TDRefinement | | | 86.29 1 | 90.77 1 | 81.06 1 | 75.10 47 | 83.76 2 | 93.79 1 | 61.08 18 | 89.57 2 | 86.19 1 | 90.06 7 | 93.01 27 | 76.72 2 | 94.71 1 | 92.72 1 | 93.47 1 | 91.56 2 |
|
COLMAP_ROB | | 75.87 2 | 84.34 2 | 89.80 2 | 77.97 13 | 75.52 45 | 82.76 4 | 90.39 21 | 54.21 50 | 89.37 3 | 83.18 2 | 89.90 8 | 95.58 11 | 72.34 10 | 92.31 4 | 90.04 5 | 92.17 5 | 88.61 18 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
CP-MVS | | | 84.06 3 | 86.79 9 | 80.86 2 | 81.81 8 | 79.66 29 | 92.67 6 | 64.48 1 | 83.13 26 | 82.32 3 | 80.89 84 | 92.97 28 | 72.51 9 | 91.74 6 | 90.02 6 | 91.40 17 | 89.14 8 |
|
ACMMPR | | | 83.94 4 | 87.20 3 | 80.14 4 | 81.04 12 | 81.92 8 | 92.57 8 | 63.14 5 | 84.35 17 | 79.45 13 | 83.37 51 | 92.04 37 | 72.82 8 | 90.66 12 | 88.96 12 | 91.80 6 | 89.13 9 |
|
MP-MVS | | | 83.50 5 | 86.11 19 | 80.45 3 | 82.58 5 | 80.60 23 | 92.68 5 | 63.48 3 | 81.43 39 | 80.21 9 | 81.95 73 | 90.76 63 | 72.86 6 | 90.14 19 | 89.30 11 | 90.92 19 | 88.59 19 |
|
ACMMP | | | 83.17 6 | 86.75 10 | 79.01 8 | 80.11 24 | 82.01 7 | 92.29 11 | 60.35 25 | 82.20 34 | 78.32 16 | 80.59 85 | 93.14 24 | 70.67 16 | 91.30 8 | 89.36 10 | 92.30 4 | 88.62 17 |
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 |
PGM-MVS | | | 83.03 7 | 85.67 25 | 79.95 5 | 80.69 16 | 81.09 15 | 92.40 10 | 63.06 6 | 79.38 58 | 80.21 9 | 80.31 87 | 91.44 47 | 71.75 12 | 90.46 15 | 88.53 15 | 91.57 9 | 88.50 20 |
|
LGP-MVS_train | | | 82.91 8 | 86.50 12 | 78.72 9 | 78.72 34 | 81.03 16 | 89.78 25 | 61.16 17 | 80.15 52 | 80.44 6 | 84.83 36 | 94.19 17 | 70.52 19 | 90.70 11 | 87.19 23 | 91.71 8 | 87.37 26 |
|
ACMM | | 71.24 7 | 82.85 9 | 86.59 11 | 78.50 10 | 80.10 25 | 78.59 31 | 91.77 12 | 60.76 23 | 84.43 15 | 76.49 25 | 81.58 79 | 93.50 19 | 70.45 20 | 91.38 7 | 89.42 9 | 91.42 16 | 87.22 28 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
zzz-MVS | | | 82.61 10 | 85.04 29 | 79.79 6 | 82.59 4 | 73.90 54 | 92.42 9 | 62.39 11 | 84.54 14 | 80.21 9 | 79.86 91 | 90.74 64 | 70.63 17 | 90.01 21 | 89.71 8 | 90.48 21 | 86.49 33 |
|
SMA-MVS | | | 82.46 11 | 86.30 14 | 77.99 12 | 80.13 23 | 80.41 24 | 91.20 15 | 60.85 22 | 85.43 9 | 79.65 12 | 84.19 40 | 87.27 104 | 66.00 36 | 88.65 29 | 87.80 19 | 90.12 22 | 90.34 4 |
|
HFP-MVS | | | 82.37 12 | 86.28 15 | 77.81 16 | 79.94 26 | 80.96 18 | 91.13 16 | 63.30 4 | 84.04 19 | 71.81 38 | 82.39 65 | 89.59 83 | 69.16 23 | 89.08 26 | 88.83 14 | 91.49 13 | 89.10 10 |
|
DeepC-MVS | | 73.80 3 | 82.34 13 | 86.87 7 | 77.06 19 | 78.62 35 | 84.34 1 | 90.30 23 | 63.54 2 | 83.10 27 | 71.30 42 | 86.91 23 | 90.54 71 | 67.12 32 | 87.81 35 | 87.05 24 | 91.46 15 | 88.37 21 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CPTT-MVS | | | 82.32 14 | 85.00 31 | 79.19 7 | 80.73 15 | 80.86 21 | 91.68 13 | 62.59 9 | 82.55 31 | 75.53 29 | 73.88 122 | 92.28 34 | 73.74 5 | 90.07 20 | 87.65 20 | 90.87 20 | 87.74 24 |
|
ACMP | | 70.35 9 | 82.17 15 | 86.45 13 | 77.18 18 | 79.33 27 | 81.00 17 | 89.27 29 | 58.63 30 | 81.35 41 | 75.46 30 | 82.97 56 | 95.08 12 | 68.90 25 | 90.49 14 | 87.43 22 | 91.48 14 | 86.84 30 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
SteuartSystems-ACMMP | | | 82.16 16 | 85.55 26 | 78.21 11 | 80.48 18 | 79.28 30 | 92.65 7 | 61.03 19 | 80.55 49 | 77.00 23 | 81.80 78 | 90.71 65 | 68.73 26 | 90.25 17 | 87.94 18 | 89.36 28 | 88.30 22 |
Skip Steuart: Steuart Systems R&D Blog. |
SD-MVS | | | 82.13 17 | 86.80 8 | 76.67 20 | 80.36 21 | 80.66 22 | 89.48 26 | 56.93 33 | 82.50 32 | 67.55 67 | 87.05 19 | 91.40 50 | 72.84 7 | 88.66 28 | 88.32 16 | 92.85 2 | 89.04 11 |
|
LTVRE_ROB | | 75.99 1 | 82.04 18 | 87.16 4 | 76.07 22 | 63.57 120 | 70.27 70 | 86.48 39 | 62.99 7 | 89.00 5 | 80.32 7 | 86.25 25 | 91.04 57 | 74.66 4 | 92.58 3 | 90.29 4 | 88.42 35 | 90.72 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 |
PMVS | | 70.37 8 | 81.82 19 | 87.08 5 | 75.68 24 | 77.06 41 | 77.23 37 | 87.77 37 | 56.25 39 | 83.33 25 | 67.18 78 | 89.48 10 | 87.94 94 | 77.70 1 | 93.02 2 | 92.57 2 | 88.13 37 | 86.00 36 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ACMMP_Plus | | | 81.79 20 | 85.72 23 | 77.21 17 | 79.15 32 | 79.68 28 | 91.62 14 | 59.66 26 | 83.55 22 | 77.74 19 | 83.72 47 | 87.34 102 | 65.36 37 | 88.61 30 | 87.56 21 | 89.73 27 | 89.58 6 |
|
X-MVS | | | 81.61 21 | 84.73 33 | 77.97 13 | 80.31 22 | 81.29 12 | 93.53 2 | 62.50 10 | 81.41 40 | 77.45 20 | 72.04 132 | 90.19 76 | 62.50 52 | 90.57 13 | 88.87 13 | 91.54 10 | 88.73 14 |
|
OPM-MVS | | | 81.44 22 | 85.68 24 | 76.49 21 | 79.27 28 | 78.21 33 | 89.84 24 | 58.67 29 | 85.25 10 | 76.26 26 | 85.28 32 | 92.88 29 | 66.03 35 | 87.20 38 | 85.40 28 | 88.86 32 | 85.58 40 |
|
TSAR-MVS + MP. | | | 81.23 23 | 86.13 17 | 75.52 25 | 80.74 14 | 83.22 3 | 90.55 17 | 55.12 45 | 80.87 45 | 67.62 66 | 88.01 13 | 92.38 33 | 70.61 18 | 86.64 40 | 83.10 43 | 88.51 33 | 88.67 15 |
|
TSAR-MVS + ACMM | | | 81.20 24 | 86.92 6 | 74.52 29 | 77.60 37 | 82.29 5 | 84.41 46 | 62.95 8 | 82.99 28 | 64.03 90 | 87.71 14 | 89.17 86 | 71.98 11 | 88.19 32 | 88.10 17 | 86.18 51 | 89.95 5 |
|
APDe-MVS | | | 81.08 25 | 86.12 18 | 75.20 27 | 79.25 29 | 80.91 19 | 90.38 22 | 57.05 32 | 85.83 8 | 66.07 83 | 87.34 17 | 91.27 54 | 69.45 21 | 85.99 44 | 82.55 44 | 88.98 31 | 88.95 12 |
|
APD-MVS | | | 80.60 26 | 84.63 34 | 75.91 23 | 81.22 10 | 81.48 10 | 90.49 19 | 58.81 28 | 77.54 64 | 67.49 69 | 85.90 27 | 89.82 82 | 69.43 22 | 86.08 43 | 83.80 38 | 88.01 38 | 87.77 23 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
HPM-MVS++ | | | 80.44 27 | 82.57 46 | 77.96 15 | 81.99 7 | 72.76 58 | 90.48 20 | 61.31 14 | 80.85 46 | 77.90 18 | 81.93 74 | 87.01 107 | 68.20 28 | 84.15 53 | 85.27 30 | 87.85 39 | 86.00 36 |
|
ESAPD | | | 80.30 28 | 84.42 37 | 75.49 26 | 79.20 31 | 79.76 27 | 89.40 27 | 58.51 31 | 81.15 43 | 69.56 54 | 85.14 33 | 88.71 90 | 68.92 24 | 85.26 49 | 82.30 50 | 87.35 42 | 88.64 16 |
|
ACMH+ | | 67.97 10 | 80.15 29 | 86.16 16 | 73.14 38 | 73.82 53 | 76.41 40 | 83.59 48 | 54.82 48 | 87.35 6 | 70.86 46 | 86.98 22 | 96.27 5 | 66.50 33 | 89.17 25 | 83.39 40 | 89.26 29 | 83.56 47 |
|
OMC-MVS | | | 79.95 30 | 85.28 27 | 73.74 35 | 72.95 56 | 80.10 26 | 87.87 36 | 48.13 75 | 84.62 13 | 79.42 14 | 80.27 88 | 92.49 31 | 64.14 43 | 87.25 37 | 85.11 31 | 89.92 25 | 87.10 29 |
|
HSP-MVS | | | 79.66 31 | 84.23 38 | 74.34 31 | 78.92 33 | 81.86 9 | 90.55 17 | 60.49 24 | 80.19 51 | 69.08 57 | 85.12 34 | 90.92 61 | 62.99 49 | 81.15 74 | 78.00 67 | 83.99 61 | 92.37 1 |
|
DeepPCF-MVS | | 71.57 5 | 79.49 32 | 84.05 39 | 74.17 32 | 74.14 50 | 80.88 20 | 89.33 28 | 56.24 40 | 82.41 33 | 71.58 40 | 82.27 66 | 86.47 112 | 66.47 34 | 84.80 51 | 84.16 36 | 87.26 43 | 87.34 27 |
|
LS3D | | | 79.33 33 | 84.03 40 | 73.84 33 | 75.37 46 | 78.09 34 | 83.30 49 | 52.94 57 | 84.42 16 | 76.01 27 | 84.16 42 | 87.44 101 | 65.34 38 | 86.30 41 | 82.08 52 | 90.09 23 | 85.70 38 |
|
3Dnovator+ | | 72.94 4 | 78.78 34 | 83.05 43 | 73.80 34 | 70.70 68 | 81.34 11 | 88.33 33 | 56.01 41 | 81.33 42 | 72.87 36 | 78.06 101 | 81.15 137 | 63.83 45 | 87.39 36 | 85.82 26 | 91.06 18 | 86.28 35 |
|
UA-Net | | | 78.65 35 | 83.96 41 | 72.46 40 | 84.87 1 | 76.15 41 | 89.06 30 | 55.70 42 | 77.25 65 | 53.14 122 | 79.73 93 | 82.09 135 | 59.69 67 | 92.21 5 | 90.93 3 | 92.32 3 | 89.36 7 |
|
DeepC-MVS_fast | | 71.40 6 | 78.48 36 | 82.92 44 | 73.31 37 | 76.44 43 | 82.23 6 | 87.59 38 | 56.56 36 | 77.79 62 | 68.91 59 | 77.00 106 | 87.32 103 | 61.90 54 | 85.40 46 | 84.37 33 | 88.46 34 | 86.33 34 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
WR-MVS | | | 78.32 37 | 86.09 20 | 69.25 57 | 76.22 44 | 72.33 65 | 85.71 42 | 59.02 27 | 86.66 7 | 51.41 127 | 92.91 1 | 96.76 2 | 53.09 114 | 90.21 18 | 85.30 29 | 90.05 24 | 78.46 73 |
|
ACMH | | 66.19 11 | 78.12 38 | 84.55 35 | 70.63 48 | 69.62 74 | 72.40 64 | 80.77 65 | 46.43 87 | 89.24 4 | 77.99 17 | 87.42 16 | 95.83 9 | 62.95 50 | 86.27 42 | 78.24 66 | 86.00 54 | 82.46 50 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
train_agg | | | 77.83 39 | 80.47 58 | 74.77 28 | 80.92 13 | 69.60 71 | 88.87 32 | 56.32 38 | 74.03 81 | 71.03 44 | 83.67 48 | 87.68 97 | 64.75 41 | 83.70 57 | 81.85 53 | 86.71 46 | 82.73 49 |
|
NCCC | | | 77.82 40 | 80.72 57 | 74.43 30 | 79.24 30 | 75.72 44 | 88.06 34 | 56.36 37 | 79.61 56 | 73.22 34 | 67.75 146 | 87.05 106 | 63.09 48 | 85.62 45 | 84.00 37 | 86.62 47 | 85.30 42 |
|
CNVR-MVS | | | 77.79 41 | 81.57 51 | 73.38 36 | 78.37 36 | 75.91 42 | 87.97 35 | 55.11 46 | 79.41 57 | 70.98 45 | 74.70 120 | 86.43 113 | 61.77 55 | 85.10 50 | 83.73 39 | 86.10 53 | 85.68 39 |
|
WR-MVS_H | | | 77.56 42 | 85.88 21 | 67.86 60 | 80.54 17 | 74.32 51 | 83.23 50 | 61.78 12 | 83.47 23 | 47.46 144 | 91.81 5 | 95.84 8 | 50.50 123 | 90.44 16 | 84.37 33 | 83.63 64 | 80.89 59 |
|
RPSCF | | | 77.56 42 | 84.51 36 | 69.46 56 | 65.17 98 | 74.36 50 | 79.74 70 | 47.45 78 | 84.01 20 | 72.89 35 | 77.89 102 | 90.67 66 | 65.14 40 | 88.25 31 | 89.74 7 | 86.38 50 | 86.64 32 |
|
PS-CasMVS | | | 77.46 44 | 85.80 22 | 67.73 62 | 81.24 9 | 72.88 57 | 80.63 66 | 61.28 15 | 84.14 18 | 50.53 131 | 92.13 3 | 96.76 2 | 50.12 126 | 91.02 9 | 84.46 32 | 82.60 77 | 79.19 66 |
|
DTE-MVSNet | | | 77.28 45 | 84.87 32 | 68.42 58 | 82.94 3 | 72.70 60 | 81.60 60 | 61.78 12 | 85.03 11 | 51.40 128 | 92.11 4 | 96.00 6 | 49.42 129 | 89.73 23 | 82.52 46 | 83.39 68 | 75.98 83 |
|
SixPastTwentyTwo | | | 77.24 46 | 83.65 42 | 69.78 52 | 65.14 99 | 64.85 91 | 77.44 81 | 47.74 77 | 82.76 30 | 68.52 60 | 87.65 15 | 93.31 21 | 71.68 13 | 89.49 24 | 82.41 47 | 88.14 36 | 85.05 43 |
|
CDPH-MVS | | | 77.22 47 | 81.05 56 | 72.75 39 | 77.29 39 | 77.46 36 | 86.36 40 | 54.02 52 | 73.00 86 | 69.75 52 | 77.78 104 | 88.90 89 | 61.31 59 | 84.09 56 | 82.54 45 | 87.79 40 | 83.57 46 |
|
PEN-MVS | | | 77.06 48 | 85.05 28 | 67.74 61 | 82.29 6 | 72.59 61 | 80.86 64 | 61.03 19 | 84.66 12 | 50.08 135 | 92.19 2 | 96.59 4 | 49.12 130 | 89.83 22 | 82.35 48 | 83.06 71 | 77.14 79 |
|
CP-MVSNet | | | 77.01 49 | 85.04 29 | 67.65 63 | 81.16 11 | 72.72 59 | 80.54 67 | 61.18 16 | 82.09 35 | 50.41 132 | 90.81 6 | 95.89 7 | 50.03 127 | 90.86 10 | 84.30 35 | 82.56 78 | 78.65 72 |
|
CSCG | | | 76.95 50 | 82.08 48 | 70.97 44 | 73.32 55 | 78.35 32 | 81.08 63 | 47.19 80 | 83.47 23 | 69.82 51 | 80.44 86 | 87.19 105 | 64.59 42 | 81.01 77 | 77.26 73 | 89.83 26 | 86.84 30 |
|
CNLPA | | | 76.67 51 | 81.72 49 | 70.77 47 | 70.75 66 | 76.68 39 | 86.14 41 | 46.11 89 | 81.82 37 | 74.68 31 | 76.37 109 | 86.23 115 | 62.92 51 | 85.28 48 | 83.29 41 | 84.02 60 | 82.40 51 |
|
MSLP-MVS++ | | | 76.66 52 | 82.32 47 | 70.06 50 | 70.51 69 | 80.27 25 | 79.77 69 | 55.58 43 | 77.79 62 | 63.09 92 | 67.25 150 | 89.50 84 | 71.01 15 | 88.10 33 | 85.74 27 | 80.39 88 | 87.56 25 |
|
TSAR-MVS + COLMAP | | | 75.85 53 | 81.06 54 | 69.77 53 | 71.15 62 | 76.90 38 | 82.93 52 | 52.43 59 | 79.25 59 | 70.13 49 | 82.78 57 | 87.00 108 | 60.02 63 | 80.30 83 | 79.61 59 | 81.95 82 | 81.61 55 |
|
HQP-MVS | | | 75.81 54 | 78.91 65 | 72.18 41 | 77.41 38 | 75.38 46 | 84.75 43 | 53.35 54 | 76.12 69 | 73.32 33 | 69.48 137 | 88.07 92 | 57.76 78 | 79.42 87 | 78.44 63 | 86.48 48 | 85.50 41 |
|
PLC | | 64.88 15 | 75.76 55 | 80.22 59 | 70.57 49 | 70.46 70 | 77.75 35 | 82.01 58 | 48.84 70 | 80.74 48 | 70.85 47 | 71.32 134 | 84.82 124 | 63.69 46 | 84.73 52 | 82.35 48 | 87.54 41 | 79.80 63 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
TAPA-MVS | | 66.11 12 | 75.37 56 | 79.24 63 | 70.86 45 | 67.63 80 | 74.09 52 | 83.17 51 | 44.75 101 | 81.82 37 | 80.83 5 | 65.61 159 | 88.04 93 | 61.58 56 | 83.21 63 | 80.12 56 | 87.17 44 | 81.82 54 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PHI-MVS | | | 75.17 57 | 78.37 66 | 71.43 42 | 71.13 63 | 72.46 63 | 82.28 57 | 50.55 63 | 73.39 84 | 79.05 15 | 73.65 124 | 87.50 100 | 61.98 53 | 81.10 75 | 78.48 62 | 83.60 65 | 81.99 52 |
|
anonymousdsp | | | 74.76 58 | 82.59 45 | 65.63 77 | 45.61 212 | 61.13 127 | 89.06 30 | 32.58 203 | 74.11 80 | 59.55 101 | 84.06 44 | 94.12 18 | 75.24 3 | 88.94 27 | 86.95 25 | 91.74 7 | 88.81 13 |
|
AdaColmap | | | 74.73 59 | 77.57 71 | 71.40 43 | 76.90 42 | 75.76 43 | 84.54 45 | 53.08 56 | 76.20 68 | 66.64 82 | 66.06 157 | 78.16 153 | 61.32 58 | 85.37 47 | 82.20 51 | 85.95 55 | 79.27 65 |
|
v7n | | | 74.47 60 | 81.06 54 | 66.77 68 | 66.98 84 | 67.10 74 | 76.76 84 | 45.88 91 | 81.98 36 | 67.43 71 | 88.38 12 | 95.67 10 | 61.38 57 | 80.76 80 | 73.49 94 | 82.21 80 | 80.06 61 |
|
PCF-MVS | | 65.25 14 | 73.99 61 | 76.74 76 | 70.79 46 | 71.61 61 | 75.33 47 | 83.76 47 | 50.40 64 | 74.88 73 | 74.50 32 | 67.60 147 | 85.36 121 | 58.30 74 | 78.61 90 | 74.25 90 | 86.15 52 | 81.13 58 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
v52 | | | 73.95 62 | 81.43 53 | 65.22 81 | 54.85 181 | 63.32 111 | 78.90 72 | 38.00 176 | 80.00 54 | 68.32 62 | 87.02 20 | 94.98 15 | 68.14 30 | 84.11 54 | 75.63 83 | 83.12 69 | 84.96 44 |
|
V4 | | | 73.95 62 | 81.44 52 | 65.22 81 | 54.86 180 | 63.31 112 | 78.89 73 | 38.00 176 | 80.03 53 | 68.29 63 | 87.02 20 | 95.00 13 | 68.15 29 | 84.11 54 | 75.62 84 | 83.12 69 | 84.95 45 |
|
MCST-MVS | | | 73.84 64 | 77.44 72 | 69.63 55 | 73.75 54 | 74.73 49 | 81.38 62 | 48.58 71 | 74.77 74 | 69.16 56 | 71.97 133 | 86.20 116 | 59.50 69 | 78.51 91 | 74.06 91 | 85.42 56 | 81.85 53 |
|
MVS_0304 | | | 73.74 65 | 77.16 74 | 69.74 54 | 74.24 49 | 73.47 55 | 84.70 44 | 49.62 65 | 62.26 157 | 67.27 75 | 75.87 112 | 87.57 99 | 57.49 83 | 81.20 73 | 79.50 60 | 85.10 57 | 80.27 60 |
|
TSAR-MVS + GP. | | | 73.42 66 | 76.31 77 | 70.05 51 | 77.15 40 | 71.13 68 | 81.59 61 | 54.11 51 | 69.84 118 | 58.65 104 | 66.20 156 | 78.77 150 | 65.29 39 | 83.65 58 | 83.14 42 | 83.54 66 | 81.47 56 |
|
Gipuma | | | 73.40 67 | 79.27 62 | 66.55 72 | 63.64 119 | 59.35 133 | 70.28 130 | 45.92 90 | 83.79 21 | 71.78 39 | 84.04 45 | 93.07 26 | 68.69 27 | 87.90 34 | 76.76 76 | 78.98 100 | 69.96 120 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MVS_111021_HR | | | 72.37 68 | 76.12 80 | 68.00 59 | 68.55 77 | 64.30 103 | 82.93 52 | 48.98 69 | 74.25 78 | 65.39 84 | 73.59 125 | 84.11 128 | 59.48 70 | 82.61 66 | 78.38 64 | 82.66 76 | 75.59 84 |
|
TinyColmap | | | 71.85 69 | 76.11 81 | 66.87 67 | 66.07 88 | 65.34 86 | 74.35 106 | 49.30 68 | 79.93 55 | 75.93 28 | 75.66 114 | 87.74 96 | 54.72 105 | 80.66 82 | 70.42 111 | 80.85 86 | 73.02 102 |
|
TranMVSNet+NR-MVSNet | | | 71.66 70 | 79.23 64 | 62.83 109 | 72.54 58 | 65.64 82 | 74.77 104 | 55.27 44 | 75.91 70 | 45.50 155 | 89.55 9 | 94.25 16 | 45.96 146 | 82.74 65 | 77.03 75 | 82.96 72 | 69.48 126 |
|
MVS_111021_LR | | | 71.60 71 | 75.21 86 | 67.38 64 | 67.42 81 | 62.44 121 | 81.73 59 | 46.24 88 | 70.89 101 | 66.80 81 | 73.19 127 | 84.98 122 | 60.09 62 | 81.94 69 | 77.77 71 | 82.00 81 | 75.29 85 |
|
EG-PatchMatch MVS | | | 71.50 72 | 76.82 75 | 65.30 79 | 70.74 67 | 66.50 78 | 74.23 108 | 43.25 115 | 72.02 89 | 59.11 102 | 79.85 92 | 86.88 110 | 63.95 44 | 80.29 84 | 75.25 87 | 80.51 87 | 76.98 80 |
|
UniMVSNet (Re) | | | 71.29 73 | 78.14 67 | 63.30 99 | 70.29 71 | 66.57 77 | 75.98 89 | 54.74 49 | 70.20 111 | 46.20 153 | 85.08 35 | 93.21 22 | 48.19 134 | 82.50 67 | 78.33 65 | 84.40 58 | 71.08 117 |
|
v748 | | | 71.27 74 | 79.41 61 | 61.76 113 | 60.62 143 | 61.73 124 | 68.46 138 | 40.71 153 | 80.76 47 | 61.02 97 | 87.12 18 | 95.00 13 | 59.62 68 | 80.67 81 | 70.67 109 | 80.14 91 | 79.93 62 |
|
CLD-MVS | | | 71.24 75 | 78.12 68 | 63.20 101 | 74.03 51 | 71.60 66 | 82.82 54 | 32.91 200 | 74.23 79 | 69.32 55 | 79.65 94 | 91.54 45 | 47.02 142 | 81.22 72 | 79.01 61 | 73.09 154 | 69.63 122 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
Anonymous20231211 | | | 71.23 76 | 81.58 50 | 59.15 122 | 71.63 60 | 60.40 132 | 70.12 131 | 52.15 60 | 92.79 1 | 42.31 165 | 88.89 11 | 98.03 1 | 40.61 165 | 80.86 79 | 75.96 82 | 78.08 117 | 74.11 89 |
|
CANet | | | 71.07 77 | 75.09 88 | 66.39 73 | 72.57 57 | 71.53 67 | 82.38 56 | 47.10 81 | 59.81 165 | 59.81 100 | 74.97 117 | 84.37 127 | 54.25 108 | 79.89 86 | 77.64 72 | 82.25 79 | 77.40 77 |
|
v1192 | | | 71.06 78 | 74.87 91 | 66.61 70 | 66.38 86 | 65.80 81 | 78.27 75 | 45.28 94 | 70.19 112 | 70.79 48 | 83.37 51 | 91.79 40 | 58.76 73 | 70.86 160 | 69.02 117 | 80.16 90 | 73.08 100 |
|
DU-MVS | | | 71.03 79 | 77.92 69 | 62.98 107 | 70.81 64 | 65.48 84 | 73.93 116 | 56.76 34 | 69.95 116 | 46.77 150 | 85.70 30 | 93.49 20 | 46.91 143 | 83.47 59 | 77.82 70 | 82.72 75 | 69.54 123 |
|
v1240 | | | 70.94 80 | 74.52 98 | 66.76 69 | 66.54 85 | 64.40 97 | 77.76 78 | 45.29 93 | 70.05 114 | 71.45 41 | 83.36 53 | 90.96 59 | 60.37 61 | 70.50 162 | 68.68 119 | 79.14 98 | 73.68 95 |
|
v1921920 | | | 70.82 81 | 74.46 100 | 66.58 71 | 66.33 87 | 64.35 102 | 77.72 79 | 45.07 96 | 70.39 105 | 71.18 43 | 83.15 54 | 90.62 68 | 59.97 64 | 70.90 158 | 68.43 127 | 79.19 97 | 73.39 97 |
|
UniMVSNet_NR-MVSNet | | | 70.82 81 | 77.44 72 | 63.11 102 | 71.75 59 | 66.02 80 | 73.93 116 | 55.00 47 | 70.90 100 | 46.77 150 | 86.68 24 | 91.54 45 | 46.91 143 | 81.07 76 | 76.32 80 | 84.28 59 | 69.54 123 |
|
PVSNet_Blended_VisFu | | | 70.70 83 | 73.62 107 | 67.28 66 | 63.53 122 | 72.96 56 | 77.97 76 | 52.10 61 | 63.65 148 | 62.66 94 | 71.14 135 | 73.46 167 | 63.55 47 | 79.35 89 | 75.34 86 | 83.90 62 | 79.43 64 |
|
v144192 | | | 70.68 84 | 74.40 102 | 66.34 74 | 65.94 90 | 64.38 99 | 77.63 80 | 45.18 95 | 69.97 115 | 70.11 50 | 82.70 60 | 90.77 62 | 59.84 66 | 71.43 153 | 68.46 123 | 79.31 96 | 73.08 100 |
|
v13 | | | 70.58 85 | 75.49 84 | 64.87 85 | 64.66 102 | 64.58 94 | 76.18 87 | 43.69 109 | 72.34 88 | 67.65 65 | 84.36 39 | 92.01 38 | 58.05 75 | 73.57 114 | 67.06 145 | 78.96 101 | 74.48 88 |
|
FPMVS | | | 70.46 86 | 74.89 90 | 65.28 80 | 69.09 76 | 61.42 125 | 77.07 83 | 46.92 84 | 76.73 67 | 53.53 119 | 67.33 148 | 75.07 162 | 67.23 31 | 83.41 61 | 81.54 54 | 77.86 122 | 78.73 70 |
|
v1144 | | | 70.45 87 | 74.50 99 | 65.73 76 | 65.74 92 | 64.88 90 | 77.33 82 | 44.16 103 | 70.59 104 | 69.63 53 | 83.15 54 | 91.42 49 | 57.79 77 | 71.29 157 | 68.53 122 | 79.72 93 | 71.63 115 |
|
v12 | | | 70.39 88 | 75.25 85 | 64.73 86 | 64.60 104 | 64.47 95 | 76.00 88 | 43.55 111 | 71.92 90 | 67.51 68 | 84.15 43 | 91.88 39 | 57.83 76 | 73.32 115 | 67.00 146 | 78.87 102 | 74.02 92 |
|
v10 | | | 70.25 89 | 74.59 96 | 65.19 83 | 65.32 96 | 66.46 79 | 76.60 85 | 44.84 99 | 67.38 127 | 67.21 77 | 82.75 59 | 90.56 70 | 57.70 79 | 71.69 147 | 68.63 120 | 79.44 94 | 74.67 87 |
|
V9 | | | 70.20 90 | 75.02 89 | 64.58 88 | 64.49 105 | 64.36 100 | 75.80 93 | 43.40 112 | 71.53 91 | 67.35 74 | 83.95 46 | 91.73 42 | 57.63 81 | 73.04 118 | 66.96 147 | 78.79 104 | 73.61 96 |
|
Effi-MVS+-dtu | | | 70.10 91 | 73.76 106 | 65.82 75 | 70.23 72 | 74.92 48 | 79.47 71 | 44.49 102 | 56.98 180 | 54.34 115 | 64.26 169 | 84.78 125 | 59.97 64 | 80.96 78 | 80.38 55 | 86.44 49 | 74.05 91 |
|
v11 | | | 70.10 91 | 74.82 92 | 64.58 88 | 64.83 100 | 64.39 98 | 75.89 90 | 43.18 117 | 71.34 94 | 67.75 64 | 84.19 40 | 91.75 41 | 57.23 85 | 71.46 152 | 66.85 150 | 78.60 107 | 73.78 93 |
|
MAR-MVS | | | 70.00 93 | 72.28 124 | 67.34 65 | 69.89 73 | 72.57 62 | 80.09 68 | 49.49 67 | 60.28 163 | 69.03 58 | 59.29 192 | 80.79 139 | 54.68 106 | 78.39 93 | 76.00 81 | 80.87 85 | 78.67 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 |
V14 | | | 69.99 94 | 74.77 94 | 64.41 91 | 64.39 106 | 64.25 104 | 75.59 95 | 43.25 115 | 71.12 98 | 67.14 79 | 83.65 49 | 91.58 44 | 57.40 84 | 72.75 126 | 66.90 149 | 78.70 105 | 73.15 99 |
|
Vis-MVSNet | | | 69.95 95 | 77.69 70 | 60.91 116 | 60.67 141 | 66.71 75 | 77.94 77 | 48.58 71 | 69.10 120 | 45.78 154 | 80.21 89 | 83.58 132 | 53.41 113 | 82.92 64 | 80.11 57 | 79.08 99 | 81.21 57 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v7 | | | 69.81 96 | 73.94 104 | 65.00 84 | 65.33 94 | 65.07 87 | 76.60 85 | 43.66 110 | 67.36 128 | 67.25 76 | 82.76 58 | 90.57 69 | 57.70 79 | 71.69 147 | 68.63 120 | 79.44 94 | 71.52 116 |
|
v15 | | | 69.80 97 | 74.53 97 | 64.27 93 | 64.30 107 | 64.15 105 | 75.40 97 | 43.12 118 | 70.71 103 | 66.98 80 | 83.41 50 | 91.43 48 | 57.21 86 | 72.48 131 | 66.84 151 | 78.62 106 | 72.72 104 |
|
EPP-MVSNet | | | 69.51 98 | 76.17 78 | 61.74 114 | 68.38 79 | 66.60 76 | 71.77 122 | 46.98 82 | 73.60 83 | 41.79 167 | 82.06 72 | 69.65 180 | 52.51 117 | 83.41 61 | 79.94 58 | 89.02 30 | 77.94 75 |
|
3Dnovator | | 65.69 13 | 69.43 99 | 75.74 83 | 62.06 112 | 60.78 140 | 70.50 69 | 75.85 92 | 39.57 163 | 74.44 76 | 57.41 107 | 75.91 110 | 77.73 155 | 55.34 101 | 76.86 96 | 75.61 85 | 83.44 67 | 79.14 67 |
|
Effi-MVS+ | | | 69.04 100 | 73.01 117 | 64.40 92 | 67.20 82 | 64.83 92 | 74.87 103 | 43.97 105 | 63.33 151 | 60.90 98 | 73.06 128 | 85.79 118 | 55.61 99 | 73.58 113 | 76.41 79 | 83.84 63 | 74.09 90 |
|
v2v482 | | | 69.01 101 | 73.39 109 | 63.89 95 | 63.86 112 | 62.99 116 | 75.26 98 | 42.05 129 | 70.22 110 | 68.46 61 | 82.64 61 | 91.61 43 | 55.38 100 | 70.89 159 | 66.93 148 | 78.30 112 | 68.48 136 |
|
v1 | | | 68.98 102 | 73.38 110 | 63.84 96 | 64.12 109 | 62.97 117 | 74.95 102 | 41.52 138 | 70.28 108 | 67.47 70 | 82.49 62 | 91.37 51 | 56.59 88 | 71.43 153 | 66.51 158 | 78.41 109 | 68.62 132 |
|
MSDG | | | 68.98 102 | 73.31 113 | 63.92 94 | 67.08 83 | 68.27 72 | 75.41 96 | 40.77 149 | 67.61 126 | 64.89 85 | 75.75 113 | 78.96 147 | 53.70 110 | 76.72 98 | 73.95 92 | 81.71 84 | 71.93 112 |
|
v1141 | | | 68.97 104 | 73.38 110 | 63.83 97 | 64.11 110 | 62.97 117 | 74.96 99 | 41.52 138 | 70.29 106 | 67.36 73 | 82.47 63 | 91.37 51 | 56.59 88 | 71.43 153 | 66.49 160 | 78.41 109 | 68.61 134 |
|
divwei89l23v2f112 | | | 68.97 104 | 73.38 110 | 63.83 97 | 64.11 110 | 62.97 117 | 74.96 99 | 41.52 138 | 70.29 106 | 67.39 72 | 82.47 63 | 91.37 51 | 56.59 88 | 71.42 156 | 66.50 159 | 78.40 111 | 68.62 132 |
|
v8 | | | 68.77 106 | 73.50 108 | 63.26 100 | 63.74 114 | 64.47 95 | 74.22 112 | 42.07 127 | 67.30 129 | 64.89 85 | 82.08 71 | 90.23 73 | 56.50 92 | 71.85 146 | 66.57 155 | 78.14 113 | 72.02 110 |
|
NR-MVSNet | | | 68.66 107 | 76.15 79 | 59.93 119 | 65.49 93 | 65.48 84 | 74.42 105 | 56.76 34 | 69.95 116 | 45.38 156 | 85.70 30 | 91.13 55 | 34.68 185 | 74.52 105 | 76.75 77 | 82.83 74 | 69.49 125 |
|
v17 | | | 68.55 108 | 73.23 114 | 63.08 103 | 63.67 118 | 63.84 106 | 74.05 114 | 42.28 124 | 66.34 136 | 63.93 91 | 81.91 75 | 89.83 81 | 56.50 92 | 71.97 140 | 66.55 156 | 78.08 117 | 72.18 108 |
|
USDC | | | 68.53 109 | 71.82 128 | 64.68 87 | 63.53 122 | 61.87 123 | 70.12 131 | 46.98 82 | 77.89 61 | 76.58 24 | 68.55 141 | 86.88 110 | 50.50 123 | 73.73 110 | 65.62 164 | 80.39 88 | 68.21 138 |
|
v16 | | | 68.33 110 | 73.03 116 | 62.86 108 | 63.57 120 | 63.83 107 | 73.98 115 | 42.30 123 | 65.58 142 | 62.94 93 | 81.82 76 | 89.37 85 | 56.36 96 | 71.91 141 | 66.52 157 | 77.99 120 | 72.17 109 |
|
v1neww | | | 68.32 111 | 72.82 118 | 63.07 104 | 63.73 115 | 63.12 113 | 74.23 108 | 40.99 144 | 67.21 130 | 64.83 88 | 82.09 69 | 90.20 74 | 56.49 94 | 71.86 143 | 66.61 152 | 78.14 113 | 68.65 130 |
|
v7new | | | 68.32 111 | 72.82 118 | 63.07 104 | 63.73 115 | 63.12 113 | 74.23 108 | 40.99 144 | 67.21 130 | 64.83 88 | 82.09 69 | 90.20 74 | 56.49 94 | 71.86 143 | 66.61 152 | 78.14 113 | 68.65 130 |
|
v6 | | | 68.32 111 | 72.82 118 | 63.07 104 | 63.73 115 | 63.11 115 | 74.23 108 | 40.99 144 | 67.21 130 | 64.86 87 | 82.11 68 | 90.19 76 | 56.51 91 | 71.86 143 | 66.61 152 | 78.14 113 | 68.66 129 |
|
IS_MVSNet | | | 68.20 114 | 74.41 101 | 60.96 115 | 68.55 77 | 64.36 100 | 71.47 124 | 48.33 73 | 70.11 113 | 43.30 163 | 80.90 83 | 74.54 165 | 47.19 141 | 81.25 71 | 77.97 69 | 86.94 45 | 71.76 113 |
|
Baseline_NR-MVSNet | | | 68.15 115 | 75.12 87 | 60.02 118 | 70.81 64 | 55.67 160 | 75.88 91 | 53.40 53 | 71.25 95 | 43.96 160 | 85.88 28 | 92.68 30 | 45.76 147 | 83.47 59 | 68.34 128 | 70.34 175 | 68.58 135 |
|
v18 | | | 67.99 116 | 72.63 122 | 62.57 110 | 63.32 125 | 63.64 109 | 73.58 121 | 42.07 127 | 64.75 145 | 62.64 95 | 81.36 80 | 89.01 88 | 56.02 97 | 71.57 149 | 66.41 161 | 77.80 123 | 71.69 114 |
|
Fast-Effi-MVS+ | | | 67.71 117 | 72.54 123 | 62.07 111 | 63.83 113 | 63.68 108 | 75.74 94 | 39.94 160 | 60.89 162 | 54.29 116 | 73.00 129 | 86.19 117 | 56.85 87 | 78.46 92 | 73.23 95 | 81.74 83 | 72.36 106 |
|
EPNet | | | 66.87 118 | 68.89 139 | 64.53 90 | 73.97 52 | 61.13 127 | 78.46 74 | 61.03 19 | 56.78 181 | 53.41 120 | 66.91 151 | 70.91 172 | 43.49 155 | 76.08 102 | 76.68 78 | 76.81 125 | 73.73 94 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
canonicalmvs | | | 66.37 119 | 74.37 103 | 57.04 133 | 65.89 91 | 65.06 88 | 62.58 162 | 42.55 119 | 76.82 66 | 46.87 149 | 67.33 148 | 86.38 114 | 45.49 149 | 76.77 97 | 71.85 101 | 78.87 102 | 76.35 81 |
|
QAPM | | | 66.36 120 | 72.76 121 | 58.90 124 | 59.57 149 | 65.01 89 | 64.05 158 | 41.17 143 | 73.09 85 | 56.82 109 | 69.42 138 | 77.78 154 | 55.07 103 | 73.00 122 | 72.07 100 | 76.71 126 | 78.96 68 |
|
V42 | | | 65.79 121 | 72.11 126 | 58.42 127 | 51.89 191 | 58.69 135 | 73.80 118 | 34.50 190 | 65.40 143 | 57.10 108 | 79.54 96 | 89.09 87 | 57.51 82 | 71.98 139 | 67.83 138 | 75.70 131 | 72.26 107 |
|
IterMVS-LS | | | 65.76 122 | 70.85 133 | 59.81 121 | 65.33 94 | 57.78 139 | 64.63 155 | 48.02 76 | 65.65 140 | 51.05 130 | 81.31 81 | 77.47 156 | 54.94 104 | 69.46 169 | 69.36 114 | 74.90 135 | 74.95 86 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PM-MVS | | | 65.66 123 | 71.25 132 | 59.14 123 | 58.92 158 | 54.88 166 | 73.66 120 | 38.55 172 | 66.12 138 | 49.91 136 | 69.87 136 | 86.97 109 | 60.61 60 | 76.30 100 | 74.75 88 | 73.19 152 | 69.83 121 |
|
UGNet | | | 65.61 124 | 74.79 93 | 54.91 142 | 54.54 184 | 68.20 73 | 70.97 127 | 48.21 74 | 67.14 134 | 41.67 168 | 74.15 121 | 80.65 140 | 36.10 180 | 79.39 88 | 77.99 68 | 77.95 121 | 76.01 82 |
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 |
DELS-MVS | | | 65.54 125 | 71.79 129 | 58.24 129 | 59.68 148 | 65.55 83 | 70.99 125 | 38.69 171 | 62.29 156 | 49.27 139 | 75.03 116 | 81.42 136 | 50.93 120 | 73.71 112 | 71.35 102 | 79.90 92 | 73.20 98 |
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 | | | 65.36 126 | 70.09 137 | 59.85 120 | 63.05 127 | 53.61 169 | 74.29 107 | 46.45 86 | 68.14 124 | 51.45 126 | 78.83 98 | 85.78 119 | 49.87 128 | 70.44 163 | 70.45 110 | 74.00 140 | 63.38 154 |
|
v148 | | | 64.92 127 | 70.58 135 | 58.32 128 | 59.89 146 | 57.09 146 | 66.04 146 | 35.27 189 | 69.11 119 | 60.66 99 | 79.57 95 | 90.93 60 | 53.91 109 | 69.81 168 | 62.22 179 | 74.14 138 | 65.31 147 |
|
FC-MVSNet-train | | | 64.87 128 | 74.76 95 | 53.33 145 | 65.24 97 | 58.05 138 | 69.69 134 | 41.92 133 | 70.99 99 | 32.62 189 | 85.75 29 | 88.23 91 | 32.10 205 | 77.61 95 | 74.41 89 | 78.43 108 | 68.25 137 |
|
pmmvs6 | | | 64.78 129 | 75.82 82 | 51.89 152 | 62.41 129 | 57.13 145 | 60.24 170 | 45.59 92 | 82.90 29 | 34.69 181 | 84.83 36 | 93.18 23 | 36.22 179 | 76.43 99 | 71.13 106 | 72.21 159 | 65.12 148 |
|
OpenMVS | | 60.79 16 | 64.42 130 | 69.72 138 | 58.23 130 | 61.63 134 | 62.17 122 | 64.11 157 | 37.54 180 | 67.17 133 | 55.71 114 | 65.89 158 | 74.89 163 | 52.67 116 | 72.20 137 | 68.29 130 | 77.73 124 | 77.39 78 |
|
no-one | | | 64.33 131 | 73.23 114 | 53.94 144 | 38.32 224 | 50.78 182 | 56.78 193 | 27.44 214 | 61.95 160 | 56.77 110 | 64.60 166 | 93.12 25 | 71.12 14 | 81.91 70 | 77.19 74 | 73.20 151 | 83.04 48 |
|
TransMVSNet (Re) | | | 63.49 132 | 73.86 105 | 51.39 158 | 64.26 108 | 56.07 157 | 61.17 167 | 42.23 125 | 78.81 60 | 34.80 179 | 85.94 26 | 90.63 67 | 34.35 192 | 72.73 128 | 67.98 136 | 71.50 162 | 64.84 149 |
|
DI_MVS_plusplus_trai | | | 63.43 133 | 67.54 142 | 58.63 125 | 62.34 130 | 58.06 137 | 65.75 150 | 42.15 126 | 63.05 152 | 53.28 121 | 75.88 111 | 75.92 160 | 50.18 125 | 68.04 173 | 64.20 170 | 78.07 119 | 67.65 139 |
|
Fast-Effi-MVS+-dtu | | | 63.22 134 | 65.55 148 | 60.49 117 | 61.24 136 | 64.70 93 | 74.15 113 | 53.24 55 | 51.46 197 | 49.67 137 | 58.03 198 | 78.42 151 | 48.05 136 | 72.03 138 | 71.14 105 | 76.60 129 | 63.09 155 |
|
MVS_Test | | | 62.58 135 | 67.46 143 | 56.89 135 | 59.52 152 | 55.90 158 | 64.94 153 | 38.83 168 | 57.08 179 | 56.55 112 | 76.53 107 | 84.49 126 | 47.45 137 | 66.95 175 | 62.01 180 | 74.04 139 | 69.27 127 |
|
MDA-MVSNet-bldmvs | | | 62.46 136 | 72.13 125 | 51.19 160 | 34.32 228 | 56.10 155 | 68.65 137 | 38.85 165 | 69.05 121 | 49.50 138 | 78.17 100 | 85.43 120 | 51.32 118 | 86.67 39 | 67.40 143 | 64.46 188 | 62.08 158 |
|
pm-mvs1 | | | 61.97 137 | 72.01 127 | 50.25 167 | 60.64 142 | 55.23 163 | 58.67 178 | 42.44 121 | 74.40 77 | 33.63 185 | 81.03 82 | 89.86 80 | 34.87 184 | 72.93 125 | 67.95 137 | 71.28 163 | 62.65 157 |
|
conf0.05thres1000 | | | 61.96 138 | 70.38 136 | 52.13 150 | 63.31 126 | 58.12 136 | 62.09 163 | 42.45 120 | 75.50 71 | 33.07 187 | 77.89 102 | 69.72 179 | 37.32 171 | 77.88 94 | 70.72 108 | 74.55 137 | 62.82 156 |
|
FMVSNet1 | | | 61.92 139 | 71.36 130 | 50.90 163 | 57.67 168 | 59.29 134 | 59.48 174 | 44.14 104 | 70.24 109 | 34.72 180 | 75.45 115 | 84.94 123 | 36.75 175 | 72.33 134 | 68.45 124 | 72.66 156 | 68.83 128 |
|
PVSNet_BlendedMVS | | | 61.75 140 | 65.07 153 | 57.87 131 | 56.27 171 | 60.99 129 | 65.81 148 | 43.75 107 | 51.27 200 | 54.08 117 | 62.12 179 | 78.84 148 | 50.67 121 | 71.49 150 | 63.91 172 | 76.64 127 | 66.86 141 |
|
PVSNet_Blended | | | 61.75 140 | 65.07 153 | 57.87 131 | 56.27 171 | 60.99 129 | 65.81 148 | 43.75 107 | 51.27 200 | 54.08 117 | 62.12 179 | 78.84 148 | 50.67 121 | 71.49 150 | 63.91 172 | 76.64 127 | 66.86 141 |
|
tfpnnormal | | | 61.41 142 | 71.33 131 | 49.83 168 | 61.73 133 | 54.90 165 | 58.52 179 | 41.24 141 | 75.20 72 | 32.00 197 | 82.13 67 | 87.87 95 | 35.63 183 | 72.75 126 | 66.30 162 | 69.87 176 | 60.14 163 |
|
IB-MVS | | 57.02 17 | 61.37 143 | 65.39 150 | 56.69 136 | 56.65 169 | 60.85 131 | 70.70 128 | 37.90 178 | 49.37 209 | 45.37 157 | 48.75 221 | 79.14 145 | 53.55 112 | 76.26 101 | 70.85 107 | 75.97 130 | 72.50 105 |
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 | | | 61.22 144 | 67.07 144 | 54.40 143 | 59.89 146 | 63.62 110 | 70.98 126 | 36.77 184 | 50.49 203 | 47.15 145 | 62.45 177 | 80.81 138 | 37.90 170 | 71.87 142 | 70.09 112 | 73.69 141 | 70.19 119 |
|
pmmvs4 | | | 61.12 145 | 64.61 156 | 57.04 133 | 60.88 139 | 52.15 178 | 70.59 129 | 44.82 100 | 61.35 161 | 46.91 148 | 72.08 131 | 73.27 168 | 46.79 145 | 65.06 178 | 67.76 139 | 72.28 157 | 60.58 162 |
|
Vis-MVSNet (Re-imp) | | | 60.99 146 | 67.78 141 | 53.06 147 | 64.66 102 | 53.49 170 | 67.40 141 | 49.52 66 | 68.55 122 | 28.00 212 | 79.53 97 | 71.41 171 | 33.08 201 | 75.30 104 | 71.28 104 | 75.69 132 | 54.91 188 |
|
PatchMatch-RL | | | 60.96 147 | 63.00 170 | 58.57 126 | 59.16 157 | 52.18 177 | 67.38 142 | 41.99 130 | 57.85 174 | 48.16 140 | 53.55 212 | 69.77 178 | 59.47 71 | 73.73 110 | 72.49 99 | 75.27 134 | 61.44 160 |
|
GA-MVS | | | 60.73 148 | 64.24 160 | 56.64 137 | 59.38 156 | 57.45 143 | 65.07 151 | 36.65 185 | 57.39 177 | 58.17 105 | 73.43 126 | 69.10 183 | 47.38 138 | 64.47 182 | 63.63 174 | 73.19 152 | 64.22 151 |
|
CVMVSNet | | | 60.45 149 | 63.72 163 | 56.63 138 | 54.82 182 | 53.75 168 | 68.41 139 | 41.95 132 | 55.07 185 | 48.03 141 | 58.08 197 | 68.67 184 | 55.09 102 | 69.14 171 | 68.34 128 | 71.51 161 | 72.97 103 |
|
FC-MVSNet-test | | | 60.28 150 | 70.83 134 | 47.96 187 | 54.69 183 | 47.12 194 | 68.06 140 | 41.68 137 | 71.42 92 | 23.73 222 | 84.70 38 | 77.41 157 | 28.92 208 | 82.33 68 | 73.08 96 | 70.68 170 | 59.77 165 |
|
EU-MVSNet | | | 59.77 151 | 66.07 146 | 52.42 149 | 47.81 203 | 51.86 180 | 62.98 161 | 32.28 205 | 62.08 158 | 47.10 146 | 59.94 189 | 83.42 133 | 53.08 115 | 70.06 167 | 63.19 175 | 71.26 165 | 71.96 111 |
|
diffmvs | | | 59.30 152 | 64.79 155 | 52.90 148 | 54.48 185 | 50.17 186 | 64.98 152 | 36.44 187 | 60.16 164 | 50.33 133 | 76.51 108 | 74.56 164 | 44.99 150 | 62.52 188 | 62.37 178 | 66.18 185 | 67.22 140 |
|
IterMVS | | | 59.24 153 | 64.45 157 | 53.16 146 | 50.98 194 | 61.29 126 | 66.51 144 | 32.85 201 | 58.17 170 | 46.31 152 | 72.58 130 | 70.23 174 | 54.26 107 | 64.81 181 | 60.24 183 | 68.04 182 | 63.81 153 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
view800 | | | 59.22 154 | 66.23 145 | 51.03 162 | 61.99 132 | 56.71 148 | 60.53 168 | 41.20 142 | 66.26 137 | 32.46 191 | 66.68 154 | 69.93 175 | 36.77 174 | 74.52 105 | 70.00 113 | 73.24 150 | 59.56 167 |
|
HyFIR lowres test | | | 59.15 155 | 62.28 172 | 55.49 140 | 52.42 189 | 62.59 120 | 71.76 123 | 39.74 161 | 50.25 205 | 41.92 166 | 62.88 174 | 69.16 182 | 55.85 98 | 62.77 187 | 67.18 144 | 71.27 164 | 61.11 161 |
|
thres600view7 | | | 58.87 156 | 65.91 147 | 50.66 164 | 61.27 135 | 56.32 152 | 59.88 172 | 40.63 156 | 64.88 144 | 32.10 196 | 64.82 164 | 69.83 177 | 36.72 176 | 72.99 123 | 72.55 98 | 73.34 148 | 59.97 164 |
|
view600 | | | 58.47 157 | 65.42 149 | 50.36 166 | 61.04 138 | 55.84 159 | 59.33 175 | 40.34 159 | 64.46 146 | 32.31 195 | 64.78 165 | 69.85 176 | 36.46 177 | 72.46 132 | 71.31 103 | 72.68 155 | 59.26 171 |
|
CMPMVS | | 45.32 18 | 58.10 158 | 65.24 152 | 49.76 169 | 47.88 202 | 46.86 197 | 48.16 222 | 32.82 202 | 58.06 171 | 61.35 96 | 59.64 190 | 80.00 141 | 47.27 140 | 70.15 165 | 64.10 171 | 61.08 192 | 77.85 76 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MDTV_nov1_ep13_2view | | | 58.09 159 | 63.54 165 | 51.74 154 | 50.13 198 | 46.56 198 | 66.95 143 | 33.41 198 | 63.52 149 | 58.77 103 | 74.84 118 | 84.10 129 | 43.12 156 | 65.95 177 | 54.69 196 | 58.04 198 | 55.13 187 |
|
CDS-MVSNet | | | 57.90 160 | 63.57 164 | 51.28 159 | 62.30 131 | 53.17 171 | 64.70 154 | 51.61 62 | 57.41 176 | 32.75 188 | 63.73 170 | 70.53 173 | 27.12 211 | 72.49 129 | 73.02 97 | 69.22 179 | 54.68 190 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
FMVSNet2 | | | 57.80 161 | 65.39 150 | 48.94 180 | 55.88 173 | 57.61 140 | 57.26 190 | 42.37 122 | 58.21 169 | 33.19 186 | 68.36 143 | 75.55 161 | 34.58 186 | 66.91 176 | 64.55 168 | 70.38 172 | 66.56 143 |
|
tfpn | | | 57.74 162 | 63.03 169 | 51.58 157 | 62.87 128 | 57.28 144 | 61.53 166 | 41.99 130 | 67.67 125 | 32.52 190 | 68.13 144 | 43.08 230 | 36.94 173 | 76.07 103 | 69.31 115 | 73.62 142 | 59.68 166 |
|
thres400 | | | 57.25 163 | 63.73 162 | 49.70 170 | 60.19 145 | 54.95 164 | 58.16 180 | 39.60 162 | 62.42 155 | 31.98 199 | 62.33 178 | 69.20 181 | 35.96 181 | 70.07 166 | 68.03 135 | 72.28 157 | 59.12 172 |
|
tfpn_n400 | | | 57.07 164 | 64.44 158 | 48.48 183 | 59.55 150 | 52.25 175 | 57.99 187 | 38.85 165 | 71.25 95 | 29.07 208 | 65.20 161 | 63.07 195 | 34.41 189 | 73.99 107 | 67.52 141 | 70.99 167 | 57.83 174 |
|
tfpnconf | | | 57.07 164 | 64.44 158 | 48.48 183 | 59.55 150 | 52.25 175 | 57.99 187 | 38.85 165 | 71.25 95 | 29.07 208 | 65.20 161 | 63.07 195 | 34.41 189 | 73.99 107 | 67.52 141 | 70.99 167 | 57.83 174 |
|
gm-plane-assit | | | 56.76 166 | 57.64 186 | 55.73 139 | 66.01 89 | 55.45 162 | 74.96 99 | 30.54 210 | 73.71 82 | 56.04 113 | 81.81 77 | 30.91 237 | 43.83 153 | 58.77 200 | 54.71 195 | 63.02 190 | 48.13 207 |
|
MIMVSNet1 | | | 56.72 167 | 68.69 140 | 42.76 202 | 46.70 208 | 42.81 204 | 69.13 136 | 30.52 211 | 81.01 44 | 32.00 197 | 74.82 119 | 91.10 56 | 26.83 213 | 73.98 109 | 64.72 167 | 51.40 210 | 52.38 193 |
|
tfpnview11 | | | 56.69 168 | 63.86 161 | 48.33 186 | 59.46 153 | 52.35 174 | 57.85 189 | 38.80 169 | 68.21 123 | 29.07 208 | 65.20 161 | 63.07 195 | 34.36 191 | 73.21 116 | 68.72 118 | 70.44 171 | 56.28 183 |
|
EPNet_dtu | | | 56.63 169 | 60.77 179 | 51.80 153 | 55.47 178 | 44.63 199 | 69.83 133 | 38.74 170 | 50.27 204 | 47.64 142 | 58.01 199 | 72.27 169 | 33.71 198 | 68.60 172 | 67.72 140 | 65.39 186 | 63.86 152 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
GBi-Net | | | 56.54 170 | 63.26 166 | 48.70 181 | 55.88 173 | 57.61 140 | 57.26 190 | 41.75 134 | 49.06 210 | 32.37 192 | 61.81 181 | 67.02 186 | 34.58 186 | 72.33 134 | 68.45 124 | 70.38 172 | 66.56 143 |
|
test1 | | | 56.54 170 | 63.26 166 | 48.70 181 | 55.88 173 | 57.61 140 | 57.26 190 | 41.75 134 | 49.06 210 | 32.37 192 | 61.81 181 | 67.02 186 | 34.58 186 | 72.33 134 | 68.45 124 | 70.38 172 | 66.56 143 |
|
gg-mvs-nofinetune | | | 56.45 172 | 61.04 176 | 51.10 161 | 63.42 124 | 49.40 189 | 53.71 204 | 52.52 58 | 74.77 74 | 46.93 147 | 77.31 105 | 53.88 211 | 26.42 215 | 62.51 189 | 57.81 188 | 63.60 189 | 51.57 197 |
|
thres200 | | | 56.35 173 | 62.36 171 | 49.34 172 | 58.87 159 | 56.32 152 | 55.91 194 | 40.63 156 | 58.51 167 | 31.34 200 | 58.81 196 | 67.31 185 | 35.96 181 | 72.99 123 | 65.51 165 | 73.34 148 | 57.07 179 |
|
MS-PatchMatch | | | 56.31 174 | 60.22 182 | 51.73 155 | 60.53 144 | 55.53 161 | 63.41 159 | 37.18 181 | 51.34 199 | 37.44 172 | 60.53 186 | 62.19 199 | 45.52 148 | 64.25 183 | 63.17 176 | 66.33 184 | 64.56 150 |
|
tfpn1000 | | | 56.13 175 | 63.18 168 | 47.91 188 | 58.34 166 | 53.03 172 | 58.87 177 | 38.14 173 | 65.64 141 | 27.09 213 | 65.41 160 | 59.49 207 | 33.41 200 | 73.14 117 | 69.08 116 | 71.63 160 | 56.46 182 |
|
conf200view11 | | | 56.07 176 | 61.85 173 | 49.32 174 | 58.57 160 | 56.49 149 | 58.01 182 | 40.73 150 | 53.23 188 | 30.91 203 | 56.41 201 | 66.40 190 | 34.18 193 | 73.03 119 | 68.06 131 | 73.54 143 | 59.36 168 |
|
tfpn200view9 | | | 56.07 176 | 61.85 173 | 49.34 172 | 58.57 160 | 56.48 151 | 58.01 182 | 40.72 152 | 53.23 188 | 31.01 201 | 56.41 201 | 66.40 190 | 34.18 193 | 73.02 121 | 68.06 131 | 73.53 145 | 59.35 170 |
|
tfpn111 | | | 55.56 178 | 60.91 178 | 49.32 174 | 58.57 160 | 56.49 149 | 58.01 182 | 40.73 150 | 53.23 188 | 30.91 203 | 49.82 218 | 66.40 190 | 34.18 193 | 73.03 119 | 68.06 131 | 73.54 143 | 59.36 168 |
|
tpmp4_e23 | | | 55.21 179 | 55.01 195 | 55.44 141 | 61.24 136 | 53.77 167 | 69.57 135 | 43.81 106 | 55.88 183 | 51.16 129 | 60.15 187 | 45.66 224 | 44.99 150 | 59.13 199 | 53.13 200 | 61.88 191 | 57.35 177 |
|
FMVSNet3 | | | 54.77 180 | 60.73 180 | 47.81 189 | 54.29 186 | 56.88 147 | 55.89 195 | 41.75 134 | 49.06 210 | 32.37 192 | 61.81 181 | 67.02 186 | 33.67 199 | 62.88 186 | 61.96 181 | 68.88 180 | 65.53 146 |
|
thres100view900 | | | 53.88 181 | 59.19 183 | 47.68 190 | 58.57 160 | 52.74 173 | 54.45 200 | 38.07 175 | 53.23 188 | 31.01 201 | 56.41 201 | 66.40 190 | 32.80 202 | 65.03 179 | 64.43 169 | 71.18 166 | 56.10 184 |
|
CR-MVSNet | | | 53.82 182 | 55.40 193 | 51.98 151 | 51.57 193 | 50.23 184 | 45.00 225 | 44.97 97 | 46.90 217 | 52.60 123 | 67.91 145 | 46.99 221 | 48.37 132 | 59.15 197 | 59.53 185 | 69.38 178 | 57.07 179 |
|
conf0.01 | | | 53.73 183 | 57.58 187 | 49.24 177 | 58.35 165 | 56.17 154 | 58.01 182 | 40.65 154 | 53.23 188 | 30.80 206 | 51.96 214 | 43.35 229 | 34.18 193 | 72.49 129 | 68.06 131 | 73.43 146 | 57.80 176 |
|
test20.03 | | | 53.49 184 | 60.95 177 | 44.78 199 | 64.73 101 | 47.25 193 | 61.58 165 | 43.30 114 | 65.86 139 | 22.85 223 | 66.87 153 | 79.85 142 | 22.99 217 | 62.38 190 | 56.95 190 | 53.25 206 | 47.46 208 |
|
MVSTER | | | 53.08 185 | 56.39 190 | 49.21 179 | 47.19 205 | 51.08 181 | 60.14 171 | 31.74 207 | 40.63 228 | 38.97 171 | 55.78 204 | 46.74 222 | 42.47 159 | 67.29 174 | 62.99 177 | 74.73 136 | 70.23 118 |
|
CHOSEN 1792x2688 | | | 52.99 186 | 56.91 189 | 48.42 185 | 47.32 204 | 50.10 187 | 64.18 156 | 33.85 195 | 45.46 222 | 36.95 174 | 55.20 207 | 66.49 189 | 51.20 119 | 59.28 195 | 59.81 184 | 57.01 201 | 61.99 159 |
|
conf0.002 | | | 52.78 187 | 55.83 191 | 49.22 178 | 58.28 167 | 56.09 156 | 58.01 182 | 40.64 155 | 53.23 188 | 30.79 207 | 50.10 217 | 36.15 234 | 34.18 193 | 72.40 133 | 65.72 163 | 73.41 147 | 57.11 178 |
|
CostFormer | | | 52.59 188 | 55.14 194 | 49.61 171 | 52.72 188 | 50.40 183 | 66.28 145 | 33.78 196 | 52.85 194 | 43.43 161 | 66.30 155 | 51.37 213 | 41.78 162 | 54.92 212 | 51.18 205 | 59.68 194 | 58.98 173 |
|
testgi | | | 51.94 189 | 61.37 175 | 40.94 206 | 58.38 164 | 47.03 195 | 65.88 147 | 30.49 212 | 70.87 102 | 22.64 224 | 57.53 200 | 87.59 98 | 18.30 223 | 63.01 185 | 54.32 197 | 49.93 213 | 49.27 201 |
|
tfpn_ndepth | | | 51.52 190 | 57.21 188 | 44.88 197 | 54.05 187 | 52.14 179 | 53.58 205 | 37.07 182 | 55.55 184 | 24.73 218 | 47.12 223 | 56.92 209 | 28.92 208 | 69.22 170 | 64.80 166 | 70.94 169 | 54.74 189 |
|
tpm cat1 | | | 50.98 191 | 51.28 205 | 50.62 165 | 55.74 176 | 49.92 188 | 63.13 160 | 38.12 174 | 52.38 196 | 47.61 143 | 60.11 188 | 44.51 226 | 44.86 152 | 51.31 222 | 47.49 215 | 54.25 205 | 53.24 192 |
|
RPMNet | | | 50.92 192 | 50.32 208 | 51.62 156 | 50.25 197 | 50.23 184 | 59.16 176 | 46.70 85 | 46.90 217 | 42.39 164 | 48.97 220 | 37.23 231 | 41.78 162 | 57.30 208 | 56.18 192 | 69.44 177 | 55.43 186 |
|
pmmvs5 | | | 50.64 193 | 58.01 184 | 42.05 203 | 47.01 207 | 43.67 202 | 49.27 218 | 29.43 213 | 50.77 202 | 33.83 184 | 68.69 140 | 76.16 159 | 27.82 210 | 57.53 207 | 57.07 189 | 64.95 187 | 52.18 194 |
|
PatchT | | | 50.55 194 | 53.55 201 | 47.05 194 | 37.59 227 | 42.26 206 | 50.55 215 | 37.56 179 | 46.37 219 | 52.60 123 | 66.91 151 | 43.54 228 | 48.37 132 | 59.15 197 | 59.53 185 | 55.62 203 | 57.07 179 |
|
Anonymous20231206 | | | 50.28 195 | 57.94 185 | 41.35 205 | 55.45 179 | 43.65 203 | 58.06 181 | 34.12 194 | 62.02 159 | 24.25 221 | 59.33 191 | 79.80 143 | 24.49 216 | 59.55 193 | 54.28 198 | 51.74 209 | 46.94 210 |
|
thresconf0.02 | | | 49.98 196 | 53.83 199 | 45.48 196 | 56.47 170 | 49.38 190 | 52.01 210 | 36.49 186 | 63.51 150 | 28.04 211 | 49.82 218 | 36.72 233 | 32.63 203 | 64.84 180 | 60.66 182 | 67.22 183 | 51.91 196 |
|
dps | | | 49.71 197 | 51.97 203 | 47.07 193 | 52.37 190 | 47.00 196 | 53.02 208 | 40.52 158 | 44.91 223 | 41.23 169 | 64.55 167 | 44.27 227 | 40.12 166 | 57.71 206 | 51.97 203 | 55.14 204 | 53.41 191 |
|
MDTV_nov1_ep13 | | | 49.60 198 | 51.57 204 | 47.31 191 | 46.28 209 | 44.61 200 | 59.82 173 | 30.96 208 | 48.80 214 | 50.20 134 | 59.26 193 | 52.38 212 | 38.56 167 | 56.20 210 | 49.70 210 | 58.04 198 | 50.01 199 |
|
LP | | | 49.44 199 | 55.77 192 | 42.05 203 | 38.31 225 | 42.61 205 | 51.74 211 | 36.31 188 | 58.35 168 | 40.36 170 | 68.52 142 | 60.77 204 | 37.08 172 | 58.27 204 | 51.76 204 | 48.51 214 | 50.13 198 |
|
PatchmatchNet | | | 48.67 200 | 50.10 209 | 46.99 195 | 48.29 201 | 41.00 207 | 55.54 196 | 38.94 164 | 51.38 198 | 45.15 158 | 63.22 172 | 48.45 216 | 42.83 157 | 53.80 218 | 48.50 213 | 51.19 212 | 44.37 212 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
DWT-MVSNet_training | | | 48.57 201 | 47.93 217 | 49.31 176 | 51.79 192 | 48.05 192 | 61.84 164 | 34.33 193 | 41.94 226 | 43.42 162 | 50.35 215 | 34.74 236 | 47.30 139 | 52.62 219 | 52.08 201 | 57.20 200 | 55.74 185 |
|
new-patchmatchnet | | | 47.33 202 | 60.49 181 | 31.99 224 | 55.69 177 | 33.86 224 | 36.84 234 | 33.31 199 | 72.36 87 | 14.33 235 | 80.09 90 | 92.14 35 | 13.27 231 | 63.54 184 | 40.09 225 | 38.51 227 | 41.32 219 |
|
tpm | | | 46.67 203 | 49.20 214 | 43.72 200 | 49.60 199 | 36.60 220 | 53.93 203 | 26.84 215 | 52.70 195 | 58.05 106 | 69.04 139 | 47.96 217 | 30.06 207 | 48.33 226 | 42.76 220 | 43.88 221 | 47.01 209 |
|
pmmvs3 | | | 46.64 204 | 54.13 198 | 37.90 213 | 31.23 233 | 40.68 208 | 49.83 217 | 15.34 231 | 46.31 220 | 36.34 176 | 53.15 213 | 74.40 166 | 36.36 178 | 58.43 202 | 56.64 191 | 58.32 197 | 49.29 200 |
|
TAMVS | | | 46.64 204 | 53.62 200 | 38.49 211 | 49.56 200 | 36.87 217 | 53.16 207 | 25.76 217 | 56.33 182 | 22.55 226 | 60.72 184 | 61.80 201 | 27.12 211 | 59.50 194 | 58.33 187 | 52.79 207 | 41.82 218 |
|
test-LLR | | | 46.01 206 | 45.06 225 | 47.11 192 | 59.39 154 | 36.72 218 | 51.28 212 | 40.95 147 | 36.41 233 | 34.45 182 | 46.14 225 | 47.02 219 | 38.00 168 | 51.78 220 | 48.53 211 | 58.60 195 | 48.84 203 |
|
MIMVSNet | | | 45.83 207 | 53.46 202 | 36.94 214 | 45.38 214 | 39.50 210 | 52.20 209 | 30.68 209 | 57.09 178 | 24.53 220 | 55.22 206 | 71.54 170 | 21.74 219 | 55.81 211 | 51.08 206 | 47.11 217 | 43.96 213 |
|
test0.0.03 1 | | | 45.40 208 | 49.55 212 | 40.57 208 | 59.39 154 | 44.36 201 | 53.37 206 | 40.95 147 | 47.14 216 | 19.23 229 | 45.49 227 | 60.24 205 | 19.24 221 | 54.82 213 | 51.98 202 | 51.21 211 | 42.82 215 |
|
PMMVS | | | 45.37 209 | 49.29 213 | 40.79 207 | 27.75 234 | 35.07 222 | 50.88 214 | 19.88 226 | 39.27 230 | 35.78 177 | 50.11 216 | 61.29 202 | 42.04 160 | 54.13 217 | 55.95 193 | 68.43 181 | 49.19 202 |
|
test1235678 | | | 44.92 210 | 54.19 196 | 34.11 219 | 41.53 217 | 37.95 214 | 54.27 201 | 23.09 221 | 53.64 186 | 22.14 227 | 53.92 209 | 84.05 130 | 16.41 226 | 60.66 191 | 50.30 208 | 47.20 215 | 38.84 222 |
|
testmv | | | 44.91 211 | 54.17 197 | 34.11 219 | 41.52 218 | 37.93 215 | 54.27 201 | 23.09 221 | 53.61 187 | 22.14 227 | 53.89 210 | 84.00 131 | 16.41 226 | 60.64 192 | 50.29 209 | 47.20 215 | 38.83 223 |
|
MVS-HIRNet | | | 44.56 212 | 45.52 223 | 43.44 201 | 40.98 219 | 31.03 229 | 39.52 233 | 36.96 183 | 42.80 225 | 44.37 159 | 53.80 211 | 60.04 206 | 41.85 161 | 47.97 228 | 41.08 223 | 56.99 202 | 41.95 217 |
|
test-mter | | | 44.18 213 | 47.60 218 | 40.18 209 | 33.20 229 | 39.03 211 | 55.28 197 | 13.91 233 | 39.07 231 | 36.63 175 | 48.09 222 | 49.52 214 | 41.12 164 | 54.55 214 | 50.91 207 | 60.97 193 | 52.03 195 |
|
EMVS | | | 43.85 214 | 49.91 210 | 36.77 216 | 45.46 213 | 32.70 226 | 44.09 227 | 25.33 218 | 57.88 173 | 26.62 214 | 58.99 195 | 61.14 203 | 42.77 158 | 70.26 164 | 38.52 230 | 36.38 229 | 29.87 231 |
|
E-PMN | | | 43.83 215 | 49.81 211 | 36.84 215 | 46.09 211 | 31.86 228 | 42.77 229 | 25.85 216 | 57.76 175 | 25.53 215 | 55.50 205 | 62.47 198 | 43.77 154 | 70.78 161 | 39.51 227 | 37.04 228 | 30.79 230 |
|
tpmrst | | | 43.31 216 | 46.14 221 | 40.02 210 | 47.05 206 | 36.48 221 | 48.01 223 | 32.17 206 | 49.50 208 | 37.26 173 | 63.66 171 | 47.04 218 | 31.98 206 | 42.00 233 | 40.55 224 | 43.64 222 | 43.75 214 |
|
TESTMET0.1,1 | | | 41.79 217 | 45.06 225 | 37.97 212 | 31.32 232 | 36.72 218 | 51.28 212 | 14.17 232 | 36.41 233 | 34.45 182 | 46.14 225 | 47.02 219 | 38.00 168 | 51.78 220 | 48.53 211 | 58.60 195 | 48.84 203 |
|
testus | | | 41.61 218 | 50.54 207 | 31.20 226 | 38.11 226 | 38.92 212 | 49.10 219 | 17.60 228 | 48.25 215 | 25.05 216 | 41.45 229 | 79.34 144 | 13.18 232 | 58.28 203 | 47.10 216 | 44.17 220 | 40.41 220 |
|
testpf | | | 41.44 219 | 38.52 232 | 44.85 198 | 46.17 210 | 38.68 213 | 60.29 169 | 43.31 113 | 24.28 235 | 35.09 178 | 39.52 231 | 34.84 235 | 32.39 204 | 43.79 232 | 39.89 226 | 51.88 208 | 48.65 205 |
|
ADS-MVSNet | | | 40.61 220 | 46.31 219 | 33.96 221 | 40.70 220 | 30.42 230 | 40.42 231 | 33.44 197 | 58.01 172 | 30.87 205 | 63.05 173 | 54.48 210 | 22.67 218 | 44.35 231 | 39.23 229 | 35.64 230 | 34.64 226 |
|
CHOSEN 280x420 | | | 40.24 221 | 44.14 229 | 35.69 217 | 32.36 231 | 23.58 235 | 50.30 216 | 21.21 225 | 40.94 227 | 18.84 230 | 32.75 234 | 48.65 215 | 48.13 135 | 59.16 196 | 55.31 194 | 43.28 223 | 48.62 206 |
|
EPMVS | | | 40.11 222 | 44.96 227 | 34.44 218 | 41.55 216 | 32.65 227 | 41.74 230 | 32.39 204 | 49.89 207 | 24.83 217 | 64.44 168 | 46.38 223 | 26.57 214 | 44.75 230 | 39.47 228 | 39.59 225 | 37.16 224 |
|
FMVSNet5 | | | 39.83 223 | 45.08 224 | 33.71 222 | 39.24 221 | 39.56 209 | 48.77 220 | 23.55 220 | 39.45 229 | 24.55 219 | 33.73 233 | 44.57 225 | 20.97 220 | 58.27 204 | 54.23 199 | 45.16 218 | 45.77 211 |
|
1111 | | | 39.71 224 | 44.86 228 | 33.71 222 | 50.45 195 | 28.51 231 | 55.07 198 | 34.43 191 | 62.60 153 | 17.59 231 | 62.60 175 | 28.17 238 | 14.69 228 | 54.19 215 | 41.91 222 | 30.02 232 | 36.03 225 |
|
test12356 | | | 39.53 225 | 49.18 215 | 28.26 228 | 32.93 230 | 33.64 225 | 48.68 221 | 15.96 230 | 46.26 221 | 16.21 233 | 46.46 224 | 79.07 146 | 17.13 224 | 58.60 201 | 48.30 214 | 38.67 226 | 31.96 228 |
|
N_pmnet | | | 39.50 226 | 51.01 206 | 26.09 230 | 44.48 215 | 25.59 234 | 40.20 232 | 21.49 224 | 64.20 147 | 7.98 238 | 73.86 123 | 76.67 158 | 13.66 230 | 50.17 224 | 36.69 232 | 28.71 233 | 29.86 232 |
|
test2356 | | | 35.97 227 | 39.61 231 | 31.71 225 | 38.85 222 | 34.29 223 | 45.78 224 | 22.27 223 | 38.89 232 | 22.59 225 | 37.67 232 | 37.07 232 | 16.57 225 | 50.72 223 | 45.45 217 | 44.20 219 | 33.19 227 |
|
MVE | | 28.01 19 | 35.86 228 | 43.56 230 | 26.88 229 | 22.33 236 | 19.75 237 | 30.85 237 | 23.88 219 | 49.90 206 | 10.48 236 | 43.64 228 | 61.87 200 | 48.99 131 | 47.26 229 | 42.15 221 | 24.76 234 | 40.37 221 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
new_pmnet | | | 35.76 229 | 45.64 222 | 24.22 231 | 38.59 223 | 25.83 233 | 31.87 236 | 19.24 227 | 49.06 210 | 9.01 237 | 54.34 208 | 64.73 194 | 12.46 233 | 49.21 225 | 44.91 218 | 34.17 231 | 31.41 229 |
|
PMMVS2 | | | 34.11 230 | 48.55 216 | 17.26 232 | 25.45 235 | 20.72 236 | 35.08 235 | 16.26 229 | 58.71 166 | 4.16 240 | 59.22 194 | 78.40 152 | 3.65 234 | 57.24 209 | 38.31 231 | 18.94 235 | 27.28 233 |
|
GG-mvs-BLEND | | | 31.54 231 | 46.27 220 | 14.37 233 | 0.07 240 | 48.65 191 | 42.97 228 | 0.08 238 | 44.04 224 | 1.21 242 | 39.77 230 | 57.94 208 | 0.15 238 | 48.19 227 | 42.82 219 | 41.70 224 | 42.46 216 |
|
.test1245 | | | 31.52 232 | 33.91 233 | 28.73 227 | 50.45 195 | 28.51 231 | 55.07 198 | 34.43 191 | 62.60 153 | 17.59 231 | 62.60 175 | 28.17 238 | 14.69 228 | 54.19 215 | 0.54 235 | 0.15 239 | 0.77 236 |
|
test123 | | | 0.53 233 | 0.60 235 | 0.46 235 | 0.22 238 | 0.25 240 | 0.33 242 | 0.13 237 | 0.66 238 | 1.37 241 | 1.10 237 | 0.00 243 | 0.43 236 | 0.68 236 | 0.61 234 | 0.26 238 | 0.88 235 |
|
testmvs | | | 0.47 234 | 0.69 234 | 0.21 236 | 0.17 239 | 0.17 241 | 0.35 241 | 0.16 236 | 0.66 238 | 0.18 243 | 1.05 238 | 0.99 242 | 0.27 237 | 0.62 237 | 0.54 235 | 0.15 239 | 0.77 236 |
|
sosnet-low-res | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 243 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
sosnet | | | 0.00 235 | 0.00 236 | 0.00 237 | 0.00 241 | 0.00 242 | 0.00 243 | 0.00 239 | 0.00 240 | 0.00 244 | 0.00 239 | 0.00 243 | 0.00 239 | 0.00 238 | 0.00 237 | 0.00 241 | 0.00 238 |
|
ambc | | | | 79.96 60 | | 74.57 48 | 75.48 45 | 73.75 119 | | 80.32 50 | 72.34 37 | 78.46 99 | 92.41 32 | 59.05 72 | 80.24 85 | 73.95 92 | 75.41 133 | 78.85 69 |
|
MTAPA | | | | | | | | | | | 80.26 8 | | 90.53 72 | | | | | |
|
MTMP | | | | | | | | | | | 82.07 4 | | 91.00 58 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.05 240 | | | | | | | | | | |
|
tmp_tt | | | | | 7.47 234 | 8.89 237 | 3.32 239 | 4.35 239 | 1.14 235 | 15.58 237 | 15.76 234 | 8.50 236 | 5.90 241 | 2.00 235 | 20.02 234 | 21.51 233 | 12.70 236 | |
|
XVS | | | | | | 80.47 19 | 81.29 12 | 93.33 3 | | | 77.45 20 | | 90.19 76 | | | | 91.52 11 | |
|
X-MVStestdata | | | | | | 80.47 19 | 81.29 12 | 93.33 3 | | | 77.45 20 | | 90.19 76 | | | | 91.52 11 | |
|
abl_6 | | | | | 65.41 78 | 69.37 75 | 74.02 53 | 82.50 55 | 47.39 79 | 66.39 135 | 56.63 111 | 60.61 185 | 82.76 134 | 53.68 111 | | | 82.92 73 | 78.39 74 |
|
mPP-MVS | | | | | | 82.97 2 | | | | | | | 92.12 36 | | | | | |
|
NP-MVS | | | | | | | | | | 71.39 93 | | | | | | | | |
|
Patchmtry | | | | | | | 37.73 216 | 45.00 225 | 44.97 97 | | 52.60 123 | | | | | | | |
|
DeepMVS_CX | | | | | | | 8.52 238 | 9.75 238 | 3.19 234 | 16.70 236 | 5.02 239 | 23.06 235 | 19.33 240 | 18.69 222 | 13.75 235 | | 11.34 237 | 25.07 234 |
|