TDRefinement | | | 86.29 1 | 90.77 1 | 81.06 1 | 75.10 48 | 83.76 2 | 93.79 1 | 61.08 18 | 89.57 1 | 86.19 1 | 90.06 7 | 93.01 26 | 76.72 2 | 94.71 1 | 92.72 1 | 93.47 1 | 91.56 2 |
|
COLMAP_ROB | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 75.87 2 | 84.34 2 | 89.80 2 | 77.97 12 | 75.52 46 | 82.76 4 | 90.39 21 | 54.21 51 | 89.37 2 | 83.18 2 | 89.90 8 | 95.58 10 | 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 30 | 92.67 6 | 64.48 1 | 83.13 25 | 82.32 3 | 80.89 85 | 92.97 27 | 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 16 | 79.45 12 | 83.37 50 | 92.04 36 | 72.82 8 | 90.66 12 | 88.96 12 | 91.80 6 | 89.13 9 |
|
MP-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 83.50 5 | 86.11 18 | 80.45 3 | 82.58 5 | 80.60 24 | 92.68 5 | 63.48 3 | 81.43 39 | 80.21 9 | 81.95 73 | 90.76 62 | 72.86 6 | 90.14 19 | 89.30 11 | 90.92 19 | 88.59 19 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
ACMMP | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 83.17 6 | 86.75 10 | 79.01 8 | 80.11 24 | 82.01 7 | 92.29 11 | 60.35 25 | 82.20 33 | 78.32 16 | 80.59 86 | 93.14 23 | 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 88 | 91.44 46 | 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 16 | 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 32 | 91.77 12 | 60.76 22 | 84.43 14 | 76.49 25 | 81.58 80 | 93.50 18 | 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 30 | 79.79 6 | 82.59 4 | 73.90 55 | 92.42 9 | 62.39 11 | 84.54 13 | 80.21 9 | 79.86 92 | 90.74 63 | 70.63 17 | 90.01 21 | 89.71 8 | 90.48 21 | 86.49 33 |
|
HFP-MVS | | | 82.37 11 | 86.28 14 | 77.81 15 | 79.94 26 | 80.96 18 | 91.13 15 | 63.30 4 | 84.04 18 | 71.81 39 | 82.39 65 | 89.59 83 | 69.16 24 | 89.08 26 | 88.83 14 | 91.49 13 | 89.10 10 |
|
DeepC-MVS | | 73.80 3 | 82.34 12 | 86.87 7 | 77.06 19 | 78.62 35 | 84.34 1 | 90.30 23 | 63.54 2 | 83.10 26 | 71.30 43 | 86.91 22 | 90.54 70 | 67.12 33 | 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 13 | 85.00 32 | 79.19 7 | 80.73 15 | 80.86 21 | 91.68 13 | 62.59 9 | 82.55 30 | 75.53 29 | 73.88 127 | 92.28 33 | 73.74 5 | 90.07 20 | 87.65 19 | 90.87 20 | 87.74 24 |
|
ACMP | | 70.35 9 | 82.17 14 | 86.45 13 | 77.18 18 | 79.33 27 | 81.00 17 | 89.27 30 | 58.63 30 | 81.35 41 | 75.46 30 | 82.97 56 | 95.08 11 | 68.90 26 | 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 15 | 85.55 26 | 78.21 11 | 80.48 18 | 79.28 31 | 92.65 7 | 61.03 19 | 80.55 49 | 77.00 23 | 81.80 78 | 90.71 64 | 68.73 27 | 90.25 17 | 87.94 18 | 89.36 28 | 88.30 22 |
Skip Steuart: Steuart Systems R&D Blog. |
SMA-MVS | | | 82.15 16 | 85.93 20 | 77.74 16 | 80.13 23 | 80.25 26 | 91.01 16 | 60.61 23 | 85.54 8 | 78.61 15 | 83.21 53 | 86.96 115 | 65.95 37 | 88.10 32 | 87.59 20 | 90.11 22 | 89.83 5 |
|
SD-MVS | | | 82.13 17 | 86.80 8 | 76.67 20 | 80.36 21 | 80.66 22 | 89.48 27 | 56.93 34 | 82.50 31 | 67.55 68 | 87.05 18 | 91.40 49 | 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 23 | 63.57 125 | 70.27 71 | 86.48 40 | 62.99 7 | 89.00 4 | 80.32 7 | 86.25 25 | 91.04 56 | 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 | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 70.37 8 | 81.82 19 | 87.08 5 | 75.68 25 | 77.06 42 | 77.23 38 | 87.77 38 | 56.25 40 | 83.33 24 | 67.18 79 | 89.48 10 | 87.94 99 | 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 29 | 91.62 14 | 59.66 26 | 83.55 21 | 77.74 19 | 83.72 46 | 87.34 108 | 65.36 38 | 88.61 29 | 87.56 21 | 89.73 27 | 89.58 6 |
|
X-MVS | | | 81.61 21 | 84.73 34 | 77.97 12 | 80.31 22 | 81.29 12 | 93.53 2 | 62.50 10 | 81.41 40 | 77.45 20 | 72.04 138 | 90.19 75 | 62.50 53 | 90.57 13 | 88.87 13 | 91.54 10 | 88.73 15 |
|
OPM-MVS | | | 81.44 22 | 85.68 24 | 76.49 21 | 79.27 28 | 78.21 34 | 89.84 24 | 58.67 29 | 85.25 9 | 76.26 26 | 85.28 32 | 92.88 28 | 66.03 36 | 87.20 38 | 85.40 28 | 88.86 32 | 85.58 40 |
|
TSAR-MVS + MP. | | | 81.23 23 | 86.13 16 | 75.52 26 | 80.74 14 | 83.22 3 | 90.55 17 | 55.12 46 | 80.87 45 | 67.62 67 | 88.01 12 | 92.38 32 | 70.61 18 | 86.64 40 | 83.10 43 | 88.51 33 | 88.67 16 |
|
TSAR-MVS + ACMM | | | 81.20 24 | 86.92 6 | 74.52 30 | 77.60 38 | 82.29 5 | 84.41 47 | 62.95 8 | 82.99 27 | 64.03 92 | 87.71 13 | 89.17 86 | 71.98 11 | 88.19 31 | 88.10 17 | 86.18 52 | 89.95 4 |
|
APDe-MVS | | | 81.08 25 | 86.12 17 | 75.20 28 | 79.25 29 | 80.91 19 | 90.38 22 | 57.05 33 | 85.83 7 | 66.07 84 | 87.34 16 | 91.27 53 | 69.45 21 | 85.99 44 | 82.55 45 | 88.98 31 | 88.95 13 |
|
ESAPD | | | 81.01 26 | 85.18 28 | 76.15 22 | 78.58 36 | 80.64 23 | 89.77 26 | 57.92 32 | 81.66 38 | 73.45 33 | 86.84 23 | 89.80 82 | 69.33 23 | 85.40 46 | 82.91 44 | 87.87 39 | 89.01 12 |
|
APD-MVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 80.60 27 | 84.63 35 | 75.91 24 | 81.22 10 | 81.48 10 | 90.49 19 | 58.81 28 | 77.54 64 | 67.49 70 | 85.90 27 | 89.82 81 | 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++ | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 80.44 28 | 82.57 46 | 77.96 14 | 81.99 7 | 72.76 59 | 90.48 20 | 61.31 14 | 80.85 46 | 77.90 18 | 81.93 74 | 87.01 112 | 68.20 29 | 84.15 54 | 85.27 30 | 87.85 40 | 86.00 36 |
|
ACMH+ | | 67.97 10 | 80.15 29 | 86.16 15 | 73.14 39 | 73.82 54 | 76.41 41 | 83.59 49 | 54.82 49 | 87.35 5 | 70.86 47 | 86.98 21 | 96.27 4 | 66.50 34 | 89.17 25 | 83.39 40 | 89.26 29 | 83.56 47 |
|
OMC-MVS | | | 79.95 30 | 85.28 27 | 73.74 36 | 72.95 57 | 80.10 27 | 87.87 37 | 48.13 76 | 84.62 12 | 79.42 13 | 80.27 89 | 92.49 30 | 64.14 44 | 87.25 37 | 85.11 31 | 89.92 25 | 87.10 29 |
|
HSP-MVS | | | 79.66 31 | 84.23 38 | 74.34 32 | 78.92 33 | 81.86 9 | 90.55 17 | 60.49 24 | 80.19 51 | 69.08 58 | 85.12 34 | 90.92 60 | 62.99 50 | 81.15 75 | 78.00 68 | 83.99 63 | 92.37 1 |
|
DeepPCF-MVS | | 71.57 5 | 79.49 32 | 84.05 39 | 74.17 33 | 74.14 51 | 80.88 20 | 89.33 29 | 56.24 41 | 82.41 32 | 71.58 41 | 82.27 66 | 86.47 118 | 66.47 35 | 84.80 52 | 84.16 36 | 87.26 44 | 87.34 27 |
|
LS3D | | | 79.33 33 | 84.03 40 | 73.84 34 | 75.37 47 | 78.09 35 | 83.30 50 | 52.94 58 | 84.42 15 | 76.01 27 | 84.16 41 | 87.44 107 | 65.34 39 | 86.30 41 | 82.08 53 | 90.09 23 | 85.70 38 |
|
3Dnovator+ | | 72.94 4 | 78.78 34 | 83.05 43 | 73.80 35 | 70.70 69 | 81.34 11 | 88.33 34 | 56.01 42 | 81.33 42 | 72.87 37 | 78.06 105 | 81.15 144 | 63.83 46 | 87.39 36 | 85.82 26 | 91.06 18 | 86.28 35 |
|
UA-Net | | | 78.65 35 | 83.96 41 | 72.46 41 | 84.87 1 | 76.15 42 | 89.06 31 | 55.70 43 | 77.25 65 | 53.14 128 | 79.73 94 | 82.09 142 | 59.69 69 | 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 38 | 76.44 44 | 82.23 6 | 87.59 39 | 56.56 37 | 77.79 62 | 68.91 60 | 77.00 111 | 87.32 109 | 61.90 55 | 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 19 | 69.25 58 | 76.22 45 | 72.33 66 | 85.71 43 | 59.02 27 | 86.66 6 | 51.41 133 | 92.91 1 | 96.76 1 | 53.09 120 | 90.21 18 | 85.30 29 | 90.05 24 | 78.46 73 |
|
ACMH | | 66.19 11 | 78.12 38 | 84.55 36 | 70.63 49 | 69.62 75 | 72.40 65 | 80.77 68 | 46.43 88 | 89.24 3 | 77.99 17 | 87.42 15 | 95.83 8 | 62.95 51 | 86.27 42 | 78.24 67 | 86.00 55 | 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 57 | 74.77 29 | 80.92 13 | 69.60 72 | 88.87 33 | 56.32 39 | 74.03 82 | 71.03 45 | 83.67 47 | 87.68 102 | 64.75 42 | 83.70 58 | 81.85 54 | 86.71 47 | 82.73 49 |
|
NCCC | | | 77.82 40 | 80.72 56 | 74.43 31 | 79.24 30 | 75.72 45 | 88.06 35 | 56.36 38 | 79.61 56 | 73.22 35 | 67.75 152 | 87.05 111 | 63.09 49 | 85.62 45 | 84.00 37 | 86.62 48 | 85.30 42 |
|
CNVR-MVS | | | 77.79 41 | 81.57 50 | 73.38 37 | 78.37 37 | 75.91 43 | 87.97 36 | 55.11 47 | 79.41 57 | 70.98 46 | 74.70 125 | 86.43 119 | 61.77 56 | 85.10 51 | 83.73 39 | 86.10 54 | 85.68 39 |
|
WR-MVS_H | | | 77.56 42 | 85.88 21 | 67.86 61 | 80.54 17 | 74.32 52 | 83.23 51 | 61.78 12 | 83.47 22 | 47.46 151 | 91.81 5 | 95.84 7 | 50.50 129 | 90.44 16 | 84.37 33 | 83.63 66 | 80.89 59 |
|
RPSCF | | | 77.56 42 | 84.51 37 | 69.46 57 | 65.17 101 | 74.36 51 | 79.74 73 | 47.45 79 | 84.01 19 | 72.89 36 | 77.89 106 | 90.67 65 | 65.14 41 | 88.25 30 | 89.74 7 | 86.38 51 | 86.64 32 |
|
PS-CasMVS | | | 77.46 44 | 85.80 22 | 67.73 63 | 81.24 9 | 72.88 58 | 80.63 69 | 61.28 15 | 84.14 17 | 50.53 137 | 92.13 3 | 96.76 1 | 50.12 132 | 91.02 9 | 84.46 32 | 82.60 80 | 79.19 66 |
|
DTE-MVSNet | | | 77.28 45 | 84.87 33 | 68.42 59 | 82.94 3 | 72.70 61 | 81.60 62 | 61.78 12 | 85.03 10 | 51.40 134 | 92.11 4 | 96.00 5 | 49.42 135 | 89.73 23 | 82.52 47 | 83.39 70 | 75.98 83 |
|
SixPastTwentyTwo | | | 77.24 46 | 83.65 42 | 69.78 53 | 65.14 102 | 64.85 94 | 77.44 84 | 47.74 78 | 82.76 29 | 68.52 61 | 87.65 14 | 93.31 20 | 71.68 13 | 89.49 24 | 82.41 48 | 88.14 36 | 85.05 43 |
|
CDPH-MVS | | | 77.22 47 | 81.05 55 | 72.75 40 | 77.29 40 | 77.46 37 | 86.36 41 | 54.02 53 | 73.00 88 | 69.75 53 | 77.78 108 | 88.90 90 | 61.31 60 | 84.09 57 | 82.54 46 | 87.79 41 | 83.57 46 |
|
PEN-MVS | | | 77.06 48 | 85.05 29 | 67.74 62 | 82.29 6 | 72.59 62 | 80.86 67 | 61.03 19 | 84.66 11 | 50.08 140 | 92.19 2 | 96.59 3 | 49.12 136 | 89.83 22 | 82.35 49 | 83.06 73 | 77.14 79 |
|
CP-MVSNet | | | 77.01 49 | 85.04 30 | 67.65 64 | 81.16 11 | 72.72 60 | 80.54 70 | 61.18 16 | 82.09 34 | 50.41 138 | 90.81 6 | 95.89 6 | 50.03 133 | 90.86 10 | 84.30 35 | 82.56 82 | 78.65 72 |
|
CSCG | | | 76.95 50 | 82.08 48 | 70.97 45 | 73.32 56 | 78.35 33 | 81.08 65 | 47.19 81 | 83.47 22 | 69.82 52 | 80.44 87 | 87.19 110 | 64.59 43 | 81.01 78 | 77.26 74 | 89.83 26 | 86.84 30 |
|
CNLPA | | | 76.67 51 | 81.72 49 | 70.77 48 | 70.75 67 | 76.68 40 | 86.14 42 | 46.11 90 | 81.82 36 | 74.68 31 | 76.37 113 | 86.23 121 | 62.92 52 | 85.28 49 | 83.29 41 | 84.02 62 | 82.40 51 |
|
MSLP-MVS++ | | | 76.66 52 | 82.32 47 | 70.06 51 | 70.51 70 | 80.27 25 | 79.77 72 | 55.58 44 | 77.79 62 | 63.09 95 | 67.25 156 | 89.50 84 | 71.01 15 | 88.10 32 | 85.74 27 | 80.39 92 | 87.56 25 |
|
TSAR-MVS + COLMAP | | | 75.85 53 | 81.06 53 | 69.77 54 | 71.15 63 | 76.90 39 | 82.93 53 | 52.43 60 | 79.25 59 | 70.13 50 | 82.78 57 | 87.00 113 | 60.02 65 | 80.30 83 | 79.61 60 | 81.95 86 | 81.61 55 |
|
HQP-MVS | | | 75.81 54 | 78.91 64 | 72.18 42 | 77.41 39 | 75.38 47 | 84.75 44 | 53.35 55 | 76.12 69 | 73.32 34 | 69.48 143 | 88.07 97 | 57.76 80 | 79.42 87 | 78.44 64 | 86.48 49 | 85.50 41 |
|
PLC | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 64.88 15 | 75.76 55 | 80.22 58 | 70.57 50 | 70.46 71 | 77.75 36 | 82.01 59 | 48.84 70 | 80.74 48 | 70.85 48 | 71.32 140 | 84.82 131 | 63.69 47 | 84.73 53 | 82.35 49 | 87.54 42 | 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 62 | 70.86 46 | 67.63 82 | 74.09 53 | 83.17 52 | 44.75 103 | 81.82 36 | 80.83 5 | 65.61 168 | 88.04 98 | 61.58 57 | 83.21 64 | 80.12 57 | 87.17 45 | 81.82 54 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
PHI-MVS | | | 75.17 57 | 78.37 65 | 71.43 43 | 71.13 64 | 72.46 64 | 82.28 58 | 50.55 63 | 73.39 85 | 79.05 14 | 73.65 129 | 87.50 105 | 61.98 54 | 81.10 76 | 78.48 63 | 83.60 67 | 81.99 52 |
|
anonymousdsp | | | 74.76 58 | 82.59 45 | 65.63 79 | 45.61 222 | 61.13 131 | 89.06 31 | 32.58 212 | 74.11 81 | 59.55 104 | 84.06 43 | 94.12 17 | 75.24 3 | 88.94 27 | 86.95 25 | 91.74 7 | 88.81 14 |
|
AdaColmap | ![Method available as binary. binary](img/icon_binary.png) | | 74.73 59 | 77.57 70 | 71.40 44 | 76.90 43 | 75.76 44 | 84.54 46 | 53.08 57 | 76.20 68 | 66.64 83 | 66.06 166 | 78.16 161 | 61.32 59 | 85.37 48 | 82.20 52 | 85.95 56 | 79.27 65 |
|
v7n | | | 74.47 60 | 81.06 53 | 66.77 69 | 66.98 86 | 67.10 77 | 76.76 87 | 45.88 92 | 81.98 35 | 67.43 72 | 88.38 11 | 95.67 9 | 61.38 58 | 80.76 80 | 73.49 94 | 82.21 84 | 80.06 61 |
|
PCF-MVS | | 65.25 14 | 73.99 61 | 76.74 75 | 70.79 47 | 71.61 61 | 75.33 48 | 83.76 48 | 50.40 64 | 74.88 73 | 74.50 32 | 67.60 153 | 85.36 128 | 58.30 76 | 78.61 90 | 74.25 90 | 86.15 53 | 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 52 | 65.22 84 | 54.85 190 | 63.32 114 | 78.90 75 | 38.00 186 | 80.00 54 | 68.32 63 | 87.02 19 | 94.98 14 | 68.14 31 | 84.11 55 | 75.63 83 | 83.12 71 | 84.96 44 |
|
V4 | | | 73.95 62 | 81.44 51 | 65.22 84 | 54.86 189 | 63.31 115 | 78.89 76 | 38.00 186 | 80.03 53 | 68.29 64 | 87.02 19 | 95.00 12 | 68.15 30 | 84.11 55 | 75.62 84 | 83.12 71 | 84.95 45 |
|
MCST-MVS | | | 73.84 64 | 77.44 71 | 69.63 56 | 73.75 55 | 74.73 50 | 81.38 64 | 48.58 71 | 74.77 74 | 69.16 57 | 71.97 139 | 86.20 122 | 59.50 71 | 78.51 91 | 74.06 91 | 85.42 57 | 81.85 53 |
|
MVS_0304 | | | 73.74 65 | 77.16 73 | 69.74 55 | 74.24 50 | 73.47 56 | 84.70 45 | 49.62 65 | 62.26 164 | 67.27 76 | 75.87 116 | 87.57 104 | 57.49 85 | 81.20 74 | 79.50 61 | 85.10 58 | 80.27 60 |
|
v1.0 | | | 73.48 66 | 71.76 136 | 75.49 27 | 79.20 31 | 79.76 28 | 89.40 28 | 58.51 31 | 81.15 43 | 69.56 55 | 85.14 33 | 88.71 91 | 68.92 25 | 85.26 50 | 82.30 51 | 87.35 43 | 0.00 246 |
|
TSAR-MVS + GP. | | | 73.42 67 | 76.31 76 | 70.05 52 | 77.15 41 | 71.13 69 | 81.59 63 | 54.11 52 | 69.84 122 | 58.65 107 | 66.20 165 | 78.77 158 | 65.29 40 | 83.65 59 | 83.14 42 | 83.54 68 | 81.47 56 |
|
Gipuma | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | 73.40 68 | 79.27 61 | 66.55 73 | 63.64 124 | 59.35 138 | 70.28 134 | 45.92 91 | 83.79 20 | 71.78 40 | 84.04 44 | 93.07 25 | 68.69 28 | 87.90 34 | 76.76 77 | 78.98 104 | 69.96 127 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MVS_111021_HR | | | 72.37 69 | 76.12 79 | 68.00 60 | 68.55 78 | 64.30 106 | 82.93 53 | 48.98 69 | 74.25 79 | 65.39 85 | 73.59 130 | 84.11 135 | 59.48 72 | 82.61 67 | 78.38 65 | 82.66 79 | 75.59 86 |
|
TinyColmap | | | 71.85 70 | 76.11 80 | 66.87 68 | 66.07 91 | 65.34 89 | 74.35 109 | 49.30 68 | 79.93 55 | 75.93 28 | 75.66 118 | 87.74 101 | 54.72 110 | 80.66 82 | 70.42 114 | 80.85 90 | 73.02 108 |
|
TranMVSNet+NR-MVSNet | | | 71.66 71 | 79.23 63 | 62.83 112 | 72.54 59 | 65.64 85 | 74.77 107 | 55.27 45 | 75.91 70 | 45.50 162 | 89.55 9 | 94.25 15 | 45.96 154 | 82.74 66 | 77.03 76 | 82.96 75 | 69.48 133 |
|
MVS_111021_LR | | | 71.60 72 | 75.21 85 | 67.38 65 | 67.42 83 | 62.44 124 | 81.73 61 | 46.24 89 | 70.89 105 | 66.80 82 | 73.19 132 | 84.98 129 | 60.09 64 | 81.94 70 | 77.77 72 | 82.00 85 | 75.29 88 |
|
EG-PatchMatch MVS | | | 71.50 73 | 76.82 74 | 65.30 82 | 70.74 68 | 66.50 81 | 74.23 111 | 43.25 118 | 72.02 92 | 59.11 105 | 79.85 93 | 86.88 116 | 63.95 45 | 80.29 84 | 75.25 87 | 80.51 91 | 76.98 80 |
|
UniMVSNet (Re) | | | 71.29 74 | 78.14 66 | 63.30 102 | 70.29 72 | 66.57 80 | 75.98 92 | 54.74 50 | 70.20 115 | 46.20 160 | 85.08 35 | 93.21 21 | 48.19 140 | 82.50 68 | 78.33 66 | 84.40 60 | 71.08 124 |
|
v748 | | | 71.27 75 | 79.41 60 | 61.76 116 | 60.62 150 | 61.73 128 | 68.46 144 | 40.71 160 | 80.76 47 | 61.02 100 | 87.12 17 | 95.00 12 | 59.62 70 | 80.67 81 | 70.67 112 | 80.14 95 | 79.93 62 |
|
CLD-MVS | | | 71.24 76 | 78.12 67 | 63.20 104 | 74.03 52 | 71.60 67 | 82.82 55 | 32.91 209 | 74.23 80 | 69.32 56 | 79.65 95 | 91.54 44 | 47.02 149 | 81.22 73 | 79.01 62 | 73.09 162 | 69.63 129 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
CANet | | | 71.07 77 | 75.09 87 | 66.39 74 | 72.57 58 | 71.53 68 | 82.38 57 | 47.10 82 | 59.81 171 | 59.81 103 | 74.97 122 | 84.37 134 | 54.25 114 | 79.89 86 | 77.64 73 | 82.25 83 | 77.40 77 |
|
v1192 | | | 71.06 78 | 74.87 90 | 66.61 71 | 66.38 88 | 65.80 84 | 78.27 78 | 45.28 96 | 70.19 116 | 70.79 49 | 83.37 50 | 91.79 39 | 58.76 75 | 70.86 168 | 69.02 122 | 80.16 94 | 73.08 106 |
|
DU-MVS | | | 71.03 79 | 77.92 68 | 62.98 110 | 70.81 65 | 65.48 87 | 73.93 119 | 56.76 35 | 69.95 120 | 46.77 157 | 85.70 30 | 93.49 19 | 46.91 150 | 83.47 60 | 77.82 71 | 82.72 78 | 69.54 130 |
|
v1240 | | | 70.94 80 | 74.52 98 | 66.76 70 | 66.54 87 | 64.40 100 | 77.76 81 | 45.29 95 | 70.05 118 | 71.45 42 | 83.36 52 | 90.96 58 | 60.37 62 | 70.50 170 | 68.68 124 | 79.14 102 | 73.68 101 |
|
v1921920 | | | 70.82 81 | 74.46 100 | 66.58 72 | 66.33 89 | 64.35 105 | 77.72 82 | 45.07 98 | 70.39 109 | 71.18 44 | 83.15 54 | 90.62 67 | 59.97 66 | 70.90 166 | 68.43 132 | 79.19 101 | 73.39 103 |
|
UniMVSNet_NR-MVSNet | | | 70.82 81 | 77.44 71 | 63.11 105 | 71.75 60 | 66.02 83 | 73.93 119 | 55.00 48 | 70.90 104 | 46.77 157 | 86.68 24 | 91.54 44 | 46.91 150 | 81.07 77 | 76.32 81 | 84.28 61 | 69.54 130 |
|
PVSNet_Blended_VisFu | | | 70.70 83 | 73.62 107 | 67.28 67 | 63.53 127 | 72.96 57 | 77.97 79 | 52.10 61 | 63.65 154 | 62.66 97 | 71.14 141 | 73.46 174 | 63.55 48 | 79.35 89 | 75.34 86 | 83.90 64 | 79.43 64 |
|
v144192 | | | 70.68 84 | 74.40 102 | 66.34 75 | 65.94 93 | 64.38 102 | 77.63 83 | 45.18 97 | 69.97 119 | 70.11 51 | 82.70 60 | 90.77 61 | 59.84 68 | 71.43 160 | 68.46 128 | 79.31 100 | 73.08 106 |
|
v13 | | | 70.58 85 | 75.49 83 | 64.87 88 | 64.66 105 | 64.58 97 | 76.18 90 | 43.69 112 | 72.34 91 | 67.65 66 | 84.36 39 | 92.01 37 | 58.05 77 | 73.57 117 | 67.06 151 | 78.96 105 | 74.48 93 |
|
casdiffmvs1 | | | 70.58 85 | 74.72 95 | 65.75 77 | 71.33 62 | 68.44 73 | 81.94 60 | 43.71 111 | 73.30 86 | 64.64 91 | 77.09 110 | 89.14 87 | 54.94 108 | 72.66 135 | 66.50 166 | 77.91 126 | 75.92 84 |
|
FPMVS | | | 70.46 87 | 74.89 89 | 65.28 83 | 69.09 77 | 61.42 129 | 77.07 86 | 46.92 85 | 76.73 67 | 53.53 124 | 67.33 154 | 75.07 170 | 67.23 32 | 83.41 62 | 81.54 55 | 77.86 127 | 78.73 70 |
|
v1144 | | | 70.45 88 | 74.50 99 | 65.73 78 | 65.74 95 | 64.88 93 | 77.33 85 | 44.16 105 | 70.59 108 | 69.63 54 | 83.15 54 | 91.42 48 | 57.79 79 | 71.29 164 | 68.53 127 | 79.72 97 | 71.63 122 |
|
v12 | | | 70.39 89 | 75.25 84 | 64.73 89 | 64.60 107 | 64.47 98 | 76.00 91 | 43.55 114 | 71.92 93 | 67.51 69 | 84.15 42 | 91.88 38 | 57.83 78 | 73.32 119 | 67.00 152 | 78.87 106 | 74.02 98 |
|
v10 | | | 70.25 90 | 74.59 96 | 65.19 86 | 65.32 99 | 66.46 82 | 76.60 88 | 44.84 101 | 67.38 133 | 67.21 78 | 82.75 59 | 90.56 69 | 57.70 81 | 71.69 154 | 68.63 125 | 79.44 98 | 74.67 92 |
|
V9 | | | 70.20 91 | 75.02 88 | 64.58 91 | 64.49 108 | 64.36 103 | 75.80 96 | 43.40 115 | 71.53 94 | 67.35 75 | 83.95 45 | 91.73 41 | 57.63 83 | 73.04 124 | 66.96 154 | 78.79 108 | 73.61 102 |
|
Effi-MVS+-dtu | | | 70.10 92 | 73.76 106 | 65.82 76 | 70.23 73 | 74.92 49 | 79.47 74 | 44.49 104 | 56.98 187 | 54.34 119 | 64.26 178 | 84.78 132 | 59.97 66 | 80.96 79 | 80.38 56 | 86.44 50 | 74.05 97 |
|
v11 | | | 70.10 92 | 74.82 91 | 64.58 91 | 64.83 103 | 64.39 101 | 75.89 93 | 43.18 120 | 71.34 97 | 67.75 65 | 84.19 40 | 91.75 40 | 57.23 87 | 71.46 159 | 66.85 157 | 78.60 112 | 73.78 99 |
|
MAR-MVS | | | 70.00 94 | 72.28 128 | 67.34 66 | 69.89 74 | 72.57 63 | 80.09 71 | 49.49 67 | 60.28 170 | 69.03 59 | 59.29 201 | 80.79 146 | 54.68 111 | 78.39 93 | 76.00 82 | 80.87 89 | 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 95 | 74.77 93 | 64.41 94 | 64.39 109 | 64.25 107 | 75.59 98 | 43.25 118 | 71.12 102 | 67.14 80 | 83.65 48 | 91.58 43 | 57.40 86 | 72.75 132 | 66.90 156 | 78.70 110 | 73.15 105 |
|
Vis-MVSNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 69.95 96 | 77.69 69 | 60.91 119 | 60.67 148 | 66.71 78 | 77.94 80 | 48.58 71 | 69.10 124 | 45.78 161 | 80.21 90 | 83.58 139 | 53.41 119 | 82.92 65 | 80.11 58 | 79.08 103 | 81.21 57 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v7 | | | 69.81 97 | 73.94 104 | 65.00 87 | 65.33 97 | 65.07 90 | 76.60 88 | 43.66 113 | 67.36 134 | 67.25 77 | 82.76 58 | 90.57 68 | 57.70 81 | 71.69 154 | 68.63 125 | 79.44 98 | 71.52 123 |
|
v15 | | | 69.80 98 | 74.53 97 | 64.27 96 | 64.30 110 | 64.15 108 | 75.40 100 | 43.12 121 | 70.71 107 | 66.98 81 | 83.41 49 | 91.43 47 | 57.21 88 | 72.48 138 | 66.84 158 | 78.62 111 | 72.72 110 |
|
EPP-MVSNet | | | 69.51 99 | 76.17 77 | 61.74 117 | 68.38 80 | 66.60 79 | 71.77 125 | 46.98 83 | 73.60 84 | 41.79 173 | 82.06 72 | 69.65 187 | 52.51 123 | 83.41 62 | 79.94 59 | 89.02 30 | 77.94 75 |
|
3Dnovator | | 65.69 13 | 69.43 100 | 75.74 82 | 62.06 115 | 60.78 147 | 70.50 70 | 75.85 95 | 39.57 170 | 74.44 76 | 57.41 110 | 75.91 114 | 77.73 163 | 55.34 105 | 76.86 96 | 75.61 85 | 83.44 69 | 79.14 67 |
|
Effi-MVS+ | | | 69.04 101 | 73.01 117 | 64.40 95 | 67.20 84 | 64.83 95 | 74.87 106 | 43.97 107 | 63.33 157 | 60.90 101 | 73.06 133 | 85.79 125 | 55.61 103 | 73.58 116 | 76.41 80 | 83.84 65 | 74.09 96 |
|
v2v482 | | | 69.01 102 | 73.39 109 | 63.89 98 | 63.86 115 | 62.99 119 | 75.26 101 | 42.05 133 | 70.22 114 | 68.46 62 | 82.64 61 | 91.61 42 | 55.38 104 | 70.89 167 | 66.93 155 | 78.30 117 | 68.48 143 |
|
v1 | | | 68.98 103 | 73.38 110 | 63.84 99 | 64.12 112 | 62.97 120 | 74.95 105 | 41.52 143 | 70.28 112 | 67.47 71 | 82.49 62 | 91.37 50 | 56.59 92 | 71.43 160 | 66.51 165 | 78.41 114 | 68.62 139 |
|
MSDG | | | 68.98 103 | 73.31 113 | 63.92 97 | 67.08 85 | 68.27 74 | 75.41 99 | 40.77 156 | 67.61 131 | 64.89 86 | 75.75 117 | 78.96 155 | 53.70 116 | 76.72 98 | 73.95 92 | 81.71 88 | 71.93 119 |
|
v1141 | | | 68.97 105 | 73.38 110 | 63.83 100 | 64.11 113 | 62.97 120 | 74.96 102 | 41.52 143 | 70.29 110 | 67.36 74 | 82.47 63 | 91.37 50 | 56.59 92 | 71.43 160 | 66.49 168 | 78.41 114 | 68.61 141 |
|
divwei89l23v2f112 | | | 68.97 105 | 73.38 110 | 63.83 100 | 64.11 113 | 62.97 120 | 74.96 102 | 41.52 143 | 70.29 110 | 67.39 73 | 82.47 63 | 91.37 50 | 56.59 92 | 71.42 163 | 66.50 166 | 78.40 116 | 68.62 139 |
|
casdiffmvs | | | 68.96 107 | 71.83 133 | 65.61 80 | 67.84 81 | 67.61 76 | 80.93 66 | 48.34 73 | 62.61 159 | 63.42 94 | 72.75 135 | 87.50 105 | 54.35 112 | 71.06 165 | 67.00 152 | 78.77 109 | 74.30 94 |
|
v8 | | | 68.77 108 | 73.50 108 | 63.26 103 | 63.74 118 | 64.47 98 | 74.22 115 | 42.07 131 | 67.30 135 | 64.89 86 | 82.08 71 | 90.23 72 | 56.50 96 | 71.85 153 | 66.57 162 | 78.14 118 | 72.02 117 |
|
NR-MVSNet | | | 68.66 109 | 76.15 78 | 59.93 123 | 65.49 96 | 65.48 87 | 74.42 108 | 56.76 35 | 69.95 120 | 45.38 163 | 85.70 30 | 91.13 54 | 34.68 194 | 74.52 107 | 76.75 78 | 82.83 77 | 69.49 132 |
|
v17 | | | 68.55 110 | 73.23 114 | 63.08 106 | 63.67 123 | 63.84 109 | 74.05 117 | 42.28 128 | 66.34 142 | 63.93 93 | 81.91 75 | 89.83 80 | 56.50 96 | 71.97 147 | 66.55 163 | 78.08 122 | 72.18 115 |
|
USDC | | | 68.53 111 | 71.82 134 | 64.68 90 | 63.53 127 | 61.87 127 | 70.12 135 | 46.98 83 | 77.89 61 | 76.58 24 | 68.55 147 | 86.88 116 | 50.50 129 | 73.73 113 | 65.62 173 | 80.39 92 | 68.21 145 |
|
v16 | | | 68.33 112 | 73.03 116 | 62.86 111 | 63.57 125 | 63.83 110 | 73.98 118 | 42.30 127 | 65.58 148 | 62.94 96 | 81.82 76 | 89.37 85 | 56.36 100 | 71.91 148 | 66.52 164 | 77.99 124 | 72.17 116 |
|
v1neww | | | 68.32 113 | 72.82 120 | 63.07 107 | 63.73 119 | 63.12 116 | 74.23 111 | 40.99 150 | 67.21 136 | 64.83 89 | 82.09 69 | 90.20 73 | 56.49 98 | 71.86 150 | 66.61 159 | 78.14 118 | 68.65 137 |
|
v7new | | | 68.32 113 | 72.82 120 | 63.07 107 | 63.73 119 | 63.12 116 | 74.23 111 | 40.99 150 | 67.21 136 | 64.83 89 | 82.09 69 | 90.20 73 | 56.49 98 | 71.86 150 | 66.61 159 | 78.14 118 | 68.65 137 |
|
v6 | | | 68.32 113 | 72.82 120 | 63.07 107 | 63.73 119 | 63.11 118 | 74.23 111 | 40.99 150 | 67.21 136 | 64.86 88 | 82.11 68 | 90.19 75 | 56.51 95 | 71.86 150 | 66.61 159 | 78.14 118 | 68.66 136 |
|
IS_MVSNet | | | 68.20 116 | 74.41 101 | 60.96 118 | 68.55 78 | 64.36 103 | 71.47 127 | 48.33 74 | 70.11 117 | 43.30 170 | 80.90 84 | 74.54 172 | 47.19 148 | 81.25 72 | 77.97 70 | 86.94 46 | 71.76 120 |
|
Baseline_NR-MVSNet | | | 68.15 117 | 75.12 86 | 60.02 122 | 70.81 65 | 55.67 168 | 75.88 94 | 53.40 54 | 71.25 98 | 43.96 167 | 85.88 28 | 92.68 29 | 45.76 155 | 83.47 60 | 68.34 133 | 70.34 185 | 68.58 142 |
|
v18 | | | 67.99 118 | 72.63 124 | 62.57 113 | 63.32 130 | 63.64 112 | 73.58 124 | 42.07 131 | 64.75 151 | 62.64 98 | 81.36 81 | 89.01 89 | 56.02 101 | 71.57 156 | 66.41 169 | 77.80 128 | 71.69 121 |
|
Fast-Effi-MVS+ | | | 67.71 119 | 72.54 125 | 62.07 114 | 63.83 116 | 63.68 111 | 75.74 97 | 39.94 167 | 60.89 169 | 54.29 120 | 73.00 134 | 86.19 123 | 56.85 89 | 78.46 92 | 73.23 95 | 81.74 87 | 72.36 113 |
|
thisisatest0515 | | | 66.95 120 | 72.29 127 | 60.72 120 | 56.37 178 | 56.05 165 | 71.08 128 | 38.81 176 | 67.59 132 | 53.26 127 | 78.21 103 | 79.79 151 | 60.11 63 | 75.69 104 | 73.02 97 | 84.69 59 | 75.66 85 |
|
EPNet | | | 66.87 121 | 68.89 147 | 64.53 93 | 73.97 53 | 61.13 131 | 78.46 77 | 61.03 19 | 56.78 188 | 53.41 125 | 66.91 159 | 70.91 179 | 43.49 162 | 76.08 102 | 76.68 79 | 76.81 131 | 73.73 100 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
canonicalmvs | | | 66.37 122 | 74.37 103 | 57.04 140 | 65.89 94 | 65.06 91 | 62.58 169 | 42.55 123 | 76.82 66 | 46.87 156 | 67.33 154 | 86.38 120 | 45.49 157 | 76.77 97 | 71.85 102 | 78.87 106 | 76.35 81 |
|
QAPM | | | 66.36 123 | 72.76 123 | 58.90 127 | 59.57 156 | 65.01 92 | 64.05 165 | 41.17 149 | 73.09 87 | 56.82 112 | 69.42 144 | 77.78 162 | 55.07 107 | 73.00 128 | 72.07 101 | 76.71 132 | 78.96 68 |
|
V42 | | | 65.79 124 | 72.11 131 | 58.42 131 | 51.89 201 | 58.69 141 | 73.80 121 | 34.50 199 | 65.40 149 | 57.10 111 | 79.54 97 | 89.09 88 | 57.51 84 | 71.98 146 | 67.83 144 | 75.70 138 | 72.26 114 |
|
IterMVS-LS | | | 65.76 125 | 70.85 140 | 59.81 125 | 65.33 97 | 57.78 146 | 64.63 162 | 48.02 77 | 65.65 146 | 51.05 136 | 81.31 82 | 77.47 164 | 54.94 108 | 69.46 179 | 69.36 119 | 74.90 143 | 74.95 90 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PM-MVS | | | 65.66 126 | 71.25 139 | 59.14 126 | 58.92 165 | 54.88 176 | 73.66 123 | 38.55 180 | 66.12 144 | 49.91 142 | 69.87 142 | 86.97 114 | 60.61 61 | 76.30 100 | 74.75 88 | 73.19 160 | 69.83 128 |
|
diffmvs1 | | | 65.64 127 | 72.40 126 | 57.76 138 | 62.01 137 | 59.11 140 | 68.90 141 | 41.83 138 | 68.05 129 | 53.87 123 | 79.46 99 | 88.52 93 | 48.19 140 | 70.08 174 | 65.99 171 | 71.76 168 | 75.58 87 |
|
UGNet | | | 65.61 128 | 74.79 92 | 54.91 149 | 54.54 194 | 68.20 75 | 70.97 131 | 48.21 75 | 67.14 140 | 41.67 174 | 74.15 126 | 80.65 147 | 36.10 189 | 79.39 88 | 77.99 69 | 77.95 125 | 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 | | | 65.54 129 | 71.79 135 | 58.24 133 | 59.68 155 | 65.55 86 | 70.99 129 | 38.69 179 | 62.29 163 | 49.27 145 | 75.03 121 | 81.42 143 | 50.93 126 | 73.71 115 | 71.35 103 | 79.90 96 | 73.20 104 |
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 | | | 65.36 130 | 70.09 145 | 59.85 124 | 63.05 132 | 53.61 180 | 74.29 110 | 46.45 87 | 68.14 128 | 51.45 132 | 78.83 101 | 85.78 126 | 49.87 134 | 70.44 171 | 70.45 113 | 74.00 148 | 63.38 162 |
|
v148 | | | 64.92 131 | 70.58 142 | 58.32 132 | 59.89 153 | 57.09 153 | 66.04 154 | 35.27 198 | 69.11 123 | 60.66 102 | 79.57 96 | 90.93 59 | 53.91 115 | 69.81 178 | 62.22 188 | 74.14 146 | 65.31 154 |
|
FC-MVSNet-train | | | 64.87 132 | 74.76 94 | 53.33 153 | 65.24 100 | 58.05 144 | 69.69 137 | 41.92 137 | 70.99 103 | 32.62 197 | 85.75 29 | 88.23 94 | 32.10 214 | 77.61 95 | 74.41 89 | 78.43 113 | 68.25 144 |
|
pmmvs6 | | | 64.78 133 | 75.82 81 | 51.89 160 | 62.41 134 | 57.13 152 | 60.24 180 | 45.59 94 | 82.90 28 | 34.69 189 | 84.83 36 | 93.18 22 | 36.22 188 | 76.43 99 | 71.13 107 | 72.21 167 | 65.12 155 |
|
OpenMVS | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | 60.79 16 | 64.42 134 | 69.72 146 | 58.23 134 | 61.63 140 | 62.17 125 | 64.11 164 | 37.54 190 | 67.17 139 | 55.71 117 | 65.89 167 | 74.89 171 | 52.67 122 | 72.20 144 | 68.29 135 | 77.73 129 | 77.39 78 |
|
Anonymous20240521 | | | 64.34 135 | 72.84 119 | 54.42 150 | 63.79 117 | 62.09 126 | 62.50 170 | 42.72 122 | 74.32 78 | 41.34 175 | 66.96 157 | 88.57 92 | 39.18 173 | 75.20 106 | 70.35 115 | 77.01 130 | 72.37 112 |
|
no-one | | | 64.33 136 | 73.23 114 | 53.94 152 | 38.32 234 | 50.78 193 | 56.78 203 | 27.44 223 | 61.95 167 | 56.77 113 | 64.60 175 | 93.12 24 | 71.12 14 | 81.91 71 | 77.19 75 | 73.20 159 | 83.04 48 |
|
diffmvs | | | 64.17 137 | 70.24 144 | 57.09 139 | 61.02 145 | 57.87 145 | 67.54 148 | 41.48 146 | 59.57 172 | 54.70 118 | 75.51 119 | 88.20 95 | 46.84 152 | 69.86 177 | 65.47 175 | 70.95 178 | 75.21 89 |
|
Anonymous20231211 | | | 63.69 138 | 72.86 118 | 53.00 156 | 63.72 122 | 60.25 137 | 60.33 178 | 40.96 153 | 72.49 89 | 38.91 179 | 81.77 79 | 88.17 96 | 37.60 178 | 73.30 120 | 68.01 141 | 76.47 136 | 66.06 152 |
|
TransMVSNet (Re) | | | 63.49 139 | 73.86 105 | 51.39 166 | 64.26 111 | 56.07 164 | 61.17 176 | 42.23 129 | 78.81 60 | 34.80 187 | 85.94 26 | 90.63 66 | 34.35 201 | 72.73 134 | 67.98 142 | 71.50 171 | 64.84 156 |
|
DI_MVS_plusplus_trai | | | 63.43 140 | 67.54 150 | 58.63 128 | 62.34 135 | 58.06 143 | 65.75 158 | 42.15 130 | 63.05 158 | 53.28 126 | 75.88 115 | 75.92 168 | 50.18 131 | 68.04 183 | 64.20 180 | 78.07 123 | 67.65 146 |
|
Fast-Effi-MVS+-dtu | | | 63.22 141 | 65.55 156 | 60.49 121 | 61.24 142 | 64.70 96 | 74.15 116 | 53.24 56 | 51.46 206 | 49.67 143 | 58.03 207 | 78.42 159 | 48.05 143 | 72.03 145 | 71.14 106 | 76.60 135 | 63.09 163 |
|
MVS_Test | | | 62.58 142 | 67.46 151 | 56.89 142 | 59.52 159 | 55.90 166 | 64.94 160 | 38.83 175 | 57.08 186 | 56.55 115 | 76.53 112 | 84.49 133 | 47.45 144 | 66.95 185 | 62.01 189 | 74.04 147 | 69.27 134 |
|
MDA-MVSNet-bldmvs | | | 62.46 143 | 72.13 130 | 51.19 168 | 34.32 238 | 56.10 162 | 68.65 143 | 38.85 172 | 69.05 125 | 49.50 144 | 78.17 104 | 85.43 127 | 51.32 124 | 86.67 39 | 67.40 149 | 64.46 197 | 62.08 166 |
|
pm-mvs1 | | | 61.97 144 | 72.01 132 | 50.25 175 | 60.64 149 | 55.23 172 | 58.67 188 | 42.44 125 | 74.40 77 | 33.63 193 | 81.03 83 | 89.86 79 | 34.87 193 | 72.93 131 | 67.95 143 | 71.28 172 | 62.65 165 |
|
conf0.05thres1000 | | | 61.96 145 | 70.38 143 | 52.13 158 | 63.31 131 | 58.12 142 | 62.09 172 | 42.45 124 | 75.50 71 | 33.07 195 | 77.89 106 | 69.72 186 | 37.32 180 | 77.88 94 | 70.72 111 | 74.55 145 | 62.82 164 |
|
FMVSNet1 | | | 61.92 146 | 71.36 137 | 50.90 171 | 57.67 175 | 59.29 139 | 59.48 184 | 44.14 106 | 70.24 113 | 34.72 188 | 75.45 120 | 84.94 130 | 36.75 184 | 72.33 141 | 68.45 129 | 72.66 164 | 68.83 135 |
|
PVSNet_BlendedMVS | | | 61.75 147 | 65.07 161 | 57.87 136 | 56.27 179 | 60.99 134 | 65.81 156 | 43.75 109 | 51.27 209 | 54.08 121 | 62.12 188 | 78.84 156 | 50.67 127 | 71.49 157 | 63.91 182 | 76.64 133 | 66.86 147 |
|
PVSNet_Blended | | | 61.75 147 | 65.07 161 | 57.87 136 | 56.27 179 | 60.99 134 | 65.81 156 | 43.75 109 | 51.27 209 | 54.08 121 | 62.12 188 | 78.84 156 | 50.67 127 | 71.49 157 | 63.91 182 | 76.64 133 | 66.86 147 |
|
tttt0517 | | | 61.44 149 | 63.85 169 | 58.62 129 | 55.20 188 | 55.61 169 | 68.80 142 | 38.02 184 | 55.70 191 | 50.01 141 | 66.93 158 | 48.90 222 | 56.69 90 | 73.84 112 | 71.10 108 | 82.99 74 | 74.89 91 |
|
tfpnnormal | | | 61.41 150 | 71.33 138 | 49.83 176 | 61.73 139 | 54.90 175 | 58.52 189 | 41.24 147 | 75.20 72 | 32.00 205 | 82.13 67 | 87.87 100 | 35.63 192 | 72.75 132 | 66.30 170 | 69.87 186 | 60.14 171 |
|
IB-MVS | | 57.02 17 | 61.37 151 | 65.39 158 | 56.69 143 | 56.65 176 | 60.85 136 | 70.70 132 | 37.90 188 | 49.37 218 | 45.37 164 | 48.75 230 | 79.14 153 | 53.55 118 | 76.26 101 | 70.85 110 | 75.97 137 | 72.50 111 |
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 | | | 61.22 152 | 67.07 152 | 54.40 151 | 59.89 153 | 63.62 113 | 70.98 130 | 36.77 194 | 50.49 212 | 47.15 152 | 62.45 186 | 80.81 145 | 37.90 177 | 71.87 149 | 70.09 116 | 73.69 149 | 70.19 126 |
|
pmmvs4 | | | 61.12 153 | 64.61 163 | 57.04 140 | 60.88 146 | 52.15 189 | 70.59 133 | 44.82 102 | 61.35 168 | 46.91 155 | 72.08 137 | 73.27 175 | 46.79 153 | 65.06 188 | 67.76 145 | 72.28 165 | 60.58 170 |
|
thisisatest0530 | | | 61.02 154 | 63.44 174 | 58.19 135 | 54.75 192 | 55.09 173 | 68.03 147 | 38.02 184 | 55.45 193 | 49.06 146 | 66.58 163 | 48.69 223 | 56.69 90 | 73.07 123 | 71.10 108 | 82.60 80 | 74.14 95 |
|
Vis-MVSNet (Re-imp) | | | 60.99 155 | 67.78 149 | 53.06 155 | 64.66 105 | 53.49 181 | 67.40 149 | 49.52 66 | 68.55 126 | 28.00 220 | 79.53 98 | 71.41 178 | 33.08 210 | 75.30 105 | 71.28 105 | 75.69 139 | 54.91 196 |
|
PatchMatch-RL | | | 60.96 156 | 63.00 179 | 58.57 130 | 59.16 164 | 52.18 188 | 67.38 150 | 41.99 134 | 57.85 181 | 48.16 147 | 53.55 221 | 69.77 185 | 59.47 73 | 73.73 113 | 72.49 100 | 75.27 142 | 61.44 168 |
|
GA-MVS | | | 60.73 157 | 64.24 167 | 56.64 144 | 59.38 163 | 57.45 150 | 65.07 159 | 36.65 195 | 57.39 184 | 58.17 108 | 73.43 131 | 69.10 190 | 47.38 145 | 64.47 192 | 63.63 184 | 73.19 160 | 64.22 159 |
|
CVMVSNet | | | 60.45 158 | 63.72 171 | 56.63 145 | 54.82 191 | 53.75 178 | 68.41 145 | 41.95 136 | 55.07 194 | 48.03 148 | 58.08 206 | 68.67 191 | 55.09 106 | 69.14 181 | 68.34 133 | 71.51 170 | 72.97 109 |
|
FC-MVSNet-test | | | 60.28 159 | 70.83 141 | 47.96 195 | 54.69 193 | 47.12 204 | 68.06 146 | 41.68 142 | 71.42 95 | 23.73 230 | 84.70 38 | 77.41 165 | 28.92 217 | 82.33 69 | 73.08 96 | 70.68 180 | 59.77 173 |
|
EU-MVSNet | | | 59.77 160 | 66.07 154 | 52.42 157 | 47.81 213 | 51.86 191 | 62.98 168 | 32.28 214 | 62.08 165 | 47.10 153 | 59.94 198 | 83.42 140 | 53.08 121 | 70.06 176 | 63.19 185 | 71.26 174 | 71.96 118 |
|
IterMVS | | | 59.24 161 | 64.45 164 | 53.16 154 | 50.98 204 | 61.29 130 | 66.51 152 | 32.85 210 | 58.17 177 | 46.31 159 | 72.58 136 | 70.23 181 | 54.26 113 | 64.81 191 | 60.24 192 | 68.04 192 | 63.81 161 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
view800 | | | 59.22 162 | 66.23 153 | 51.03 170 | 61.99 138 | 56.71 155 | 60.53 177 | 41.20 148 | 66.26 143 | 32.46 199 | 66.68 162 | 69.93 182 | 36.77 183 | 74.52 107 | 70.00 117 | 73.24 158 | 59.56 175 |
|
HyFIR lowres test | | | 59.15 163 | 62.28 181 | 55.49 147 | 52.42 199 | 62.59 123 | 71.76 126 | 39.74 168 | 50.25 214 | 41.92 172 | 62.88 183 | 69.16 189 | 55.85 102 | 62.77 197 | 67.18 150 | 71.27 173 | 61.11 169 |
|
thres600view7 | | | 58.87 164 | 65.91 155 | 50.66 172 | 61.27 141 | 56.32 159 | 59.88 182 | 40.63 163 | 64.88 150 | 32.10 204 | 64.82 173 | 69.83 184 | 36.72 185 | 72.99 129 | 72.55 99 | 73.34 156 | 59.97 172 |
|
view600 | | | 58.47 165 | 65.42 157 | 50.36 174 | 61.04 144 | 55.84 167 | 59.33 185 | 40.34 166 | 64.46 152 | 32.31 203 | 64.78 174 | 69.85 183 | 36.46 186 | 72.46 139 | 71.31 104 | 72.68 163 | 59.26 179 |
|
CMPMVS | ![Method available as binary. binary](img/icon_binary.png) | 45.32 18 | 58.10 166 | 65.24 160 | 49.76 177 | 47.88 212 | 46.86 207 | 48.16 232 | 32.82 211 | 58.06 178 | 61.35 99 | 59.64 199 | 80.00 148 | 47.27 147 | 70.15 173 | 64.10 181 | 61.08 201 | 77.85 76 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MDTV_nov1_ep13_2view | | | 58.09 167 | 63.54 173 | 51.74 162 | 50.13 208 | 46.56 208 | 66.95 151 | 33.41 207 | 63.52 155 | 58.77 106 | 74.84 123 | 84.10 136 | 43.12 163 | 65.95 187 | 54.69 205 | 58.04 207 | 55.13 195 |
|
CDS-MVSNet | | | 57.90 168 | 63.57 172 | 51.28 167 | 62.30 136 | 53.17 182 | 64.70 161 | 51.61 62 | 57.41 183 | 32.75 196 | 63.73 179 | 70.53 180 | 27.12 220 | 72.49 136 | 73.02 97 | 69.22 189 | 54.68 198 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
FMVSNet2 | | | 57.80 169 | 65.39 158 | 48.94 188 | 55.88 181 | 57.61 147 | 57.26 200 | 42.37 126 | 58.21 176 | 33.19 194 | 68.36 149 | 75.55 169 | 34.58 195 | 66.91 186 | 64.55 178 | 70.38 182 | 66.56 149 |
|
tfpn | | | 57.74 170 | 63.03 178 | 51.58 165 | 62.87 133 | 57.28 151 | 61.53 175 | 41.99 134 | 67.67 130 | 32.52 198 | 68.13 150 | 43.08 239 | 36.94 182 | 76.07 103 | 69.31 120 | 73.62 150 | 59.68 174 |
|
thres400 | | | 57.25 171 | 63.73 170 | 49.70 178 | 60.19 152 | 54.95 174 | 58.16 190 | 39.60 169 | 62.42 162 | 31.98 207 | 62.33 187 | 69.20 188 | 35.96 190 | 70.07 175 | 68.03 140 | 72.28 165 | 59.12 180 |
|
tfpn_n400 | | | 57.07 172 | 64.44 165 | 48.48 191 | 59.55 157 | 52.25 186 | 57.99 197 | 38.85 172 | 71.25 98 | 29.07 216 | 65.20 170 | 63.07 202 | 34.41 198 | 73.99 109 | 67.52 147 | 70.99 176 | 57.83 182 |
|
tfpnconf | | | 57.07 172 | 64.44 165 | 48.48 191 | 59.55 157 | 52.25 186 | 57.99 197 | 38.85 172 | 71.25 98 | 29.07 216 | 65.20 170 | 63.07 202 | 34.41 198 | 73.99 109 | 67.52 147 | 70.99 176 | 57.83 182 |
|
gm-plane-assit | | | 56.76 174 | 57.64 195 | 55.73 146 | 66.01 92 | 55.45 171 | 74.96 102 | 30.54 219 | 73.71 83 | 56.04 116 | 81.81 77 | 30.91 246 | 43.83 160 | 58.77 209 | 54.71 204 | 63.02 199 | 48.13 215 |
|
MIMVSNet1 | | | 56.72 175 | 68.69 148 | 42.76 210 | 46.70 218 | 42.81 214 | 69.13 139 | 30.52 220 | 81.01 44 | 32.00 205 | 74.82 124 | 91.10 55 | 26.83 222 | 73.98 111 | 64.72 177 | 51.40 219 | 52.38 201 |
|
tfpnview11 | | | 56.69 176 | 63.86 168 | 48.33 194 | 59.46 160 | 52.35 185 | 57.85 199 | 38.80 177 | 68.21 127 | 29.07 216 | 65.20 170 | 63.07 202 | 34.36 200 | 73.21 121 | 68.72 123 | 70.44 181 | 56.28 191 |
|
EPNet_dtu | | | 56.63 177 | 60.77 188 | 51.80 161 | 55.47 186 | 44.63 209 | 69.83 136 | 38.74 178 | 50.27 213 | 47.64 149 | 58.01 208 | 72.27 176 | 33.71 207 | 68.60 182 | 67.72 146 | 65.39 195 | 63.86 160 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
GBi-Net | | | 56.54 178 | 63.26 175 | 48.70 189 | 55.88 181 | 57.61 147 | 57.26 200 | 41.75 139 | 49.06 219 | 32.37 200 | 61.81 190 | 67.02 193 | 34.58 195 | 72.33 141 | 68.45 129 | 70.38 182 | 66.56 149 |
|
test1 | | | 56.54 178 | 63.26 175 | 48.70 189 | 55.88 181 | 57.61 147 | 57.26 200 | 41.75 139 | 49.06 219 | 32.37 200 | 61.81 190 | 67.02 193 | 34.58 195 | 72.33 141 | 68.45 129 | 70.38 182 | 66.56 149 |
|
gg-mvs-nofinetune | | | 56.45 180 | 61.04 185 | 51.10 169 | 63.42 129 | 49.40 199 | 53.71 214 | 52.52 59 | 74.77 74 | 46.93 154 | 77.31 109 | 53.88 218 | 26.42 224 | 62.51 198 | 57.81 197 | 63.60 198 | 51.57 205 |
|
thres200 | | | 56.35 181 | 62.36 180 | 49.34 180 | 58.87 166 | 56.32 159 | 55.91 204 | 40.63 163 | 58.51 174 | 31.34 208 | 58.81 205 | 67.31 192 | 35.96 190 | 72.99 129 | 65.51 174 | 73.34 156 | 57.07 187 |
|
MS-PatchMatch | | | 56.31 182 | 60.22 191 | 51.73 163 | 60.53 151 | 55.53 170 | 63.41 166 | 37.18 191 | 51.34 208 | 37.44 180 | 60.53 195 | 62.19 206 | 45.52 156 | 64.25 193 | 63.17 186 | 66.33 194 | 64.56 157 |
|
tfpn1000 | | | 56.13 183 | 63.18 177 | 47.91 196 | 58.34 173 | 53.03 183 | 58.87 187 | 38.14 181 | 65.64 147 | 27.09 221 | 65.41 169 | 59.49 214 | 33.41 209 | 73.14 122 | 69.08 121 | 71.63 169 | 56.46 190 |
|
conf200view11 | | | 56.07 184 | 61.85 182 | 49.32 182 | 58.57 167 | 56.49 156 | 58.01 192 | 40.73 157 | 53.23 197 | 30.91 211 | 56.41 210 | 66.40 197 | 34.18 202 | 73.03 125 | 68.06 136 | 73.54 151 | 59.36 176 |
|
tfpn200view9 | | | 56.07 184 | 61.85 182 | 49.34 180 | 58.57 167 | 56.48 158 | 58.01 192 | 40.72 159 | 53.23 197 | 31.01 209 | 56.41 210 | 66.40 197 | 34.18 202 | 73.02 127 | 68.06 136 | 73.53 153 | 59.35 178 |
|
tfpn111 | | | 55.56 186 | 60.91 187 | 49.32 182 | 58.57 167 | 56.49 156 | 58.01 192 | 40.73 157 | 53.23 197 | 30.91 211 | 49.82 227 | 66.40 197 | 34.18 202 | 73.03 125 | 68.06 136 | 73.54 151 | 59.36 176 |
|
tpmp4_e23 | | | 55.21 187 | 55.01 204 | 55.44 148 | 61.24 142 | 53.77 177 | 69.57 138 | 43.81 108 | 55.88 190 | 51.16 135 | 60.15 196 | 45.66 233 | 44.99 158 | 59.13 208 | 53.13 209 | 61.88 200 | 57.35 185 |
|
FMVSNet3 | | | 54.77 188 | 60.73 189 | 47.81 197 | 54.29 195 | 56.88 154 | 55.89 205 | 41.75 139 | 49.06 219 | 32.37 200 | 61.81 190 | 67.02 193 | 33.67 208 | 62.88 196 | 61.96 190 | 68.88 190 | 65.53 153 |
|
thres100view900 | | | 53.88 189 | 59.19 192 | 47.68 198 | 58.57 167 | 52.74 184 | 54.45 210 | 38.07 183 | 53.23 197 | 31.01 209 | 56.41 210 | 66.40 197 | 32.80 211 | 65.03 189 | 64.43 179 | 71.18 175 | 56.10 192 |
|
CR-MVSNet | | | 53.82 190 | 55.40 202 | 51.98 159 | 51.57 203 | 50.23 195 | 45.00 235 | 44.97 99 | 46.90 226 | 52.60 129 | 67.91 151 | 46.99 230 | 48.37 138 | 59.15 206 | 59.53 194 | 69.38 188 | 57.07 187 |
|
conf0.01 | | | 53.73 191 | 57.58 196 | 49.24 185 | 58.35 172 | 56.17 161 | 58.01 192 | 40.65 161 | 53.23 197 | 30.80 214 | 51.96 223 | 43.35 238 | 34.18 202 | 72.49 136 | 68.06 136 | 73.43 154 | 57.80 184 |
|
test20.03 | | | 53.49 192 | 60.95 186 | 44.78 207 | 64.73 104 | 47.25 203 | 61.58 174 | 43.30 117 | 65.86 145 | 22.85 231 | 66.87 161 | 79.85 149 | 22.99 226 | 62.38 199 | 56.95 199 | 53.25 215 | 47.46 216 |
|
MVSTER | | | 53.08 193 | 56.39 199 | 49.21 187 | 47.19 215 | 51.08 192 | 60.14 181 | 31.74 216 | 40.63 237 | 38.97 178 | 55.78 213 | 46.74 231 | 42.47 166 | 67.29 184 | 62.99 187 | 74.73 144 | 70.23 125 |
|
CHOSEN 1792x2688 | | | 52.99 194 | 56.91 198 | 48.42 193 | 47.32 214 | 50.10 197 | 64.18 163 | 33.85 204 | 45.46 231 | 36.95 182 | 55.20 216 | 66.49 196 | 51.20 125 | 59.28 204 | 59.81 193 | 57.01 210 | 61.99 167 |
|
conf0.002 | | | 52.78 195 | 55.83 200 | 49.22 186 | 58.28 174 | 56.09 163 | 58.01 192 | 40.64 162 | 53.23 197 | 30.79 215 | 50.10 226 | 36.15 243 | 34.18 202 | 72.40 140 | 65.72 172 | 73.41 155 | 57.11 186 |
|
CostFormer | | | 52.59 196 | 55.14 203 | 49.61 179 | 52.72 197 | 50.40 194 | 66.28 153 | 33.78 205 | 52.85 203 | 43.43 168 | 66.30 164 | 51.37 220 | 41.78 169 | 54.92 221 | 51.18 214 | 59.68 203 | 58.98 181 |
|
testgi | | | 51.94 197 | 61.37 184 | 40.94 214 | 58.38 171 | 47.03 205 | 65.88 155 | 30.49 221 | 70.87 106 | 22.64 232 | 57.53 209 | 87.59 103 | 18.30 232 | 63.01 195 | 54.32 206 | 49.93 222 | 49.27 209 |
|
tfpn_ndepth | | | 51.52 198 | 57.21 197 | 44.88 205 | 54.05 196 | 52.14 190 | 53.58 215 | 37.07 192 | 55.55 192 | 24.73 226 | 47.12 232 | 56.92 216 | 28.92 217 | 69.22 180 | 64.80 176 | 70.94 179 | 54.74 197 |
|
tpm cat1 | | | 50.98 199 | 51.28 214 | 50.62 173 | 55.74 184 | 49.92 198 | 63.13 167 | 38.12 182 | 52.38 205 | 47.61 150 | 60.11 197 | 44.51 235 | 44.86 159 | 51.31 231 | 47.49 224 | 54.25 214 | 53.24 200 |
|
RPMNet | | | 50.92 200 | 50.32 217 | 51.62 164 | 50.25 207 | 50.23 195 | 59.16 186 | 46.70 86 | 46.90 226 | 42.39 171 | 48.97 229 | 37.23 240 | 41.78 169 | 57.30 217 | 56.18 201 | 69.44 187 | 55.43 194 |
|
pmmvs5 | | | 50.64 201 | 58.01 193 | 42.05 211 | 47.01 217 | 43.67 212 | 49.27 228 | 29.43 222 | 50.77 211 | 33.83 192 | 68.69 146 | 76.16 167 | 27.82 219 | 57.53 216 | 57.07 198 | 64.95 196 | 52.18 202 |
|
PatchT | | | 50.55 202 | 53.55 210 | 47.05 202 | 37.59 237 | 42.26 216 | 50.55 225 | 37.56 189 | 46.37 228 | 52.60 129 | 66.91 159 | 43.54 237 | 48.37 138 | 59.15 206 | 59.53 194 | 55.62 212 | 57.07 187 |
|
Anonymous20231206 | | | 50.28 203 | 57.94 194 | 41.35 213 | 55.45 187 | 43.65 213 | 58.06 191 | 34.12 203 | 62.02 166 | 24.25 229 | 59.33 200 | 79.80 150 | 24.49 225 | 59.55 202 | 54.28 207 | 51.74 218 | 46.94 218 |
|
thresconf0.02 | | | 49.98 204 | 53.83 208 | 45.48 204 | 56.47 177 | 49.38 200 | 52.01 220 | 36.49 196 | 63.51 156 | 28.04 219 | 49.82 227 | 36.72 242 | 32.63 212 | 64.84 190 | 60.66 191 | 67.22 193 | 51.91 204 |
|
dps | | | 49.71 205 | 51.97 212 | 47.07 201 | 52.37 200 | 47.00 206 | 53.02 218 | 40.52 165 | 44.91 232 | 41.23 176 | 64.55 176 | 44.27 236 | 40.12 172 | 57.71 215 | 51.97 212 | 55.14 213 | 53.41 199 |
|
MDTV_nov1_ep13 | | | 49.60 206 | 51.57 213 | 47.31 199 | 46.28 219 | 44.61 210 | 59.82 183 | 30.96 217 | 48.80 223 | 50.20 139 | 59.26 202 | 52.38 219 | 38.56 174 | 56.20 219 | 49.70 219 | 58.04 207 | 50.01 207 |
|
LP | | | 49.44 207 | 55.77 201 | 42.05 211 | 38.31 235 | 42.61 215 | 51.74 221 | 36.31 197 | 58.35 175 | 40.36 177 | 68.52 148 | 60.77 211 | 37.08 181 | 58.27 213 | 51.76 213 | 48.51 223 | 50.13 206 |
|
PatchmatchNet | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | | 48.67 208 | 50.10 218 | 46.99 203 | 48.29 211 | 41.00 217 | 55.54 206 | 38.94 171 | 51.38 207 | 45.15 165 | 63.22 181 | 48.45 225 | 42.83 164 | 53.80 227 | 48.50 222 | 51.19 221 | 44.37 220 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
DWT-MVSNet_training | | | 48.57 209 | 47.93 226 | 49.31 184 | 51.79 202 | 48.05 202 | 61.84 173 | 34.33 202 | 41.94 235 | 43.42 169 | 50.35 224 | 34.74 245 | 47.30 146 | 52.62 228 | 52.08 210 | 57.20 209 | 55.74 193 |
|
new-patchmatchnet | | | 47.33 210 | 60.49 190 | 31.99 232 | 55.69 185 | 33.86 234 | 36.84 244 | 33.31 208 | 72.36 90 | 14.33 243 | 80.09 91 | 92.14 34 | 13.27 240 | 63.54 194 | 40.09 234 | 38.51 236 | 41.32 227 |
|
tpm | | | 46.67 211 | 49.20 223 | 43.72 208 | 49.60 209 | 36.60 230 | 53.93 213 | 26.84 224 | 52.70 204 | 58.05 109 | 69.04 145 | 47.96 226 | 30.06 216 | 48.33 235 | 42.76 229 | 43.88 230 | 47.01 217 |
|
pmmvs3 | | | 46.64 212 | 54.13 207 | 37.90 221 | 31.23 243 | 40.68 218 | 49.83 227 | 15.34 240 | 46.31 229 | 36.34 184 | 53.15 222 | 74.40 173 | 36.36 187 | 58.43 211 | 56.64 200 | 58.32 206 | 49.29 208 |
|
TAMVS | | | 46.64 212 | 53.62 209 | 38.49 219 | 49.56 210 | 36.87 227 | 53.16 217 | 25.76 226 | 56.33 189 | 22.55 234 | 60.72 193 | 61.80 208 | 27.12 220 | 59.50 203 | 58.33 196 | 52.79 216 | 41.82 226 |
|
test-LLR | | | 46.01 214 | 45.06 234 | 47.11 200 | 59.39 161 | 36.72 228 | 51.28 222 | 40.95 154 | 36.41 242 | 34.45 190 | 46.14 234 | 47.02 228 | 38.00 175 | 51.78 229 | 48.53 220 | 58.60 204 | 48.84 211 |
|
MIMVSNet | | | 45.83 215 | 53.46 211 | 36.94 222 | 45.38 224 | 39.50 220 | 52.20 219 | 30.68 218 | 57.09 185 | 24.53 228 | 55.22 215 | 71.54 177 | 21.74 228 | 55.81 220 | 51.08 215 | 47.11 226 | 43.96 221 |
|
test0.0.03 1 | | | 45.40 216 | 49.55 221 | 40.57 216 | 59.39 161 | 44.36 211 | 53.37 216 | 40.95 154 | 47.14 225 | 19.23 237 | 45.49 236 | 60.24 212 | 19.24 230 | 54.82 222 | 51.98 211 | 51.21 220 | 42.82 223 |
|
PMMVS | | | 45.37 217 | 49.29 222 | 40.79 215 | 27.75 244 | 35.07 232 | 50.88 224 | 19.88 235 | 39.27 239 | 35.78 185 | 50.11 225 | 61.29 209 | 42.04 167 | 54.13 226 | 55.95 202 | 68.43 191 | 49.19 210 |
|
test1235678 | | | 44.92 218 | 54.19 205 | 34.11 227 | 41.53 227 | 37.95 224 | 54.27 211 | 23.09 230 | 53.64 195 | 22.14 235 | 53.92 218 | 84.05 137 | 16.41 235 | 60.66 200 | 50.30 217 | 47.20 224 | 38.84 230 |
|
testmv | | | 44.91 219 | 54.17 206 | 34.11 227 | 41.52 228 | 37.93 225 | 54.27 211 | 23.09 230 | 53.61 196 | 22.14 235 | 53.89 219 | 84.00 138 | 16.41 235 | 60.64 201 | 50.29 218 | 47.20 224 | 38.83 231 |
|
MVS-HIRNet | | | 44.56 220 | 45.52 232 | 43.44 209 | 40.98 229 | 31.03 239 | 39.52 243 | 36.96 193 | 42.80 234 | 44.37 166 | 53.80 220 | 60.04 213 | 41.85 168 | 47.97 237 | 41.08 232 | 56.99 211 | 41.95 225 |
|
test-mter | | | 44.18 221 | 47.60 227 | 40.18 217 | 33.20 239 | 39.03 221 | 55.28 207 | 13.91 242 | 39.07 240 | 36.63 183 | 48.09 231 | 49.52 221 | 41.12 171 | 54.55 223 | 50.91 216 | 60.97 202 | 52.03 203 |
|
EMVS | | | 43.85 222 | 49.91 219 | 36.77 224 | 45.46 223 | 32.70 236 | 44.09 237 | 25.33 227 | 57.88 180 | 26.62 222 | 58.99 204 | 61.14 210 | 42.77 165 | 70.26 172 | 38.52 239 | 36.38 238 | 29.87 239 |
|
E-PMN | | | 43.83 223 | 49.81 220 | 36.84 223 | 46.09 221 | 31.86 238 | 42.77 239 | 25.85 225 | 57.76 182 | 25.53 223 | 55.50 214 | 62.47 205 | 43.77 161 | 70.78 169 | 39.51 236 | 37.04 237 | 30.79 238 |
|
tpmrst | | | 43.31 224 | 46.14 230 | 40.02 218 | 47.05 216 | 36.48 231 | 48.01 233 | 32.17 215 | 49.50 217 | 37.26 181 | 63.66 180 | 47.04 227 | 31.98 215 | 42.00 242 | 40.55 233 | 43.64 231 | 43.75 222 |
|
TESTMET0.1,1 | | | 41.79 225 | 45.06 234 | 37.97 220 | 31.32 242 | 36.72 228 | 51.28 222 | 14.17 241 | 36.41 242 | 34.45 190 | 46.14 234 | 47.02 228 | 38.00 175 | 51.78 229 | 48.53 220 | 58.60 204 | 48.84 211 |
|
testus | | | 41.61 226 | 50.54 216 | 31.20 234 | 38.11 236 | 38.92 222 | 49.10 229 | 17.60 237 | 48.25 224 | 25.05 224 | 41.45 238 | 79.34 152 | 13.18 241 | 58.28 212 | 47.10 225 | 44.17 229 | 40.41 228 |
|
testpf | | | 41.44 227 | 38.52 241 | 44.85 206 | 46.17 220 | 38.68 223 | 60.29 179 | 43.31 116 | 24.28 244 | 35.09 186 | 39.52 240 | 34.84 244 | 32.39 213 | 43.79 241 | 39.89 235 | 51.88 217 | 48.65 213 |
|
ADS-MVSNet | | | 40.61 228 | 46.31 228 | 33.96 229 | 40.70 230 | 30.42 240 | 40.42 241 | 33.44 206 | 58.01 179 | 30.87 213 | 63.05 182 | 54.48 217 | 22.67 227 | 44.35 240 | 39.23 238 | 35.64 239 | 34.64 234 |
|
CHOSEN 280x420 | | | 40.24 229 | 44.14 238 | 35.69 225 | 32.36 241 | 23.58 245 | 50.30 226 | 21.21 234 | 40.94 236 | 18.84 238 | 32.75 243 | 48.65 224 | 48.13 142 | 59.16 205 | 55.31 203 | 43.28 232 | 48.62 214 |
|
EPMVS | | | 40.11 230 | 44.96 236 | 34.44 226 | 41.55 226 | 32.65 237 | 41.74 240 | 32.39 213 | 49.89 216 | 24.83 225 | 64.44 177 | 46.38 232 | 26.57 223 | 44.75 239 | 39.47 237 | 39.59 234 | 37.16 232 |
|
FMVSNet5 | | | 39.83 231 | 45.08 233 | 33.71 230 | 39.24 231 | 39.56 219 | 48.77 230 | 23.55 229 | 39.45 238 | 24.55 227 | 33.73 242 | 44.57 234 | 20.97 229 | 58.27 213 | 54.23 208 | 45.16 227 | 45.77 219 |
|
1111 | | | 39.71 232 | 44.86 237 | 33.71 230 | 50.45 205 | 28.51 241 | 55.07 208 | 34.43 200 | 62.60 160 | 17.59 239 | 62.60 184 | 28.17 247 | 14.69 237 | 54.19 224 | 41.91 231 | 30.02 241 | 36.03 233 |
|
test12356 | | | 39.53 233 | 49.18 224 | 28.26 236 | 32.93 240 | 33.64 235 | 48.68 231 | 15.96 239 | 46.26 230 | 16.21 241 | 46.46 233 | 79.07 154 | 17.13 233 | 58.60 210 | 48.30 223 | 38.67 235 | 31.96 236 |
|
N_pmnet | | | 39.50 234 | 51.01 215 | 26.09 238 | 44.48 225 | 25.59 244 | 40.20 242 | 21.49 233 | 64.20 153 | 7.98 246 | 73.86 128 | 76.67 166 | 13.66 239 | 50.17 233 | 36.69 241 | 28.71 242 | 29.86 240 |
|
test2356 | | | 35.97 235 | 39.61 240 | 31.71 233 | 38.85 232 | 34.29 233 | 45.78 234 | 22.27 232 | 38.89 241 | 22.59 233 | 37.67 241 | 37.07 241 | 16.57 234 | 50.72 232 | 45.45 226 | 44.20 228 | 33.19 235 |
|
MVE | ![Method available under a permissive open source license. permissive](img/icon_permissive.png) | 28.01 19 | 35.86 236 | 43.56 239 | 26.88 237 | 22.33 246 | 19.75 247 | 30.85 247 | 23.88 228 | 49.90 215 | 10.48 244 | 43.64 237 | 61.87 207 | 48.99 137 | 47.26 238 | 42.15 230 | 24.76 243 | 40.37 229 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
new_pmnet | | | 35.76 237 | 45.64 231 | 24.22 239 | 38.59 233 | 25.83 243 | 31.87 246 | 19.24 236 | 49.06 219 | 9.01 245 | 54.34 217 | 64.73 201 | 12.46 242 | 49.21 234 | 44.91 227 | 34.17 240 | 31.41 237 |
|
PMMVS2 | | | 34.11 238 | 48.55 225 | 17.26 240 | 25.45 245 | 20.72 246 | 35.08 245 | 16.26 238 | 58.71 173 | 4.16 248 | 59.22 203 | 78.40 160 | 3.65 243 | 57.24 218 | 38.31 240 | 18.94 244 | 27.28 241 |
|
GG-mvs-BLEND | | | 31.54 239 | 46.27 229 | 14.37 241 | 0.07 250 | 48.65 201 | 42.97 238 | 0.08 247 | 44.04 233 | 1.21 250 | 39.77 239 | 57.94 215 | 0.15 247 | 48.19 236 | 42.82 228 | 41.70 233 | 42.46 224 |
|
.test1245 | | | 31.52 240 | 33.91 242 | 28.73 235 | 50.45 205 | 28.51 241 | 55.07 208 | 34.43 200 | 62.60 160 | 17.59 239 | 62.60 184 | 28.17 247 | 14.69 237 | 54.19 224 | 0.54 244 | 0.15 248 | 0.77 244 |
|
test123 | | | 0.53 241 | 0.60 244 | 0.46 243 | 0.22 248 | 0.25 250 | 0.33 252 | 0.13 246 | 0.66 247 | 1.37 249 | 1.10 246 | 0.00 252 | 0.43 245 | 0.68 245 | 0.61 243 | 0.26 247 | 0.88 243 |
|
testmvs | | | 0.47 242 | 0.69 243 | 0.21 244 | 0.17 249 | 0.17 251 | 0.35 251 | 0.16 245 | 0.66 247 | 0.18 251 | 1.05 247 | 0.99 251 | 0.27 246 | 0.62 246 | 0.54 244 | 0.15 248 | 0.77 244 |
|
sosnet-low-res | | | 0.00 243 | 0.00 245 | 0.00 245 | 0.00 251 | 0.00 252 | 0.00 253 | 0.00 248 | 0.00 249 | 0.00 252 | 0.00 248 | 0.00 252 | 0.00 248 | 0.00 247 | 0.00 246 | 0.00 250 | 0.00 246 |
|
sosnet | | | 0.00 243 | 0.00 245 | 0.00 245 | 0.00 251 | 0.00 252 | 0.00 253 | 0.00 248 | 0.00 249 | 0.00 252 | 0.00 248 | 0.00 252 | 0.00 248 | 0.00 247 | 0.00 246 | 0.00 250 | 0.00 246 |
|
Anonymous202405211 | | | | 72.22 129 | | 66.19 90 | 61.09 133 | 62.23 171 | 45.87 93 | 71.25 98 | | 79.33 100 | 86.16 124 | 37.36 179 | 73.54 118 | 69.84 118 | 75.45 140 | 64.32 158 |
|
our_test_3 | | | | | | 52.72 197 | 53.66 179 | 69.11 140 | | | | | | | | | | |
|
ambc | | | | 79.96 59 | | 74.57 49 | 75.48 46 | 73.75 122 | | 80.32 50 | 72.34 38 | 78.46 102 | 92.41 31 | 59.05 74 | 80.24 85 | 73.95 92 | 75.41 141 | 78.85 69 |
|
MTAPA | | | | | | | | | | | 80.26 8 | | 90.53 71 | | | | | |
|
MTMP | | | | | | | | | | | 82.07 4 | | 91.00 57 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.05 250 | | | | | | | | | | |
|
tmp_tt | | | | | 7.47 242 | 8.89 247 | 3.32 249 | 4.35 249 | 1.14 244 | 15.58 246 | 15.76 242 | 8.50 245 | 5.90 250 | 2.00 244 | 20.02 243 | 21.51 242 | 12.70 245 | |
|
XVS | | | | | | 80.47 19 | 81.29 12 | 93.33 3 | | | 77.45 20 | | 90.19 75 | | | | 91.52 11 | |
|
X-MVStestdata | | | | | | 80.47 19 | 81.29 12 | 93.33 3 | | | 77.45 20 | | 90.19 75 | | | | 91.52 11 | |
|
abl_6 | | | | | 65.41 81 | 69.37 76 | 74.02 54 | 82.50 56 | 47.39 80 | 66.39 141 | 56.63 114 | 60.61 194 | 82.76 141 | 53.68 117 | | | 82.92 76 | 78.39 74 |
|
mPP-MVS | | | | | | 82.97 2 | | | | | | | 92.12 35 | | | | | |
|
NP-MVS | | | | | | | | | | 71.39 96 | | | | | | | | |
|
Patchmtry | | | | | | | 37.73 226 | 45.00 235 | 44.97 99 | | 52.60 129 | | | | | | | |
|
DeepMVS_CX | ![Method available under an open source license with copyleft or other restrictive terms. copyleft](img/icon_copyleft.png) | | | | | | 8.52 248 | 9.75 248 | 3.19 243 | 16.70 245 | 5.02 247 | 23.06 244 | 19.33 249 | 18.69 231 | 13.75 244 | | 11.34 246 | 25.07 242 |
|