.06 214 | 80.25 122 | 75.75 108 | 81.85 137 | 97.75 15 | 83.63 12 | 90.99 66 | 87.20 72 | 83.67 173 | 90.14 48 |
|
OpenMVS | | 75.38 16 | 78.44 131 | 81.39 138 | 74.99 129 | 80.46 133 | 79.85 118 | 79.99 150 | 58.31 183 | 77.34 146 | 73.85 120 | 77.19 161 | 82.33 168 | 68.60 123 | 84.67 127 | 81.95 123 | 88.72 99 | 86.40 78 |
|
pm-mvs1 | | | 78.21 132 | 85.68 89 | 69.50 161 | 80.38 134 | 75.73 156 | 76.25 181 | 65.04 113 | 87.59 47 | 54.47 182 | 93.16 24 | 95.99 51 | 54.20 178 | 86.37 102 | 82.98 114 | 86.64 143 | 77.96 147 |
|
FMVSNet1 | | | 78.20 133 | 84.83 104 | 70.46 152 | 78.62 152 | 79.03 121 | 77.90 165 | 67.53 88 | 83.02 87 | 55.10 179 | 87.19 100 | 93.18 102 | 55.65 171 | 85.57 109 | 83.39 108 | 87.98 115 | 82.40 112 |
|
DI_MVS_plusplus_trai | | | 77.64 134 | 79.64 142 | 75.31 127 | 79.87 141 | 76.89 141 | 81.55 143 | 63.64 133 | 76.21 153 | 72.03 129 | 85.59 117 | 82.97 164 | 66.63 132 | 79.27 173 | 77.78 168 | 88.14 112 | 78.76 143 |
|
tfpnnormal | | | 77.16 135 | 84.26 114 | 68.88 164 | 81.02 130 | 75.02 159 | 76.52 178 | 63.30 140 | 87.29 51 | 52.40 193 | 91.24 58 | 93.97 91 | 54.85 177 | 85.46 112 | 81.08 130 | 85.18 167 | 75.76 154 |
|
conf0.05thres1000 | | | 77.12 136 | 82.38 134 | 70.98 145 | 82.30 119 | 77.95 129 | 79.86 153 | 64.74 117 | 86.63 57 | 53.93 183 | 85.74 115 | 75.63 195 | 56.85 163 | 88.98 82 | 84.10 101 | 88.20 110 | 77.61 148 |
|
Fast-Effi-MVS+-dtu | | | 76.92 137 | 77.18 158 | 76.62 121 | 79.55 143 | 79.17 120 | 84.80 126 | 77.40 29 | 64.46 204 | 68.75 146 | 70.81 204 | 86.57 153 | 63.36 146 | 81.74 161 | 81.76 125 | 85.86 159 | 75.78 153 |
|
MVS_Test | | | 76.72 138 | 79.40 144 | 73.60 134 | 78.85 151 | 74.99 160 | 79.91 151 | 61.56 163 | 69.67 178 | 72.44 125 | 85.98 112 | 90.78 130 | 63.50 144 | 78.30 176 | 75.74 179 | 85.33 165 | 80.31 131 |
|
MDA-MVSNet-bldmvs | | | 76.51 139 | 82.87 132 | 69.09 163 | 50.71 232 | 74.72 163 | 84.05 131 | 60.27 171 | 81.62 107 | 71.16 134 | 88.21 90 | 91.58 121 | 69.62 117 | 92.78 43 | 77.48 171 | 78.75 187 | 73.69 167 |
|
EU-MVSNet | | | 76.48 140 | 80.53 140 | 71.75 141 | 67.62 205 | 70.30 177 | 81.74 141 | 54.06 199 | 75.47 156 | 71.01 135 | 80.10 143 | 93.17 103 | 73.67 84 | 83.73 134 | 77.85 167 | 82.40 179 | 83.07 102 |
|
PVSNet_BlendedMVS | | | 76.45 141 | 78.12 149 | 74.49 131 | 76.76 171 | 78.46 124 | 79.65 155 | 63.26 141 | 65.42 200 | 73.15 122 | 75.05 182 | 88.96 140 | 66.51 134 | 82.73 146 | 77.66 169 | 87.61 127 | 78.60 144 |
|
PVSNet_Blended | | | 76.45 141 | 78.12 149 | 74.49 131 | 76.76 171 | 78.46 124 | 79.65 155 | 63.26 141 | 65.42 200 | 73.15 122 | 75.05 182 | 88.96 140 | 66.51 134 | 82.73 146 | 77.66 169 | 87.61 127 | 78.60 144 |
|
Vis-MVSNet (Re-imp) | | | 76.15 143 | 80.84 139 | 70.68 149 | 83.66 92 | 74.80 162 | 81.66 142 | 69.59 66 | 80.48 121 | 46.94 211 | 87.44 95 | 80.63 172 | 53.14 187 | 86.87 99 | 84.56 98 | 89.12 95 | 71.12 176 |
|
PatchMatch-RL | | | 76.05 144 | 76.64 164 | 75.36 126 | 77.84 163 | 69.87 180 | 81.09 145 | 63.43 139 | 71.66 172 | 68.34 149 | 71.70 194 | 81.76 169 | 74.98 68 | 84.83 126 | 83.44 107 | 86.45 148 | 73.22 169 |
|
pmmvs4 | | | 75.92 145 | 77.48 156 | 74.10 133 | 78.21 156 | 70.94 175 | 84.06 130 | 64.78 116 | 75.13 158 | 68.47 148 | 84.12 126 | 83.32 162 | 64.74 139 | 75.93 187 | 79.14 161 | 84.31 171 | 73.77 165 |
|
FC-MVSNet-test | | | 75.91 146 | 83.59 127 | 66.95 182 | 76.63 177 | 69.07 183 | 85.33 123 | 64.97 115 | 84.87 74 | 41.95 217 | 93.17 23 | 87.04 151 | 47.78 206 | 91.09 63 | 85.56 89 | 85.06 168 | 74.34 157 |
|
CVMVSNet | | | 75.65 147 | 77.62 155 | 73.35 137 | 71.95 192 | 69.89 179 | 83.04 136 | 60.84 168 | 69.12 180 | 68.76 145 | 79.92 146 | 78.93 176 | 73.64 85 | 81.02 166 | 81.01 131 | 81.86 181 | 83.43 98 |
|
IB-MVS | | 71.28 17 | 75.21 148 | 77.00 161 | 73.12 138 | 76.76 171 | 77.45 133 | 83.05 135 | 58.92 179 | 63.01 209 | 64.31 160 | 59.99 227 | 87.57 150 | 68.64 122 | 86.26 105 | 82.34 122 | 87.05 140 | 82.36 113 |
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 |
CANet_DTU | | | 75.04 149 | 78.45 146 | 71.07 143 | 77.27 167 | 77.96 128 | 83.88 132 | 58.00 184 | 64.11 205 | 68.67 147 | 75.65 178 | 88.37 146 | 53.92 181 | 82.05 158 | 81.11 129 | 84.67 169 | 79.88 133 |
|
GA-MVS | | | 75.01 150 | 76.39 166 | 73.39 135 | 78.37 153 | 75.66 157 | 80.03 149 | 58.40 182 | 70.51 176 | 75.85 107 | 83.24 130 | 76.14 189 | 63.75 141 | 77.28 180 | 76.62 175 | 83.97 172 | 75.30 156 |
|
view800 | | | 74.68 151 | 78.74 145 | 69.94 156 | 81.12 129 | 76.59 142 | 78.94 162 | 63.24 143 | 78.56 140 | 53.06 188 | 75.61 179 | 76.26 188 | 56.07 168 | 86.32 103 | 83.75 106 | 87.18 139 | 74.10 161 |
|
FMVSNet2 | | | 74.43 152 | 79.70 141 | 68.27 167 | 76.76 171 | 77.36 134 | 75.77 186 | 65.36 110 | 72.28 168 | 52.97 189 | 81.92 136 | 85.61 156 | 52.73 190 | 80.66 169 | 79.73 144 | 86.04 155 | 80.37 128 |
|
thres600view7 | | | 74.34 153 | 78.43 147 | 69.56 160 | 80.47 132 | 76.28 148 | 78.65 163 | 62.56 153 | 77.39 145 | 52.53 191 | 74.03 187 | 76.78 186 | 55.90 170 | 85.06 117 | 85.19 92 | 87.25 137 | 74.29 159 |
|
view600 | | | 74.08 154 | 78.15 148 | 69.32 162 | 80.27 135 | 75.82 154 | 78.27 164 | 62.20 157 | 77.26 147 | 52.80 190 | 74.07 186 | 76.86 184 | 55.57 173 | 84.90 125 | 84.43 99 | 86.84 141 | 73.71 166 |
|
diffmvs | | | 73.65 155 | 77.10 159 | 69.63 159 | 73.21 183 | 69.52 181 | 79.35 159 | 57.48 185 | 73.80 161 | 68.08 150 | 87.10 101 | 82.39 166 | 61.36 150 | 74.27 190 | 74.51 181 | 78.31 188 | 78.14 146 |
|
IterMVS | | | 73.62 156 | 76.53 165 | 70.23 154 | 71.83 193 | 77.18 138 | 80.69 147 | 53.22 204 | 72.23 169 | 66.62 156 | 85.21 119 | 78.96 175 | 69.54 118 | 76.28 186 | 71.63 189 | 79.45 184 | 74.25 160 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MIMVSNet1 | | | 73.40 157 | 81.85 137 | 63.55 196 | 72.90 185 | 64.37 198 | 84.58 128 | 53.60 202 | 90.84 18 | 53.92 184 | 87.75 93 | 96.10 45 | 45.31 209 | 85.37 114 | 79.32 153 | 70.98 203 | 69.18 186 |
|
HyFIR lowres test | | | 73.29 158 | 74.14 179 | 72.30 139 | 73.08 184 | 78.33 126 | 83.12 133 | 62.41 156 | 63.81 206 | 62.13 167 | 76.67 165 | 78.50 177 | 71.09 107 | 74.13 191 | 77.47 172 | 81.98 180 | 70.10 180 |
|
tfpn_n400 | | | 73.26 159 | 77.94 151 | 67.79 177 | 79.91 139 | 73.32 166 | 76.38 179 | 62.04 158 | 84.26 76 | 48.53 207 | 76.23 169 | 71.50 202 | 53.83 182 | 86.22 106 | 81.59 127 | 86.05 153 | 72.47 171 |
|
tfpnconf | | | 73.26 159 | 77.94 151 | 67.79 177 | 79.91 139 | 73.32 166 | 76.38 179 | 62.04 158 | 84.26 76 | 48.53 207 | 76.23 169 | 71.50 202 | 53.83 182 | 86.22 106 | 81.59 127 | 86.05 153 | 72.47 171 |
|
GBi-Net | | | 73.17 161 | 77.64 153 | 67.95 172 | 76.76 171 | 77.36 134 | 75.77 186 | 64.57 118 | 62.99 210 | 51.83 196 | 76.05 172 | 77.76 180 | 52.73 190 | 85.57 109 | 83.39 108 | 86.04 155 | 80.37 128 |
|
test1 | | | 73.17 161 | 77.64 153 | 67.95 172 | 76.76 171 | 77.36 134 | 75.77 186 | 64.57 118 | 62.99 210 | 51.83 196 | 76.05 172 | 77.76 180 | 52.73 190 | 85.57 109 | 83.39 108 | 86.04 155 | 80.37 128 |
|
thres400 | | | 73.13 163 | 76.99 162 | 68.62 165 | 79.46 144 | 74.93 161 | 77.23 168 | 61.23 164 | 75.54 155 | 52.31 194 | 72.20 193 | 77.10 183 | 54.89 175 | 82.92 141 | 82.62 121 | 86.57 145 | 73.66 168 |
|
CDS-MVSNet | | | 73.07 164 | 77.02 160 | 68.46 166 | 81.62 126 | 72.89 170 | 79.56 157 | 70.78 62 | 69.56 179 | 52.52 192 | 77.37 160 | 81.12 171 | 42.60 212 | 84.20 131 | 83.93 102 | 83.65 174 | 70.07 181 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
tfpn | | | 72.99 165 | 75.25 174 | 70.36 153 | 81.87 124 | 77.09 139 | 79.28 160 | 64.16 126 | 79.58 129 | 53.14 187 | 76.97 163 | 48.75 229 | 56.35 167 | 87.31 94 | 82.75 116 | 87.35 134 | 74.31 158 |
|
MDTV_nov1_ep13_2view | | | 72.96 166 | 75.59 171 | 69.88 157 | 71.15 197 | 64.86 197 | 82.31 139 | 54.45 197 | 76.30 152 | 78.32 100 | 86.52 106 | 91.58 121 | 61.35 151 | 76.80 181 | 66.83 201 | 71.70 198 | 66.26 193 |
|
tfpnview11 | | | 72.88 167 | 77.37 157 | 67.65 179 | 79.81 142 | 73.43 165 | 76.23 182 | 61.97 160 | 81.37 113 | 48.53 207 | 76.23 169 | 71.50 202 | 53.78 184 | 85.45 113 | 82.77 115 | 85.56 164 | 70.87 179 |
|
gg-mvs-nofinetune | | | 72.68 168 | 75.21 175 | 69.73 158 | 81.48 127 | 69.04 184 | 70.48 205 | 76.67 33 | 86.92 55 | 67.80 152 | 88.06 91 | 64.67 210 | 42.12 214 | 77.60 178 | 73.65 183 | 79.81 183 | 66.57 192 |
|
thres200 | | | 72.41 169 | 76.00 170 | 68.21 168 | 78.28 154 | 76.28 148 | 74.94 191 | 62.56 153 | 72.14 171 | 51.35 199 | 69.59 209 | 76.51 187 | 54.89 175 | 85.06 117 | 80.51 137 | 87.25 137 | 71.92 174 |
|
tfpn1000 | | | 72.27 170 | 76.88 163 | 66.88 183 | 79.01 150 | 74.04 164 | 76.60 177 | 61.15 165 | 79.65 128 | 45.52 213 | 77.41 159 | 67.98 208 | 52.47 193 | 85.22 115 | 82.99 113 | 86.54 146 | 70.89 177 |
|
tfpn200view9 | | | 72.01 171 | 75.40 172 | 68.06 169 | 77.97 158 | 76.44 144 | 77.04 170 | 62.67 151 | 66.81 190 | 50.82 200 | 67.30 211 | 75.67 191 | 52.46 194 | 85.06 117 | 82.64 117 | 87.41 132 | 73.86 164 |
|
conf200view11 | | | 72.00 172 | 75.40 172 | 68.04 170 | 77.97 158 | 76.44 144 | 77.04 170 | 62.68 149 | 66.81 190 | 50.69 202 | 67.30 211 | 75.67 191 | 52.46 194 | 85.06 117 | 82.64 117 | 87.42 130 | 73.87 162 |
|
EPNet_dtu | | | 71.90 173 | 73.03 183 | 70.59 150 | 78.28 154 | 61.64 203 | 82.44 138 | 64.12 127 | 63.26 208 | 69.74 138 | 71.47 196 | 82.41 165 | 51.89 199 | 78.83 175 | 78.01 165 | 77.07 189 | 75.60 155 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
tfpn111 | | | 71.60 174 | 74.66 177 | 68.04 170 | 77.97 158 | 76.44 144 | 77.04 170 | 62.68 149 | 66.81 190 | 50.69 202 | 62.10 222 | 75.67 191 | 52.46 194 | 85.06 117 | 82.64 117 | 87.42 130 | 73.87 162 |
|
gm-plane-assit | | | 71.56 175 | 69.99 188 | 73.39 135 | 84.43 82 | 73.21 169 | 90.42 66 | 51.36 210 | 84.08 80 | 76.00 106 | 91.30 56 | 37.09 236 | 59.01 157 | 73.65 196 | 70.24 193 | 79.09 186 | 60.37 209 |
|
CMPMVS | | 55.74 18 | 71.56 175 | 76.26 167 | 66.08 189 | 68.11 204 | 63.91 200 | 63.17 225 | 50.52 212 | 68.79 183 | 75.49 109 | 70.78 205 | 85.67 155 | 63.54 143 | 81.58 162 | 77.20 173 | 75.63 190 | 85.86 80 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
FMVSNet3 | | | 71.40 177 | 75.20 176 | 66.97 181 | 75.00 179 | 76.59 142 | 74.29 192 | 64.57 118 | 62.99 210 | 51.83 196 | 76.05 172 | 77.76 180 | 51.49 200 | 76.58 184 | 77.03 174 | 84.62 170 | 79.43 135 |
|
MS-PatchMatch | | | 71.18 178 | 73.99 180 | 67.89 176 | 77.16 168 | 71.76 174 | 77.18 169 | 56.38 191 | 67.35 186 | 55.04 180 | 74.63 184 | 75.70 190 | 62.38 148 | 76.62 183 | 75.97 178 | 79.22 185 | 75.90 152 |
|
test20.03 | | | 69.91 179 | 76.20 168 | 62.58 198 | 84.01 87 | 67.34 190 | 75.67 190 | 65.88 101 | 79.98 125 | 40.28 222 | 82.65 132 | 89.31 138 | 39.63 216 | 77.41 179 | 73.28 184 | 69.98 204 | 63.40 201 |
|
thres100view900 | | | 69.86 180 | 72.97 184 | 66.24 186 | 77.97 158 | 72.49 172 | 73.29 196 | 59.12 177 | 66.81 190 | 50.82 200 | 67.30 211 | 75.67 191 | 50.54 202 | 78.24 177 | 79.40 152 | 85.71 162 | 70.88 178 |
|
conf0.01 | | | 69.59 181 | 71.01 187 | 67.95 172 | 77.74 164 | 76.09 150 | 77.04 170 | 62.58 152 | 66.81 190 | 50.54 204 | 63.00 220 | 51.78 228 | 52.46 194 | 84.53 128 | 82.64 117 | 87.32 135 | 72.19 173 |
|
CR-MVSNet | | | 69.56 182 | 68.34 197 | 70.99 144 | 72.78 187 | 67.63 188 | 64.47 222 | 67.74 86 | 59.93 219 | 72.30 126 | 80.10 143 | 56.77 219 | 65.04 137 | 71.64 204 | 72.91 185 | 83.61 177 | 69.40 184 |
|
pmmvs5 | | | 68.91 183 | 74.35 178 | 62.56 199 | 67.45 207 | 66.78 192 | 71.70 201 | 51.47 209 | 67.17 189 | 56.25 175 | 82.41 133 | 88.59 144 | 47.21 207 | 73.21 200 | 74.23 182 | 81.30 182 | 68.03 189 |
|
CHOSEN 1792x2688 | | | 68.80 184 | 71.09 186 | 66.13 188 | 69.11 202 | 68.89 185 | 78.98 161 | 54.68 194 | 61.63 216 | 56.69 173 | 71.56 195 | 78.39 178 | 67.69 127 | 72.13 202 | 72.01 188 | 69.63 206 | 73.02 170 |
|
conf0.002 | | | 68.60 185 | 69.17 192 | 67.92 175 | 77.66 165 | 76.01 151 | 77.04 170 | 62.56 153 | 66.81 190 | 50.51 205 | 61.21 225 | 44.01 233 | 52.46 194 | 84.44 129 | 80.29 139 | 87.31 136 | 71.44 175 |
|
tpmp4_e23 | | | 68.32 186 | 66.04 201 | 70.98 145 | 77.52 166 | 69.23 182 | 80.99 146 | 65.46 108 | 68.09 185 | 69.25 141 | 70.77 206 | 54.03 225 | 59.35 155 | 69.01 211 | 63.02 208 | 73.34 195 | 68.15 188 |
|
tfpn_ndepth | | | 68.20 187 | 72.18 185 | 63.55 196 | 74.64 180 | 73.24 168 | 72.41 199 | 59.76 175 | 70.54 175 | 41.93 218 | 60.96 226 | 68.69 207 | 46.23 208 | 82.16 155 | 80.14 142 | 86.34 150 | 69.56 183 |
|
testgi | | | 68.20 187 | 76.05 169 | 59.04 205 | 79.99 138 | 67.32 191 | 81.16 144 | 51.78 208 | 84.91 73 | 39.36 225 | 73.42 189 | 95.19 71 | 32.79 222 | 76.54 185 | 70.40 192 | 69.14 207 | 64.55 197 |
|
MVSTER | | | 68.08 189 | 69.73 190 | 66.16 187 | 66.33 212 | 70.06 178 | 75.71 189 | 52.36 206 | 55.18 228 | 58.64 170 | 70.23 208 | 56.72 220 | 57.34 162 | 79.68 172 | 76.03 177 | 86.61 144 | 80.20 132 |
|
Anonymous20231206 | | | 67.28 190 | 73.41 182 | 60.12 204 | 76.45 178 | 63.61 201 | 74.21 193 | 56.52 190 | 76.35 151 | 42.23 216 | 75.81 177 | 90.47 132 | 41.51 215 | 74.52 188 | 69.97 194 | 69.83 205 | 63.17 202 |
|
RPMNet | | | 67.02 191 | 63.99 209 | 70.56 151 | 71.55 195 | 67.63 188 | 75.81 184 | 69.44 70 | 59.93 219 | 63.24 162 | 64.32 216 | 47.51 230 | 59.68 153 | 70.37 208 | 69.64 195 | 83.64 175 | 68.49 187 |
|
CostFormer | | | 66.81 192 | 66.94 199 | 66.67 184 | 72.79 186 | 68.25 187 | 79.55 158 | 55.57 193 | 65.52 199 | 62.77 165 | 76.98 162 | 60.09 214 | 56.73 165 | 65.69 221 | 62.35 209 | 72.59 196 | 69.71 182 |
|
thresconf0.02 | | | 66.71 193 | 68.28 198 | 64.89 195 | 76.83 170 | 70.38 176 | 71.62 203 | 58.90 180 | 77.64 143 | 47.04 210 | 62.10 222 | 46.01 231 | 51.32 201 | 78.85 174 | 76.09 176 | 83.62 176 | 66.85 191 |
|
PatchT | | | 66.25 194 | 66.76 200 | 65.67 192 | 55.87 226 | 60.75 205 | 70.17 206 | 59.00 178 | 59.80 221 | 72.30 126 | 78.68 151 | 54.12 224 | 65.04 137 | 71.64 204 | 72.91 185 | 71.63 200 | 69.40 184 |
|
LP | | | 65.71 195 | 69.91 189 | 60.81 203 | 56.75 225 | 61.37 204 | 69.55 212 | 56.80 189 | 73.01 163 | 60.48 169 | 79.76 147 | 70.57 205 | 55.47 174 | 72.77 201 | 67.19 200 | 65.81 213 | 64.71 196 |
|
dps | | | 65.14 196 | 64.50 207 | 65.89 191 | 71.41 196 | 65.81 195 | 71.44 204 | 61.59 162 | 58.56 222 | 61.43 168 | 75.45 180 | 52.70 227 | 58.06 160 | 69.57 210 | 64.65 204 | 71.39 201 | 64.77 195 |
|
MDTV_nov1_ep13 | | | 64.96 197 | 64.77 206 | 65.18 194 | 67.08 208 | 62.46 202 | 75.80 185 | 51.10 211 | 62.27 215 | 69.74 138 | 74.12 185 | 62.65 211 | 55.64 172 | 68.19 213 | 62.16 213 | 71.70 198 | 61.57 208 |
|
PatchmatchNet | | | 64.81 198 | 63.74 211 | 66.06 190 | 69.21 201 | 58.62 208 | 73.16 197 | 60.01 174 | 65.92 196 | 66.19 158 | 76.27 167 | 59.09 215 | 60.45 152 | 66.58 218 | 61.47 216 | 67.33 210 | 58.24 214 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpm cat1 | | | 64.79 199 | 62.74 215 | 67.17 180 | 74.61 181 | 65.91 194 | 76.18 183 | 59.32 176 | 64.88 203 | 66.41 157 | 71.21 199 | 53.56 226 | 59.17 156 | 61.53 227 | 58.16 220 | 67.33 210 | 63.95 198 |
|
DWT-MVSNet_training | | | 63.07 200 | 60.04 222 | 66.61 185 | 71.64 194 | 65.27 196 | 76.80 175 | 53.82 200 | 55.90 225 | 63.07 163 | 62.23 221 | 41.87 235 | 62.54 147 | 64.32 224 | 63.71 206 | 71.78 197 | 66.97 190 |
|
MIMVSNet | | | 63.02 201 | 69.02 193 | 56.01 210 | 68.20 203 | 59.26 207 | 70.01 208 | 53.79 201 | 71.56 173 | 41.26 221 | 71.38 197 | 82.38 167 | 36.38 218 | 71.43 206 | 67.32 199 | 66.45 212 | 59.83 211 |
|
TAMVS | | | 63.02 201 | 69.30 191 | 55.70 212 | 70.12 198 | 56.89 211 | 69.63 211 | 45.13 215 | 70.23 177 | 38.00 227 | 77.79 153 | 75.15 196 | 42.60 212 | 74.48 189 | 72.81 187 | 68.70 208 | 57.75 216 |
|
tpm | | | 62.79 203 | 63.25 212 | 62.26 200 | 70.09 199 | 53.78 217 | 71.65 202 | 47.31 213 | 65.72 198 | 76.70 103 | 80.62 140 | 56.40 222 | 48.11 205 | 64.20 225 | 58.54 218 | 59.70 222 | 63.47 200 |
|
pmmvs3 | | | 62.72 204 | 68.71 194 | 55.74 211 | 50.74 231 | 57.10 210 | 70.05 207 | 28.82 230 | 61.57 218 | 57.39 172 | 71.19 200 | 85.73 154 | 53.96 180 | 73.36 199 | 69.43 196 | 73.47 194 | 62.55 204 |
|
new-patchmatchnet | | | 62.59 205 | 73.79 181 | 49.53 223 | 76.98 169 | 53.57 218 | 53.46 233 | 54.64 195 | 85.43 69 | 28.81 233 | 91.94 43 | 96.41 35 | 25.28 230 | 76.80 181 | 53.66 227 | 57.99 224 | 58.69 213 |
|
test-LLR | | | 62.15 206 | 59.46 226 | 65.29 193 | 79.07 148 | 52.66 220 | 69.46 214 | 62.93 144 | 50.76 232 | 53.81 185 | 63.11 218 | 58.91 216 | 52.87 188 | 66.54 219 | 62.34 210 | 73.59 192 | 61.87 206 |
|
PMMVS | | | 61.98 207 | 65.61 203 | 57.74 207 | 45.03 233 | 51.76 224 | 69.54 213 | 35.05 225 | 55.49 227 | 55.32 178 | 68.23 210 | 78.39 178 | 58.09 159 | 70.21 209 | 71.56 190 | 83.42 178 | 63.66 199 |
|
test0.0.03 1 | | | 61.79 208 | 65.33 204 | 57.65 208 | 79.07 148 | 64.09 199 | 68.51 218 | 62.93 144 | 61.59 217 | 33.71 229 | 61.58 224 | 71.58 201 | 33.43 221 | 70.95 207 | 68.68 197 | 68.26 209 | 58.82 212 |
|
test1235678 | | | 60.73 209 | 68.46 195 | 51.71 220 | 61.76 216 | 56.73 213 | 73.40 194 | 42.24 219 | 67.34 187 | 39.55 223 | 70.90 201 | 92.54 107 | 28.75 225 | 73.84 193 | 66.00 202 | 64.57 215 | 51.90 222 |
|
testmv | | | 60.72 210 | 68.44 196 | 51.71 220 | 61.76 216 | 56.70 214 | 73.40 194 | 42.24 219 | 67.31 188 | 39.54 224 | 70.88 202 | 92.49 109 | 28.75 225 | 73.83 194 | 66.00 202 | 64.56 216 | 51.89 223 |
|
MVS-HIRNet | | | 59.74 211 | 58.74 229 | 60.92 202 | 57.74 224 | 45.81 231 | 56.02 231 | 58.69 181 | 55.69 226 | 65.17 159 | 70.86 203 | 71.66 199 | 56.75 164 | 61.11 228 | 53.74 226 | 71.17 202 | 52.28 221 |
|
tpmrst | | | 59.42 212 | 60.02 223 | 58.71 206 | 67.56 206 | 53.10 219 | 66.99 219 | 51.88 207 | 63.80 207 | 57.68 171 | 76.73 164 | 56.49 221 | 48.73 204 | 56.47 231 | 55.55 223 | 59.43 223 | 58.02 215 |
|
test-mter | | | 59.39 213 | 61.59 217 | 56.82 209 | 53.21 227 | 54.82 215 | 73.12 198 | 26.57 232 | 53.19 229 | 56.31 174 | 64.71 214 | 60.47 213 | 56.36 166 | 68.69 212 | 64.27 205 | 75.38 191 | 65.00 194 |
|
E-PMN | | | 59.07 214 | 62.79 214 | 54.72 213 | 67.01 210 | 47.81 230 | 60.44 228 | 43.40 216 | 72.95 165 | 44.63 214 | 70.42 207 | 73.17 198 | 58.73 158 | 80.97 167 | 51.98 228 | 54.14 228 | 42.26 231 |
|
EMVS | | | 58.97 215 | 62.63 216 | 54.70 214 | 66.26 213 | 48.71 226 | 61.74 226 | 42.71 217 | 72.80 167 | 46.00 212 | 73.01 192 | 71.66 199 | 57.91 161 | 80.41 171 | 50.68 231 | 53.55 229 | 41.11 232 |
|
testus | | | 57.41 216 | 64.98 205 | 48.58 225 | 59.39 220 | 57.17 209 | 68.81 217 | 32.86 227 | 62.32 214 | 43.25 215 | 57.59 228 | 88.49 145 | 24.19 231 | 71.68 203 | 63.20 207 | 62.99 218 | 54.42 219 |
|
TESTMET0.1,1 | | | 57.21 217 | 59.46 226 | 54.60 215 | 50.95 230 | 52.66 220 | 69.46 214 | 26.91 231 | 50.76 232 | 53.81 185 | 63.11 218 | 58.91 216 | 52.87 188 | 66.54 219 | 62.34 210 | 73.59 192 | 61.87 206 |
|
ADS-MVSNet | | | 56.89 218 | 61.09 218 | 52.00 218 | 59.48 219 | 48.10 229 | 58.02 229 | 54.37 198 | 72.82 166 | 49.19 206 | 75.32 181 | 65.97 209 | 37.96 217 | 59.34 230 | 54.66 225 | 52.99 230 | 51.42 224 |
|
EPMVS | | | 56.62 219 | 59.77 224 | 52.94 217 | 62.41 215 | 50.55 225 | 60.66 227 | 52.83 205 | 65.15 202 | 41.80 219 | 77.46 158 | 57.28 218 | 42.68 211 | 59.81 229 | 54.82 224 | 57.23 225 | 53.35 220 |
|
FMVSNet5 | | | 56.37 220 | 60.14 221 | 51.98 219 | 60.83 218 | 59.58 206 | 66.85 220 | 42.37 218 | 52.68 230 | 41.33 220 | 47.09 233 | 54.68 223 | 35.28 219 | 73.88 192 | 70.77 191 | 65.24 214 | 62.26 205 |
|
CHOSEN 280x420 | | | 56.32 221 | 58.85 228 | 53.36 216 | 51.63 229 | 39.91 234 | 69.12 216 | 38.61 224 | 56.29 224 | 36.79 228 | 48.84 232 | 62.59 212 | 63.39 145 | 73.61 197 | 67.66 198 | 60.61 220 | 63.07 203 |
|
testpf | | | 55.64 222 | 50.84 231 | 61.24 201 | 67.03 209 | 54.45 216 | 72.29 200 | 65.04 113 | 37.23 234 | 54.99 181 | 53.99 229 | 43.12 234 | 44.34 210 | 55.22 232 | 51.59 230 | 63.76 217 | 60.25 210 |
|
1111 | | | 55.38 223 | 59.51 225 | 50.57 222 | 72.41 190 | 48.16 227 | 69.76 209 | 57.08 186 | 76.79 149 | 32.10 230 | 80.12 141 | 35.41 237 | 25.87 227 | 67.23 214 | 57.74 221 | 46.17 232 | 51.09 225 |
|
N_pmnet | | | 54.95 224 | 65.90 202 | 42.18 228 | 66.37 211 | 43.86 233 | 57.92 230 | 39.79 223 | 79.54 130 | 17.24 237 | 86.31 107 | 87.91 148 | 25.44 229 | 64.68 222 | 51.76 229 | 46.33 231 | 47.23 227 |
|
test12356 | | | 54.63 225 | 63.78 210 | 43.96 226 | 51.77 228 | 51.90 223 | 65.92 221 | 30.12 228 | 62.44 213 | 30.38 232 | 64.65 215 | 89.07 139 | 30.62 223 | 73.53 198 | 62.11 214 | 54.92 226 | 42.78 230 |
|
new_pmnet | | | 52.29 226 | 63.16 213 | 39.61 230 | 58.89 222 | 44.70 232 | 48.78 235 | 34.73 226 | 65.88 197 | 17.85 236 | 73.42 189 | 80.00 173 | 23.06 232 | 67.00 217 | 62.28 212 | 54.36 227 | 48.81 226 |
|
MVE | | 41.12 19 | 51.80 227 | 60.92 219 | 41.16 229 | 35.21 235 | 34.14 236 | 48.45 236 | 41.39 221 | 69.11 181 | 19.53 235 | 63.33 217 | 73.80 197 | 63.56 142 | 67.19 216 | 61.51 215 | 38.85 233 | 57.38 217 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test2356 | | | 51.28 228 | 53.40 230 | 48.80 224 | 58.53 223 | 52.10 222 | 63.63 224 | 40.83 222 | 51.94 231 | 39.35 226 | 53.46 230 | 45.22 232 | 28.78 224 | 64.39 223 | 60.77 217 | 61.70 219 | 45.92 228 |
|
PMMVS2 | | | 48.13 229 | 64.06 208 | 29.55 231 | 44.06 234 | 36.69 235 | 51.95 234 | 29.97 229 | 74.75 160 | 8.90 239 | 76.02 175 | 91.24 127 | 7.53 233 | 73.78 195 | 55.91 222 | 34.87 234 | 40.01 233 |
|
.test1245 | | | 43.71 230 | 44.35 232 | 42.95 227 | 72.41 190 | 48.16 227 | 69.76 209 | 57.08 186 | 76.79 149 | 32.10 230 | 80.12 141 | 35.41 237 | 25.87 227 | 67.23 214 | 1.08 234 | 0.48 237 | 1.68 234 |
|
GG-mvs-BLEND | | | 41.63 231 | 60.36 220 | 19.78 232 | 0.14 239 | 66.04 193 | 55.66 232 | 0.17 237 | 57.64 223 | 2.42 240 | 51.82 231 | 69.42 206 | 0.28 237 | 64.11 226 | 58.29 219 | 60.02 221 | 55.18 218 |
|
test123 | | | 1.06 232 | 1.41 233 | 0.64 234 | 0.39 237 | 0.48 239 | 0.52 241 | 0.25 236 | 1.11 238 | 1.37 241 | 2.01 237 | 1.98 241 | 0.87 235 | 1.43 235 | 1.27 233 | 0.46 239 | 1.62 236 |
|
testmvs | | | 0.93 233 | 1.37 234 | 0.41 235 | 0.36 238 | 0.36 240 | 0.62 240 | 0.39 235 | 1.48 237 | 0.18 242 | 2.41 236 | 1.31 242 | 0.41 236 | 1.25 236 | 1.08 234 | 0.48 237 | 1.68 234 |
|
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 | | | | 88.38 59 | | 91.62 15 | 87.97 48 | 84.48 129 | | 88.64 41 | 87.93 16 | 87.38 96 | 94.82 82 | 74.53 72 | 89.14 81 | 83.86 105 | 85.94 158 | 86.84 74 |
|
MTAPA | | | | | | | | | | | 89.37 9 | | 94.85 80 | | | | | |
|
MTMP | | | | | | | | | | | 90.54 5 | | 95.16 72 | | | | | |
|
Patchmatch-RL test | | | | | | | | 4.13 239 | | | | | | | | | | |
|
tmp_tt | | | | | 13.54 233 | 16.73 236 | 6.42 238 | 8.49 238 | 2.36 234 | 28.69 236 | 27.44 234 | 18.40 235 | 13.51 240 | 3.70 234 | 33.23 233 | 36.26 232 | 22.54 236 | |
|
XVS | | | | | | 91.28 23 | 91.23 8 | 96.89 2 | | | 87.14 27 | | 94.53 84 | | | | 95.84 15 | |
|
X-MVStestdata | | | | | | 91.28 23 | 91.23 8 | 96.89 2 | | | 87.14 27 | | 94.53 84 | | | | 95.84 15 | |
|
abl_6 | | | | | 79.30 101 | 84.98 76 | 85.78 64 | 90.50 62 | 66.88 90 | 77.08 148 | 74.02 119 | 73.29 191 | 89.34 137 | 68.94 121 | | | 90.49 82 | 85.98 79 |
|
mPP-MVS | | | | | | 93.05 4 | | | | | | | 95.77 54 | | | | | |
|
NP-MVS | | | | | | | | | | 78.65 139 | | | | | | | | |
|
Patchmtry | | | | | | | 56.88 212 | 64.47 222 | 67.74 86 | | 72.30 126 | | | | | | | |
|
DeepMVS_CX | | | | | | | 17.78 237 | 20.40 237 | 6.69 233 | 31.41 235 | 9.80 238 | 38.61 234 | 34.88 239 | 33.78 220 | 28.41 234 | | 23.59 235 | 45.77 229 |
|