TDRefinement | | | 93.16 1 | 95.57 1 | 90.36 1 | 88.79 46 | 93.57 1 | 97.27 1 | 78.23 19 | 95.55 2 | 93.00 1 | 93.98 17 | 96.01 49 | 87.53 1 | 97.69 1 | 96.81 1 | 97.33 1 | 95.34 4 |
|
COLMAP_ROB | | 85.66 2 | 91.85 2 | 95.01 2 | 88.16 12 | 88.98 45 | 92.86 2 | 95.51 19 | 72.17 53 | 94.95 5 | 91.27 3 | 94.11 16 | 97.77 14 | 84.22 8 | 96.49 4 | 95.27 5 | 96.79 2 | 93.60 9 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
LTVRE_ROB | | 86.82 1 | 91.55 3 | 94.43 3 | 88.19 11 | 83.19 98 | 86.35 58 | 93.60 32 | 78.79 16 | 95.48 4 | 91.79 2 | 93.08 25 | 97.21 23 | 86.34 3 | 97.06 2 | 96.27 3 | 95.46 22 | 95.56 3 |
Andreas Kuhn, Heiko Hirschmüller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016 |
ACMMPR | | | 91.30 4 | 92.88 10 | 89.46 4 | 91.92 11 | 91.61 5 | 96.60 5 | 79.46 11 | 90.08 29 | 88.53 14 | 89.54 74 | 95.57 61 | 84.25 7 | 95.24 20 | 94.27 13 | 95.97 11 | 93.85 6 |
|
CP-MVS | | | 91.09 5 | 92.33 20 | 89.65 2 | 92.16 10 | 90.41 24 | 96.46 10 | 80.38 6 | 88.26 41 | 89.17 11 | 87.00 100 | 96.34 38 | 83.95 10 | 95.77 11 | 94.72 8 | 95.81 17 | 93.78 8 |
|
MP-MVS | | | 90.84 6 | 91.95 28 | 89.55 3 | 92.92 5 | 90.90 17 | 96.56 6 | 79.60 9 | 86.83 53 | 88.75 13 | 89.00 81 | 94.38 87 | 84.01 9 | 94.94 25 | 94.34 11 | 95.45 23 | 93.24 18 |
|
ACMM | | 80.67 7 | 90.67 7 | 92.46 17 | 88.57 8 | 91.35 19 | 89.93 28 | 96.34 12 | 77.36 28 | 90.17 27 | 86.88 30 | 87.32 94 | 96.63 28 | 83.32 14 | 95.79 10 | 94.49 10 | 96.19 9 | 92.91 21 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMMP | | | 90.63 8 | 92.40 18 | 88.56 9 | 91.24 25 | 91.60 6 | 96.49 9 | 77.53 24 | 87.89 43 | 86.87 31 | 87.24 96 | 96.46 32 | 82.87 19 | 95.59 15 | 94.50 9 | 96.35 6 | 93.51 14 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
LGP-MVS_train | | | 90.56 9 | 92.38 19 | 88.43 10 | 90.88 29 | 91.15 11 | 95.35 21 | 77.65 23 | 86.26 58 | 87.23 24 | 90.45 66 | 97.35 20 | 83.20 15 | 95.44 16 | 93.41 19 | 96.28 8 | 92.63 22 |
|
PGM-MVS | | | 90.42 10 | 91.58 32 | 89.05 6 | 91.77 13 | 91.06 13 | 96.51 7 | 78.94 14 | 85.41 66 | 87.67 18 | 87.02 99 | 95.26 69 | 83.62 13 | 95.01 24 | 93.94 15 | 95.79 18 | 93.40 16 |
|
MPTG | | | 90.38 11 | 91.35 35 | 89.25 5 | 93.08 3 | 86.59 55 | 96.45 11 | 79.00 13 | 90.23 26 | 89.30 10 | 85.87 110 | 94.97 79 | 82.54 21 | 95.05 23 | 94.83 7 | 95.14 26 | 91.94 30 |
|
DeepC-MVS | | 83.59 4 | 90.37 12 | 92.56 16 | 87.82 14 | 91.26 24 | 92.33 3 | 94.72 26 | 80.04 7 | 90.01 30 | 84.61 44 | 93.33 21 | 94.22 88 | 80.59 28 | 92.90 39 | 92.52 27 | 95.69 20 | 92.57 23 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
PMVS | | 79.51 9 | 90.23 13 | 92.67 12 | 87.39 19 | 90.16 35 | 88.75 36 | 93.64 31 | 75.78 38 | 90.00 31 | 83.70 56 | 92.97 27 | 92.22 109 | 86.13 4 | 97.01 3 | 96.79 2 | 94.94 28 | 90.96 41 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
ACMP | | 80.00 8 | 90.12 14 | 92.30 21 | 87.58 17 | 90.83 31 | 91.10 12 | 94.96 24 | 76.06 36 | 87.47 47 | 85.33 40 | 88.91 83 | 97.65 18 | 82.13 23 | 95.31 17 | 93.44 18 | 96.14 10 | 92.22 27 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
SteuartSystems-ACMMP | | | 90.00 15 | 91.73 29 | 87.97 13 | 91.21 26 | 90.29 25 | 96.51 7 | 78.00 21 | 86.33 56 | 85.32 41 | 88.23 86 | 94.67 83 | 82.08 24 | 95.13 22 | 93.88 16 | 94.72 33 | 93.59 10 |
Skip Steuart: Steuart Systems R&D Blog. |
SD-MVS | | | 89.91 16 | 92.23 24 | 87.19 20 | 91.31 21 | 89.79 30 | 94.31 28 | 75.34 40 | 89.26 33 | 81.79 74 | 92.68 30 | 95.08 75 | 83.88 11 | 93.10 36 | 92.69 24 | 96.54 4 | 93.02 19 |
|
ACMMP_Plus | | | 89.86 17 | 91.96 27 | 87.42 18 | 91.00 27 | 90.08 26 | 96.00 17 | 76.61 32 | 89.28 32 | 87.73 17 | 90.04 68 | 91.80 116 | 78.71 36 | 94.36 29 | 93.82 17 | 94.48 34 | 94.32 5 |
|
APDe-MVS | | | 89.85 18 | 92.91 9 | 86.29 24 | 90.47 34 | 91.34 7 | 96.04 16 | 76.41 35 | 91.11 15 | 78.50 95 | 93.44 20 | 95.82 53 | 81.55 26 | 93.16 35 | 91.90 36 | 94.77 32 | 93.58 12 |
|
OPM-MVS | | | 89.82 19 | 92.24 23 | 86.99 21 | 90.86 30 | 89.35 32 | 95.07 23 | 75.91 37 | 91.16 14 | 86.87 31 | 91.07 58 | 97.29 21 | 79.13 34 | 93.32 33 | 91.99 35 | 94.12 37 | 91.49 37 |
|
WR-MVS | | | 89.79 20 | 93.66 4 | 85.27 34 | 91.32 20 | 88.27 40 | 93.49 33 | 79.86 8 | 92.75 8 | 75.37 106 | 96.86 1 | 98.38 6 | 75.10 64 | 95.93 8 | 94.07 14 | 96.46 5 | 89.39 53 |
|
TSAR-MVS + MP. | | | 89.67 21 | 92.25 22 | 86.65 23 | 91.53 16 | 90.98 16 | 96.15 14 | 73.30 50 | 87.88 44 | 81.83 73 | 92.92 28 | 95.15 73 | 82.23 22 | 93.58 32 | 92.25 32 | 94.87 29 | 93.01 20 |
|
CPTT-MVS | | | 89.63 22 | 90.52 43 | 88.59 7 | 90.95 28 | 90.74 19 | 95.71 18 | 79.13 12 | 87.70 45 | 85.68 39 | 80.05 139 | 95.74 56 | 84.77 6 | 94.28 30 | 92.68 25 | 95.28 25 | 92.45 25 |
|
ACMH+ | | 79.05 11 | 89.62 23 | 93.08 7 | 85.58 29 | 88.58 48 | 89.26 33 | 92.18 40 | 74.23 46 | 93.55 7 | 82.66 65 | 92.32 39 | 98.35 8 | 80.29 29 | 95.28 18 | 92.34 30 | 95.52 21 | 90.43 44 |
|
X-MVS | | | 89.36 24 | 90.73 40 | 87.77 16 | 91.50 18 | 91.23 8 | 96.76 4 | 78.88 15 | 87.29 49 | 87.14 27 | 78.98 143 | 94.53 84 | 76.47 51 | 95.25 19 | 94.28 12 | 95.85 14 | 93.55 13 |
|
TSAR-MVS + ACMM | | | 89.14 25 | 92.11 26 | 85.67 28 | 89.27 42 | 90.61 22 | 90.98 45 | 79.48 10 | 88.86 36 | 79.80 87 | 93.01 26 | 93.53 95 | 83.17 16 | 92.75 43 | 92.45 28 | 91.32 70 | 93.59 10 |
|
SixPastTwentyTwo | | | 89.14 25 | 92.19 25 | 85.58 29 | 84.62 74 | 82.56 80 | 90.53 57 | 71.93 54 | 91.95 10 | 85.89 36 | 94.22 14 | 97.25 22 | 85.42 5 | 95.73 12 | 91.71 38 | 95.08 27 | 91.89 31 |
|
APD-MVS | | | 89.14 25 | 91.25 37 | 86.67 22 | 91.73 14 | 91.02 15 | 95.50 20 | 77.74 22 | 84.04 76 | 79.47 91 | 91.48 48 | 94.85 80 | 81.14 27 | 92.94 38 | 92.20 34 | 94.47 35 | 92.24 26 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
PS-CasMVS | | | 89.07 28 | 93.23 6 | 84.21 44 | 92.44 8 | 88.23 42 | 90.54 56 | 82.95 3 | 90.50 21 | 75.31 107 | 95.80 5 | 98.37 7 | 71.16 103 | 96.30 5 | 93.32 20 | 92.88 52 | 90.11 47 |
|
UA-Net | | | 89.02 29 | 91.44 34 | 86.20 25 | 94.88 1 | 89.84 29 | 94.76 25 | 77.45 26 | 85.41 66 | 74.79 110 | 88.83 84 | 88.90 137 | 78.67 38 | 96.06 7 | 95.45 4 | 96.66 3 | 95.58 2 |
|
LS3D | | | 89.02 29 | 91.69 30 | 85.91 27 | 89.72 39 | 90.81 18 | 92.56 39 | 71.69 55 | 90.83 19 | 87.24 22 | 89.71 72 | 92.07 112 | 78.37 39 | 94.43 28 | 92.59 26 | 95.86 13 | 91.35 38 |
|
DTE-MVSNet | | | 88.99 31 | 92.77 11 | 84.59 38 | 93.31 2 | 88.10 43 | 90.96 46 | 83.09 2 | 91.38 12 | 76.21 100 | 96.03 2 | 98.04 11 | 70.78 109 | 95.65 14 | 92.32 31 | 93.18 47 | 87.84 65 |
|
WR-MVS_H | | | 88.99 31 | 93.28 5 | 83.99 47 | 91.92 11 | 89.13 34 | 91.95 41 | 83.23 1 | 90.14 28 | 71.92 126 | 95.85 4 | 98.01 13 | 71.83 100 | 95.82 9 | 93.19 21 | 93.07 50 | 90.83 43 |
|
ACMH | | 78.40 12 | 88.94 33 | 92.62 14 | 84.65 37 | 86.45 61 | 87.16 52 | 91.47 42 | 68.79 72 | 95.49 3 | 89.74 6 | 93.55 19 | 98.50 3 | 77.96 42 | 94.14 31 | 89.57 54 | 93.49 41 | 89.94 49 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
PEN-MVS | | | 88.86 34 | 92.92 8 | 84.11 46 | 92.92 5 | 88.05 45 | 90.83 48 | 82.67 5 | 91.04 16 | 74.83 109 | 95.97 3 | 98.47 4 | 70.38 110 | 95.70 13 | 92.43 29 | 93.05 51 | 88.78 59 |
|
HPM-MVS++ | | | 88.74 35 | 89.54 49 | 87.80 15 | 92.58 7 | 85.69 63 | 95.10 22 | 78.01 20 | 87.08 50 | 87.66 19 | 87.89 89 | 92.07 112 | 80.28 30 | 90.97 65 | 91.41 39 | 93.17 48 | 91.69 32 |
|
CP-MVSNet | | | 88.71 36 | 92.63 13 | 84.13 45 | 92.39 9 | 88.09 44 | 90.47 61 | 82.86 4 | 88.79 38 | 75.16 108 | 94.87 7 | 97.68 17 | 71.05 105 | 96.16 6 | 93.18 22 | 92.85 53 | 89.64 51 |
|
HSP-MVS | | | 88.32 37 | 90.71 41 | 85.53 31 | 90.63 33 | 92.01 4 | 96.15 14 | 77.52 25 | 86.02 59 | 81.39 81 | 90.21 67 | 96.08 46 | 76.38 53 | 88.30 84 | 86.70 77 | 91.12 74 | 95.64 1 |
|
OMC-MVS | | | 88.16 38 | 91.34 36 | 84.46 41 | 86.85 58 | 90.63 21 | 93.01 36 | 67.00 85 | 90.35 25 | 87.40 21 | 86.86 102 | 96.35 37 | 77.66 44 | 92.63 44 | 90.84 40 | 94.84 30 | 91.68 33 |
|
3Dnovator+ | | 83.71 3 | 88.13 39 | 90.00 46 | 85.94 26 | 86.82 59 | 91.06 13 | 94.26 29 | 75.39 39 | 88.85 37 | 85.76 38 | 85.74 112 | 86.92 146 | 78.02 41 | 93.03 37 | 92.21 33 | 95.39 24 | 92.21 28 |
|
CSCG | | | 88.12 40 | 91.45 33 | 84.23 43 | 88.12 53 | 90.59 23 | 90.57 52 | 68.60 74 | 91.37 13 | 83.45 63 | 89.94 69 | 95.14 74 | 78.71 36 | 91.45 53 | 88.21 65 | 95.96 12 | 93.44 15 |
|
RPSCF | | | 88.05 41 | 92.61 15 | 82.73 58 | 84.24 79 | 88.40 38 | 90.04 65 | 66.29 89 | 91.46 11 | 82.29 67 | 88.93 82 | 96.01 49 | 79.38 32 | 95.15 21 | 94.90 6 | 94.15 36 | 93.40 16 |
|
DeepPCF-MVS | | 81.61 6 | 87.95 42 | 90.29 45 | 85.22 35 | 87.48 56 | 90.01 27 | 93.79 30 | 73.54 48 | 88.93 35 | 83.89 53 | 89.40 76 | 90.84 125 | 80.26 31 | 90.62 69 | 90.19 47 | 92.36 60 | 92.03 29 |
|
DeepC-MVS_fast | | 81.78 5 | 87.38 43 | 89.64 47 | 84.75 36 | 89.89 38 | 90.70 20 | 92.74 38 | 74.45 44 | 86.02 59 | 82.16 71 | 86.05 108 | 91.99 115 | 75.84 59 | 91.16 58 | 90.44 43 | 93.41 43 | 91.09 40 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
v7n | | | 87.11 44 | 90.46 44 | 83.19 50 | 85.22 70 | 83.69 71 | 90.03 66 | 68.20 79 | 91.01 17 | 86.71 34 | 94.80 8 | 98.46 5 | 77.69 43 | 91.10 60 | 85.98 81 | 91.30 71 | 88.19 62 |
|
CNVR-MVS | | | 86.93 45 | 88.98 53 | 84.54 39 | 90.11 36 | 87.41 50 | 93.23 35 | 73.47 49 | 86.31 57 | 82.25 68 | 82.96 125 | 92.15 110 | 76.04 56 | 91.69 49 | 90.69 41 | 92.17 62 | 91.64 35 |
|
NCCC | | | 86.74 46 | 87.97 65 | 85.31 33 | 90.64 32 | 87.25 51 | 93.27 34 | 74.59 43 | 86.50 54 | 83.72 55 | 75.92 165 | 92.39 107 | 77.08 48 | 91.72 48 | 90.68 42 | 92.57 58 | 91.30 39 |
|
train_agg | | | 86.67 47 | 87.73 66 | 85.43 32 | 91.51 17 | 82.72 77 | 94.47 27 | 74.22 47 | 81.71 98 | 81.54 80 | 89.20 80 | 92.87 100 | 78.33 40 | 90.12 72 | 88.47 61 | 92.51 59 | 89.04 56 |
|
CDPH-MVS | | | 86.66 48 | 88.52 56 | 84.48 40 | 89.61 40 | 88.27 40 | 92.86 37 | 72.69 52 | 80.55 113 | 82.71 64 | 86.92 101 | 93.32 96 | 75.55 61 | 91.00 63 | 89.85 49 | 93.47 42 | 89.71 50 |
|
Gipuma | | | 86.47 49 | 89.25 51 | 83.23 49 | 83.88 85 | 78.78 119 | 85.35 118 | 68.42 76 | 92.69 9 | 89.03 12 | 91.94 42 | 96.32 40 | 81.80 25 | 94.45 27 | 86.86 73 | 90.91 75 | 83.69 90 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PHI-MVS | | | 86.37 50 | 88.14 62 | 84.30 42 | 86.65 60 | 87.56 48 | 90.76 49 | 70.16 61 | 82.55 86 | 89.65 7 | 84.89 118 | 92.40 106 | 75.97 57 | 90.88 67 | 89.70 51 | 92.58 56 | 89.03 57 |
|
MSLP-MVS++ | | | 86.29 51 | 89.10 52 | 83.01 51 | 85.71 68 | 89.79 30 | 87.04 108 | 74.39 45 | 85.17 68 | 78.92 94 | 77.59 150 | 93.57 93 | 82.60 20 | 93.23 34 | 91.88 37 | 89.42 89 | 92.46 24 |
|
v52 | | | 86.26 52 | 90.85 38 | 80.91 70 | 72.49 163 | 81.25 99 | 90.55 54 | 60.30 149 | 90.43 24 | 87.24 22 | 94.64 11 | 98.30 10 | 83.16 18 | 92.86 41 | 86.82 75 | 91.69 65 | 91.65 34 |
|
V4 | | | 86.26 52 | 90.85 38 | 80.91 70 | 72.49 163 | 81.25 99 | 90.55 54 | 60.31 148 | 90.44 23 | 87.23 24 | 94.64 11 | 98.31 9 | 83.17 16 | 92.87 40 | 86.82 75 | 91.69 65 | 91.64 35 |
|
TAPA-MVS | | 78.00 13 | 85.88 54 | 88.37 58 | 82.96 53 | 84.69 73 | 88.62 37 | 90.62 50 | 64.22 120 | 89.15 34 | 88.05 15 | 78.83 144 | 93.71 90 | 76.20 55 | 90.11 73 | 88.22 64 | 94.00 38 | 89.97 48 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
anonymousdsp | | | 85.62 55 | 90.53 42 | 79.88 89 | 64.64 189 | 76.35 136 | 96.28 13 | 53.53 178 | 85.63 63 | 81.59 79 | 92.81 29 | 97.71 16 | 86.88 2 | 94.56 26 | 92.83 23 | 96.35 6 | 93.84 7 |
|
TSAR-MVS + COLMAP | | | 85.51 56 | 88.36 59 | 82.19 59 | 86.05 65 | 87.69 47 | 90.50 59 | 70.60 60 | 86.40 55 | 82.33 66 | 89.69 73 | 92.52 104 | 74.01 79 | 87.53 87 | 86.84 74 | 89.63 85 | 87.80 66 |
|
CNLPA | | | 85.50 57 | 88.58 54 | 81.91 61 | 84.55 76 | 87.52 49 | 90.89 47 | 63.56 128 | 88.18 42 | 84.06 49 | 83.85 122 | 91.34 122 | 76.46 52 | 91.27 55 | 89.00 59 | 91.96 63 | 88.88 58 |
|
PLC | | 76.06 15 | 85.38 58 | 87.46 68 | 82.95 54 | 85.79 67 | 88.84 35 | 88.86 75 | 68.70 73 | 87.06 51 | 83.60 58 | 79.02 142 | 90.05 129 | 77.37 47 | 90.88 67 | 89.66 52 | 93.37 44 | 86.74 72 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
TSAR-MVS + GP. | | | 85.32 59 | 87.41 70 | 82.89 55 | 90.07 37 | 85.69 63 | 89.07 73 | 72.99 51 | 82.45 88 | 74.52 113 | 85.09 116 | 87.67 143 | 79.24 33 | 91.11 59 | 90.41 44 | 91.45 68 | 89.45 52 |
|
TranMVSNet+NR-MVSNet | | | 85.23 60 | 89.38 50 | 80.39 86 | 88.78 47 | 83.77 70 | 87.40 96 | 76.75 30 | 85.47 64 | 68.99 139 | 95.18 6 | 97.55 19 | 67.13 126 | 91.61 50 | 89.13 58 | 93.26 45 | 82.95 102 |
|
v748 | | | 85.21 61 | 89.62 48 | 80.08 88 | 80.71 123 | 80.27 112 | 85.05 121 | 63.79 126 | 90.47 22 | 83.54 61 | 94.21 15 | 98.52 2 | 76.84 50 | 90.97 65 | 84.25 94 | 90.53 78 | 88.62 60 |
|
Anonymous20231211 | | | 85.16 62 | 91.64 31 | 77.61 112 | 88.54 49 | 79.81 115 | 83.12 128 | 74.68 42 | 98.37 1 | 66.79 150 | 94.56 13 | 99.60 1 | 61.64 145 | 91.49 52 | 89.82 50 | 90.91 75 | 87.80 66 |
|
HQP-MVS | | | 85.02 63 | 86.41 76 | 83.40 48 | 89.19 43 | 86.59 55 | 91.28 43 | 71.60 56 | 82.79 84 | 83.48 62 | 78.65 146 | 93.54 94 | 72.55 96 | 86.49 95 | 85.89 83 | 92.28 61 | 90.95 42 |
|
UniMVSNet (Re) | | | 84.95 64 | 88.53 55 | 80.78 74 | 87.82 55 | 84.21 67 | 88.03 85 | 76.50 33 | 81.18 108 | 69.29 136 | 92.63 34 | 96.83 25 | 69.07 116 | 91.23 57 | 89.60 53 | 93.97 39 | 84.00 88 |
|
DU-MVS | | | 84.88 65 | 88.27 61 | 80.92 69 | 88.30 50 | 83.59 72 | 87.06 106 | 78.35 17 | 80.64 111 | 70.49 132 | 92.67 31 | 96.91 24 | 68.13 120 | 91.79 46 | 89.29 57 | 93.20 46 | 83.02 99 |
|
MCST-MVS | | | 84.79 66 | 86.48 74 | 82.83 56 | 87.30 57 | 87.03 54 | 90.46 62 | 69.33 69 | 83.14 80 | 82.21 70 | 81.69 133 | 92.14 111 | 75.09 65 | 87.27 89 | 84.78 91 | 92.58 56 | 89.30 54 |
|
UniMVSNet_NR-MVSNet | | | 84.62 67 | 88.00 64 | 80.68 78 | 88.18 52 | 83.83 69 | 87.06 106 | 76.47 34 | 81.46 104 | 70.49 132 | 93.24 22 | 95.56 63 | 68.13 120 | 90.43 70 | 88.47 61 | 93.78 40 | 83.02 99 |
|
EG-PatchMatch MVS | | | 84.35 68 | 87.55 67 | 80.62 81 | 86.38 62 | 82.24 82 | 86.75 110 | 64.02 123 | 84.24 72 | 78.17 97 | 89.38 77 | 95.03 77 | 78.78 35 | 89.95 74 | 86.33 79 | 89.59 86 | 85.65 78 |
|
AdaColmap | | | 84.15 69 | 85.14 95 | 83.00 52 | 89.08 44 | 87.14 53 | 90.56 53 | 70.90 58 | 82.40 89 | 80.41 84 | 73.82 173 | 84.69 154 | 75.19 63 | 91.58 51 | 89.90 48 | 91.87 64 | 86.48 73 |
|
PCF-MVS | | 76.59 14 | 84.11 70 | 85.27 92 | 82.76 57 | 86.12 64 | 88.30 39 | 91.24 44 | 69.10 70 | 82.36 90 | 84.45 45 | 77.56 151 | 90.40 128 | 72.91 95 | 85.88 99 | 83.88 96 | 92.72 55 | 88.53 61 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MVS_111021_HR | | | 83.95 71 | 86.10 80 | 81.44 66 | 84.62 74 | 80.29 111 | 90.51 58 | 68.05 80 | 84.07 75 | 80.38 85 | 84.74 119 | 91.37 121 | 74.23 73 | 90.37 71 | 87.25 68 | 90.86 77 | 84.59 81 |
|
TinyColmap | | | 83.79 72 | 86.12 79 | 81.07 68 | 83.42 91 | 81.44 94 | 85.42 116 | 68.55 75 | 88.71 39 | 89.46 8 | 87.60 91 | 92.72 101 | 70.34 111 | 89.29 77 | 81.94 108 | 89.20 90 | 81.12 120 |
|
v13 | | | 83.75 73 | 86.20 78 | 80.89 72 | 83.38 92 | 81.93 85 | 88.58 78 | 66.09 92 | 83.55 77 | 84.28 46 | 92.67 31 | 96.79 26 | 74.67 69 | 84.42 110 | 79.72 122 | 88.36 101 | 84.31 84 |
|
v1192 | | | 83.61 74 | 85.23 93 | 81.72 63 | 84.05 81 | 82.15 83 | 89.54 68 | 66.20 90 | 81.38 106 | 86.76 33 | 91.79 45 | 96.03 48 | 74.88 67 | 81.81 137 | 80.92 112 | 88.91 93 | 82.50 107 |
|
v12 | | | 83.59 75 | 86.00 83 | 80.77 76 | 83.30 94 | 81.83 86 | 88.45 79 | 65.95 95 | 83.20 79 | 84.15 47 | 92.54 36 | 96.71 27 | 74.50 71 | 84.19 112 | 79.64 123 | 88.30 102 | 83.93 89 |
|
v1240 | | | 83.57 76 | 84.94 99 | 81.97 60 | 84.05 81 | 81.27 98 | 89.46 70 | 66.06 93 | 81.31 107 | 87.50 20 | 91.88 44 | 95.46 66 | 76.25 54 | 81.16 142 | 80.51 117 | 88.52 99 | 82.98 101 |
|
v1921920 | | | 83.49 77 | 84.94 99 | 81.80 62 | 83.78 86 | 81.20 102 | 89.50 69 | 65.91 96 | 81.64 100 | 87.18 26 | 91.70 46 | 95.39 67 | 75.85 58 | 81.56 140 | 80.27 118 | 88.60 97 | 82.80 103 |
|
v144192 | | | 83.43 78 | 84.97 98 | 81.63 65 | 83.43 90 | 81.23 101 | 89.42 71 | 66.04 94 | 81.45 105 | 86.40 35 | 91.46 50 | 95.70 60 | 75.76 60 | 82.14 134 | 80.23 119 | 88.74 94 | 82.57 106 |
|
V9 | | | 83.42 79 | 85.81 85 | 80.63 80 | 83.20 97 | 81.73 89 | 88.29 83 | 65.78 99 | 82.87 83 | 83.99 52 | 92.38 38 | 96.60 29 | 74.30 72 | 83.93 113 | 79.58 125 | 88.24 105 | 83.55 93 |
|
Vis-MVSNet | | | 83.32 80 | 88.12 63 | 77.71 110 | 77.91 142 | 83.44 74 | 90.58 51 | 69.49 66 | 81.11 109 | 67.10 148 | 89.85 70 | 91.48 120 | 71.71 101 | 91.34 54 | 89.37 55 | 89.48 88 | 90.26 45 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v11 | | | 83.30 81 | 85.58 88 | 80.64 79 | 83.53 89 | 81.74 88 | 88.30 82 | 65.46 104 | 82.75 85 | 84.63 43 | 92.49 37 | 96.17 44 | 73.90 80 | 82.69 128 | 79.59 124 | 88.04 109 | 83.66 91 |
|
V14 | | | 83.23 82 | 85.59 87 | 80.48 84 | 83.09 100 | 81.63 91 | 88.13 84 | 65.61 101 | 82.53 87 | 83.81 54 | 92.17 40 | 96.50 30 | 74.07 77 | 83.66 115 | 79.51 127 | 88.17 106 | 83.16 97 |
|
v1144 | | | 83.22 83 | 85.01 96 | 81.14 67 | 83.76 87 | 81.60 92 | 88.95 74 | 65.58 102 | 81.89 94 | 85.80 37 | 91.68 47 | 95.84 52 | 74.04 78 | 82.12 135 | 80.56 116 | 88.70 96 | 81.41 117 |
|
MVS_111021_LR | | | 83.20 84 | 85.33 90 | 80.73 77 | 82.88 103 | 78.23 123 | 89.61 67 | 65.23 107 | 82.08 93 | 81.19 82 | 85.31 114 | 92.04 114 | 75.22 62 | 89.50 75 | 85.90 82 | 90.24 80 | 84.23 85 |
|
v10 | | | 83.17 85 | 85.22 94 | 80.78 74 | 83.26 96 | 82.99 76 | 88.66 76 | 66.49 88 | 79.24 126 | 83.60 58 | 91.46 50 | 95.47 64 | 74.12 74 | 82.60 130 | 80.66 113 | 88.53 98 | 84.11 87 |
|
v15 | | | 83.06 86 | 85.39 89 | 80.35 87 | 83.01 101 | 81.53 93 | 87.98 87 | 65.47 103 | 82.19 92 | 83.66 57 | 92.00 41 | 96.40 36 | 73.87 81 | 83.39 117 | 79.44 128 | 88.10 108 | 82.76 104 |
|
PVSNet_Blended_VisFu | | | 83.00 87 | 84.16 112 | 81.65 64 | 82.17 116 | 86.01 59 | 88.03 85 | 71.23 57 | 76.05 141 | 79.54 90 | 83.88 121 | 83.44 155 | 77.49 46 | 87.38 88 | 84.93 90 | 91.41 69 | 87.40 70 |
|
NR-MVSNet | | | 82.89 88 | 87.43 69 | 77.59 113 | 83.91 84 | 83.59 72 | 87.10 105 | 78.35 17 | 80.64 111 | 68.85 140 | 92.67 31 | 96.50 30 | 54.19 167 | 87.19 92 | 88.68 60 | 93.16 49 | 82.75 105 |
|
Baseline_NR-MVSNet | | | 82.79 89 | 86.51 73 | 78.44 107 | 88.30 50 | 75.62 142 | 87.81 88 | 74.97 41 | 81.53 102 | 66.84 149 | 94.71 10 | 96.46 32 | 66.90 127 | 91.79 46 | 83.37 103 | 85.83 140 | 82.09 112 |
|
v7 | | | 82.76 90 | 84.65 102 | 80.55 82 | 83.27 95 | 81.77 87 | 88.66 76 | 65.10 108 | 79.23 127 | 83.60 58 | 91.47 49 | 95.47 64 | 74.12 74 | 82.61 129 | 80.66 113 | 88.52 99 | 81.35 118 |
|
EPP-MVSNet | | | 82.76 90 | 86.47 75 | 78.45 106 | 86.00 66 | 84.47 66 | 85.39 117 | 68.42 76 | 84.17 73 | 62.97 159 | 89.26 79 | 76.84 177 | 72.13 98 | 92.56 45 | 90.40 45 | 95.76 19 | 87.56 69 |
|
CLD-MVS | | | 82.75 92 | 87.22 71 | 77.54 114 | 88.01 54 | 85.76 62 | 90.23 64 | 54.52 171 | 82.28 91 | 82.11 72 | 88.48 85 | 95.27 68 | 63.95 136 | 89.41 76 | 88.29 63 | 86.45 131 | 81.01 121 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
Effi-MVS+ | | | 82.33 93 | 83.87 118 | 80.52 83 | 84.51 77 | 81.32 95 | 87.53 94 | 68.05 80 | 74.94 145 | 79.67 89 | 82.37 129 | 92.31 108 | 72.21 97 | 85.06 105 | 86.91 72 | 91.18 72 | 84.20 86 |
|
v1 | | | 82.27 94 | 84.32 106 | 79.87 90 | 82.86 104 | 80.32 108 | 87.57 93 | 63.47 132 | 81.87 96 | 84.13 48 | 91.34 52 | 96.29 41 | 73.23 91 | 82.39 131 | 79.08 138 | 87.94 111 | 78.98 134 |
|
v1141 | | | 82.26 95 | 84.32 106 | 79.85 91 | 82.86 104 | 80.31 109 | 87.58 91 | 63.48 130 | 81.86 97 | 84.03 51 | 91.33 53 | 96.28 42 | 73.23 91 | 82.39 131 | 79.08 138 | 87.93 112 | 78.97 135 |
|
divwei89l23v2f112 | | | 82.26 95 | 84.32 106 | 79.85 91 | 82.86 104 | 80.31 109 | 87.58 91 | 63.48 130 | 81.88 95 | 84.05 50 | 91.33 53 | 96.27 43 | 73.23 91 | 82.39 131 | 79.08 138 | 87.93 112 | 78.97 135 |
|
3Dnovator | | 79.41 10 | 82.21 97 | 86.07 81 | 77.71 110 | 79.31 132 | 84.61 65 | 87.18 103 | 61.02 145 | 85.65 62 | 76.11 101 | 85.07 117 | 85.38 152 | 70.96 107 | 87.22 90 | 86.47 78 | 91.66 67 | 88.12 64 |
|
v8 | | | 82.20 98 | 84.56 103 | 79.45 94 | 82.42 108 | 81.65 90 | 87.26 97 | 64.27 118 | 79.36 122 | 81.70 75 | 91.04 61 | 95.75 55 | 73.30 89 | 82.82 124 | 79.18 135 | 87.74 116 | 82.09 112 |
|
v2v482 | | | 82.20 98 | 84.26 110 | 79.81 93 | 82.67 107 | 80.18 113 | 87.67 90 | 63.96 125 | 81.69 99 | 84.73 42 | 91.27 56 | 96.33 39 | 72.05 99 | 81.94 136 | 79.56 126 | 87.79 115 | 78.84 137 |
|
v17 | | | 82.09 100 | 84.45 104 | 79.33 96 | 82.41 109 | 81.31 96 | 87.26 97 | 64.50 117 | 78.72 129 | 80.73 83 | 90.90 62 | 95.57 61 | 73.37 85 | 83.06 118 | 79.25 131 | 87.70 120 | 82.35 110 |
|
Effi-MVS+-dtu | | | 82.04 101 | 83.39 124 | 80.48 84 | 85.48 69 | 86.57 57 | 88.40 80 | 68.28 78 | 69.04 164 | 73.13 120 | 76.26 160 | 91.11 124 | 74.74 68 | 88.40 82 | 87.76 66 | 92.84 54 | 84.57 82 |
|
MAR-MVS | | | 81.98 102 | 82.92 126 | 80.88 73 | 85.18 71 | 85.85 60 | 89.13 72 | 69.52 64 | 71.21 157 | 82.25 68 | 71.28 182 | 88.89 138 | 69.69 112 | 88.71 80 | 86.96 70 | 89.52 87 | 87.57 68 |
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020 |
v16 | | | 81.92 103 | 84.32 106 | 79.12 102 | 82.31 114 | 81.29 97 | 87.20 102 | 64.51 116 | 78.16 132 | 79.76 88 | 90.86 63 | 95.23 70 | 73.29 90 | 83.05 119 | 79.29 130 | 87.63 121 | 82.34 111 |
|
v6 | | | 81.77 104 | 83.96 115 | 79.22 99 | 82.41 109 | 80.45 107 | 87.26 97 | 62.91 140 | 79.29 123 | 81.65 76 | 91.08 57 | 95.74 56 | 73.32 86 | 82.84 121 | 79.21 134 | 87.73 117 | 79.07 131 |
|
v1neww | | | 81.76 105 | 83.95 116 | 79.21 100 | 82.41 109 | 80.46 105 | 87.26 97 | 62.93 136 | 79.28 124 | 81.62 77 | 91.06 59 | 95.72 58 | 73.31 87 | 82.83 122 | 79.22 132 | 87.73 117 | 79.07 131 |
|
v7new | | | 81.76 105 | 83.95 116 | 79.21 100 | 82.41 109 | 80.46 105 | 87.26 97 | 62.93 136 | 79.28 124 | 81.62 77 | 91.06 59 | 95.72 58 | 73.31 87 | 82.83 122 | 79.22 132 | 87.73 117 | 79.07 131 |
|
IS_MVSNet | | | 81.72 107 | 85.01 96 | 77.90 109 | 86.19 63 | 82.64 79 | 85.56 115 | 70.02 62 | 80.11 117 | 63.52 156 | 87.28 95 | 81.18 164 | 67.26 124 | 91.08 62 | 89.33 56 | 94.82 31 | 83.42 95 |
|
v18 | | | 81.62 108 | 83.99 114 | 78.86 103 | 82.08 117 | 81.12 103 | 86.93 109 | 64.24 119 | 77.44 133 | 79.47 91 | 90.53 64 | 94.99 78 | 72.99 94 | 82.72 127 | 79.18 135 | 87.48 124 | 81.91 115 |
|
FPMVS | | | 81.56 109 | 84.04 113 | 78.66 104 | 82.92 102 | 75.96 138 | 86.48 113 | 65.66 100 | 84.67 71 | 71.47 128 | 77.78 148 | 83.22 157 | 77.57 45 | 91.24 56 | 90.21 46 | 87.84 114 | 85.21 79 |
|
Fast-Effi-MVS+ | | | 81.42 110 | 83.82 119 | 78.62 105 | 82.24 115 | 80.62 104 | 87.72 89 | 63.51 129 | 73.01 148 | 74.75 111 | 83.80 123 | 92.70 102 | 73.44 84 | 88.15 86 | 85.26 87 | 90.05 81 | 83.17 96 |
|
USDC | | | 81.39 111 | 83.07 125 | 79.43 95 | 81.48 121 | 78.95 118 | 82.62 132 | 66.17 91 | 87.45 48 | 90.73 4 | 82.40 128 | 93.65 92 | 66.57 129 | 83.63 116 | 77.97 142 | 89.00 92 | 77.45 143 |
|
MSDG | | | 81.39 111 | 84.23 111 | 78.09 108 | 82.40 113 | 82.47 81 | 85.31 120 | 60.91 146 | 79.73 120 | 80.26 86 | 86.30 105 | 88.27 141 | 69.67 113 | 87.20 91 | 84.98 89 | 89.97 83 | 80.67 122 |
|
canonicalmvs | | | 81.22 113 | 86.04 82 | 75.60 121 | 83.17 99 | 83.18 75 | 80.29 143 | 65.82 98 | 85.97 61 | 67.98 146 | 77.74 149 | 91.51 119 | 65.17 132 | 88.62 81 | 86.15 80 | 91.17 73 | 89.09 55 |
|
pmmvs6 | | | 80.46 114 | 88.34 60 | 71.26 138 | 81.96 118 | 77.51 126 | 77.54 156 | 68.83 71 | 93.72 6 | 55.92 171 | 93.94 18 | 98.03 12 | 55.94 162 | 89.21 78 | 85.61 84 | 87.36 125 | 80.38 123 |
|
QAPM | | | 80.43 115 | 84.34 105 | 75.86 119 | 79.40 131 | 82.06 84 | 79.86 148 | 61.94 142 | 83.28 78 | 74.73 112 | 81.74 132 | 85.44 151 | 70.97 106 | 84.99 107 | 84.71 92 | 88.29 103 | 88.14 63 |
|
PM-MVS | | | 80.42 116 | 83.63 121 | 76.67 116 | 78.04 141 | 72.37 149 | 87.14 104 | 60.18 151 | 80.13 116 | 71.75 127 | 86.12 107 | 93.92 89 | 77.08 48 | 86.56 94 | 85.12 88 | 85.83 140 | 81.18 119 |
|
IterMVS-LS | | | 79.79 117 | 82.56 128 | 76.56 118 | 81.83 119 | 77.85 124 | 79.90 147 | 69.42 68 | 78.93 128 | 71.21 129 | 90.47 65 | 85.20 153 | 70.86 108 | 80.54 147 | 80.57 115 | 86.15 133 | 84.36 83 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
DELS-MVS | | | 79.71 118 | 83.74 120 | 75.01 124 | 79.31 132 | 82.68 78 | 84.79 123 | 60.06 152 | 75.43 143 | 69.09 138 | 86.13 106 | 89.38 131 | 67.16 125 | 85.12 104 | 83.87 97 | 89.65 84 | 83.57 92 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
pmmvs-eth3d | | | 79.64 119 | 82.06 130 | 76.83 115 | 80.05 127 | 72.64 148 | 87.47 95 | 66.59 87 | 80.83 110 | 73.50 117 | 89.32 78 | 93.20 97 | 67.78 122 | 80.78 145 | 81.64 110 | 85.58 142 | 76.01 145 |
|
UGNet | | | 79.62 120 | 85.91 84 | 72.28 136 | 73.52 157 | 83.91 68 | 86.64 111 | 69.51 65 | 79.85 119 | 62.57 161 | 85.82 111 | 89.63 130 | 53.18 170 | 88.39 83 | 87.35 67 | 88.28 104 | 86.43 74 |
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022 |
V42 | | | 79.59 121 | 83.59 122 | 74.93 126 | 69.61 175 | 77.05 133 | 86.59 112 | 55.84 167 | 78.42 131 | 77.29 98 | 89.84 71 | 95.08 75 | 74.12 74 | 83.05 119 | 80.11 120 | 86.12 134 | 81.59 116 |
|
EPNet | | | 79.36 122 | 79.44 137 | 79.27 98 | 89.51 41 | 77.20 131 | 88.35 81 | 77.35 29 | 68.27 166 | 74.29 114 | 76.31 158 | 79.22 168 | 59.63 150 | 85.02 106 | 85.45 86 | 86.49 130 | 84.61 80 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
v148 | | | 79.33 123 | 82.32 129 | 75.84 120 | 80.14 126 | 75.74 139 | 81.98 135 | 57.06 163 | 81.51 103 | 79.36 93 | 89.42 75 | 96.42 34 | 71.32 102 | 81.54 141 | 75.29 155 | 85.20 144 | 76.32 144 |
|
FC-MVSNet-train | | | 79.20 124 | 86.29 77 | 70.94 141 | 84.06 80 | 77.67 125 | 85.68 114 | 64.11 122 | 82.90 82 | 52.22 183 | 92.57 35 | 93.69 91 | 49.52 179 | 88.30 84 | 86.93 71 | 90.03 82 | 81.95 114 |
|
TransMVSNet (Re) | | | 79.05 125 | 86.66 72 | 70.18 148 | 83.32 93 | 75.99 137 | 77.54 156 | 63.98 124 | 90.68 20 | 55.84 172 | 94.80 8 | 96.06 47 | 53.73 169 | 86.27 97 | 83.22 104 | 86.65 127 | 79.61 129 |
|
no-one | | | 78.59 126 | 85.28 91 | 70.79 142 | 59.01 196 | 68.77 161 | 76.62 160 | 46.06 189 | 80.25 115 | 75.75 104 | 81.85 131 | 97.75 15 | 83.63 12 | 90.99 64 | 87.20 69 | 83.67 149 | 90.14 46 |
|
OpenMVS | | 75.38 16 | 78.44 127 | 81.39 132 | 74.99 125 | 80.46 124 | 79.85 114 | 79.99 145 | 58.31 159 | 77.34 134 | 73.85 116 | 77.19 154 | 82.33 162 | 68.60 119 | 84.67 109 | 81.95 107 | 88.72 95 | 86.40 75 |
|
pm-mvs1 | | | 78.21 128 | 85.68 86 | 69.50 152 | 80.38 125 | 75.73 140 | 76.25 161 | 65.04 109 | 87.59 46 | 54.47 177 | 93.16 24 | 95.99 51 | 54.20 166 | 86.37 96 | 82.98 105 | 86.64 128 | 77.96 142 |
|
FMVSNet1 | | | 78.20 129 | 84.83 101 | 70.46 146 | 78.62 137 | 79.03 117 | 77.90 155 | 67.53 84 | 83.02 81 | 55.10 174 | 87.19 97 | 93.18 98 | 55.65 163 | 85.57 100 | 83.39 100 | 87.98 110 | 82.40 108 |
|
DI_MVS_plusplus_trai | | | 77.64 130 | 79.64 136 | 75.31 123 | 79.87 129 | 76.89 134 | 81.55 138 | 63.64 127 | 76.21 140 | 72.03 125 | 85.59 113 | 82.97 158 | 66.63 128 | 79.27 150 | 77.78 144 | 88.14 107 | 78.76 138 |
|
Fast-Effi-MVS+-dtu | | | 76.92 131 | 77.18 145 | 76.62 117 | 79.55 130 | 79.17 116 | 84.80 122 | 77.40 27 | 64.46 180 | 68.75 142 | 70.81 188 | 86.57 147 | 63.36 142 | 81.74 138 | 81.76 109 | 85.86 139 | 75.78 147 |
|
MVS_Test | | | 76.72 132 | 79.40 138 | 73.60 130 | 78.85 136 | 74.99 143 | 79.91 146 | 61.56 144 | 69.67 160 | 72.44 121 | 85.98 109 | 90.78 126 | 63.50 140 | 78.30 152 | 75.74 154 | 85.33 143 | 80.31 127 |
|
MDA-MVSNet-bldmvs | | | 76.51 133 | 82.87 127 | 69.09 153 | 50.71 207 | 74.72 145 | 84.05 127 | 60.27 150 | 81.62 101 | 71.16 130 | 88.21 87 | 91.58 117 | 69.62 114 | 92.78 42 | 77.48 147 | 78.75 162 | 73.69 153 |
|
EU-MVSNet | | | 76.48 134 | 80.53 134 | 71.75 137 | 67.62 180 | 70.30 152 | 81.74 136 | 54.06 174 | 75.47 142 | 71.01 131 | 80.10 137 | 93.17 99 | 73.67 82 | 83.73 114 | 77.85 143 | 82.40 154 | 83.07 98 |
|
PVSNet_BlendedMVS | | | 76.45 135 | 78.12 139 | 74.49 127 | 76.76 147 | 78.46 120 | 79.65 149 | 63.26 134 | 65.42 176 | 73.15 118 | 75.05 169 | 88.96 135 | 66.51 130 | 82.73 125 | 77.66 145 | 87.61 122 | 78.60 139 |
|
PVSNet_Blended | | | 76.45 135 | 78.12 139 | 74.49 127 | 76.76 147 | 78.46 120 | 79.65 149 | 63.26 134 | 65.42 176 | 73.15 118 | 75.05 169 | 88.96 135 | 66.51 130 | 82.73 125 | 77.66 145 | 87.61 122 | 78.60 139 |
|
Vis-MVSNet (Re-imp) | | | 76.15 137 | 80.84 133 | 70.68 143 | 83.66 88 | 74.80 144 | 81.66 137 | 69.59 63 | 80.48 114 | 46.94 188 | 87.44 92 | 80.63 166 | 53.14 171 | 86.87 93 | 84.56 93 | 89.12 91 | 71.12 156 |
|
PatchMatch-RL | | | 76.05 138 | 76.64 149 | 75.36 122 | 77.84 143 | 69.87 155 | 81.09 140 | 63.43 133 | 71.66 155 | 68.34 144 | 71.70 178 | 81.76 163 | 74.98 66 | 84.83 108 | 83.44 99 | 86.45 131 | 73.22 154 |
|
pmmvs4 | | | 75.92 139 | 77.48 144 | 74.10 129 | 78.21 140 | 70.94 151 | 84.06 126 | 64.78 112 | 75.13 144 | 68.47 143 | 84.12 120 | 83.32 156 | 64.74 135 | 75.93 162 | 79.14 137 | 84.31 147 | 73.77 152 |
|
FC-MVSNet-test | | | 75.91 140 | 83.59 122 | 66.95 161 | 76.63 153 | 69.07 158 | 85.33 119 | 64.97 111 | 84.87 70 | 41.95 193 | 93.17 23 | 87.04 145 | 47.78 182 | 91.09 61 | 85.56 85 | 85.06 145 | 74.34 150 |
|
CVMVSNet | | | 75.65 141 | 77.62 143 | 73.35 133 | 71.95 167 | 69.89 154 | 83.04 131 | 60.84 147 | 69.12 162 | 68.76 141 | 79.92 140 | 78.93 170 | 73.64 83 | 81.02 143 | 81.01 111 | 81.86 156 | 83.43 94 |
|
IB-MVS | | 71.28 17 | 75.21 142 | 77.00 148 | 73.12 134 | 76.76 147 | 77.45 127 | 83.05 130 | 58.92 156 | 63.01 184 | 64.31 155 | 59.99 202 | 87.57 144 | 68.64 118 | 86.26 98 | 82.34 106 | 87.05 126 | 82.36 109 |
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 |
GA-MVS | | | 75.01 143 | 76.39 151 | 73.39 131 | 78.37 138 | 75.66 141 | 80.03 144 | 58.40 158 | 70.51 158 | 75.85 103 | 83.24 124 | 76.14 178 | 63.75 137 | 77.28 155 | 76.62 151 | 83.97 148 | 75.30 149 |
|
FMVSNet2 | | | 74.43 144 | 79.70 135 | 68.27 155 | 76.76 147 | 77.36 128 | 75.77 165 | 65.36 106 | 72.28 153 | 52.97 181 | 81.92 130 | 85.61 150 | 52.73 174 | 80.66 146 | 79.73 121 | 86.04 135 | 80.37 124 |
|
diffmvs | | | 73.65 145 | 77.10 146 | 69.63 151 | 73.21 158 | 69.52 156 | 79.35 153 | 57.48 160 | 73.80 147 | 68.08 145 | 87.10 98 | 82.39 160 | 61.36 146 | 74.27 165 | 74.51 156 | 78.31 163 | 78.14 141 |
|
IterMVS | | | 73.62 146 | 76.53 150 | 70.23 147 | 71.83 168 | 77.18 132 | 80.69 142 | 53.22 179 | 72.23 154 | 66.62 151 | 85.21 115 | 78.96 169 | 69.54 115 | 76.28 161 | 71.63 164 | 79.45 159 | 74.25 151 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MIMVSNet1 | | | 73.40 147 | 81.85 131 | 63.55 172 | 72.90 160 | 64.37 173 | 84.58 124 | 53.60 177 | 90.84 18 | 53.92 178 | 87.75 90 | 96.10 45 | 45.31 184 | 85.37 103 | 79.32 129 | 70.98 178 | 69.18 162 |
|
HyFIR lowres test | | | 73.29 148 | 74.14 159 | 72.30 135 | 73.08 159 | 78.33 122 | 83.12 128 | 62.41 141 | 63.81 181 | 62.13 162 | 76.67 157 | 78.50 171 | 71.09 104 | 74.13 166 | 77.47 148 | 81.98 155 | 70.10 157 |
|
GBi-Net | | | 73.17 149 | 77.64 141 | 67.95 156 | 76.76 147 | 77.36 128 | 75.77 165 | 64.57 113 | 62.99 185 | 51.83 184 | 76.05 161 | 77.76 174 | 52.73 174 | 85.57 100 | 83.39 100 | 86.04 135 | 80.37 124 |
|
test1 | | | 73.17 149 | 77.64 141 | 67.95 156 | 76.76 147 | 77.36 128 | 75.77 165 | 64.57 113 | 62.99 185 | 51.83 184 | 76.05 161 | 77.76 174 | 52.73 174 | 85.57 100 | 83.39 100 | 86.04 135 | 80.37 124 |
|
CDS-MVSNet | | | 73.07 151 | 77.02 147 | 68.46 154 | 81.62 120 | 72.89 147 | 79.56 151 | 70.78 59 | 69.56 161 | 52.52 182 | 77.37 153 | 81.12 165 | 42.60 187 | 84.20 111 | 83.93 95 | 83.65 150 | 70.07 158 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
MDTV_nov1_ep13_2view | | | 72.96 152 | 75.59 155 | 69.88 149 | 71.15 172 | 64.86 172 | 82.31 134 | 54.45 172 | 76.30 139 | 78.32 96 | 86.52 103 | 91.58 117 | 61.35 147 | 76.80 156 | 66.83 176 | 71.70 173 | 66.26 168 |
|
gg-mvs-nofinetune | | | 72.68 153 | 75.21 156 | 69.73 150 | 81.48 121 | 69.04 159 | 70.48 180 | 76.67 31 | 86.92 52 | 67.80 147 | 88.06 88 | 64.67 189 | 42.12 189 | 77.60 153 | 73.65 158 | 79.81 158 | 66.57 167 |
|
EPNet_dtu | | | 71.90 154 | 73.03 163 | 70.59 144 | 78.28 139 | 61.64 178 | 82.44 133 | 64.12 121 | 63.26 183 | 69.74 134 | 71.47 180 | 82.41 159 | 51.89 177 | 78.83 151 | 78.01 141 | 77.07 164 | 75.60 148 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
gm-plane-assit | | | 71.56 155 | 69.99 165 | 73.39 131 | 84.43 78 | 73.21 146 | 90.42 63 | 51.36 185 | 84.08 74 | 76.00 102 | 91.30 55 | 37.09 211 | 59.01 153 | 73.65 171 | 70.24 168 | 79.09 161 | 60.37 184 |
|
CMPMVS | | 55.74 18 | 71.56 155 | 76.26 152 | 66.08 166 | 68.11 179 | 63.91 175 | 63.17 200 | 50.52 187 | 68.79 165 | 75.49 105 | 70.78 189 | 85.67 149 | 63.54 139 | 81.58 139 | 77.20 149 | 75.63 165 | 85.86 77 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
FMVSNet3 | | | 71.40 157 | 75.20 157 | 66.97 160 | 75.00 155 | 76.59 135 | 74.29 170 | 64.57 113 | 62.99 185 | 51.83 184 | 76.05 161 | 77.76 174 | 51.49 178 | 76.58 159 | 77.03 150 | 84.62 146 | 79.43 130 |
|
MS-PatchMatch | | | 71.18 158 | 73.99 160 | 67.89 158 | 77.16 145 | 71.76 150 | 77.18 158 | 56.38 166 | 67.35 168 | 55.04 175 | 74.63 171 | 75.70 179 | 62.38 144 | 76.62 158 | 75.97 153 | 79.22 160 | 75.90 146 |
|
test20.03 | | | 69.91 159 | 76.20 153 | 62.58 173 | 84.01 83 | 67.34 165 | 75.67 169 | 65.88 97 | 79.98 118 | 40.28 197 | 82.65 126 | 89.31 133 | 39.63 191 | 77.41 154 | 73.28 159 | 69.98 179 | 63.40 176 |
|
CR-MVSNet | | | 69.56 160 | 68.34 173 | 70.99 139 | 72.78 162 | 67.63 163 | 64.47 197 | 67.74 82 | 59.93 194 | 72.30 122 | 80.10 137 | 56.77 198 | 65.04 133 | 71.64 179 | 72.91 160 | 83.61 152 | 69.40 160 |
|
pmmvs5 | | | 68.91 161 | 74.35 158 | 62.56 174 | 67.45 182 | 66.78 167 | 71.70 177 | 51.47 184 | 67.17 171 | 56.25 170 | 82.41 127 | 88.59 139 | 47.21 183 | 73.21 175 | 74.23 157 | 81.30 157 | 68.03 165 |
|
CHOSEN 1792x2688 | | | 68.80 162 | 71.09 164 | 66.13 165 | 69.11 177 | 68.89 160 | 78.98 154 | 54.68 169 | 61.63 191 | 56.69 168 | 71.56 179 | 78.39 172 | 67.69 123 | 72.13 177 | 72.01 163 | 69.63 181 | 73.02 155 |
|
tpmp4_e23 | | | 68.32 163 | 66.04 176 | 70.98 140 | 77.52 144 | 69.23 157 | 80.99 141 | 65.46 104 | 68.09 167 | 69.25 137 | 70.77 190 | 54.03 204 | 59.35 151 | 69.01 186 | 63.02 183 | 73.34 170 | 68.15 164 |
|
testgi | | | 68.20 164 | 76.05 154 | 59.04 180 | 79.99 128 | 67.32 166 | 81.16 139 | 51.78 183 | 84.91 69 | 39.36 200 | 73.42 174 | 95.19 71 | 32.79 197 | 76.54 160 | 70.40 167 | 69.14 182 | 64.55 172 |
|
MVSTER | | | 68.08 165 | 69.73 167 | 66.16 164 | 66.33 187 | 70.06 153 | 75.71 168 | 52.36 181 | 55.18 203 | 58.64 165 | 70.23 192 | 56.72 199 | 57.34 158 | 79.68 149 | 76.03 152 | 86.61 129 | 80.20 128 |
|
Anonymous20231206 | | | 67.28 166 | 73.41 162 | 60.12 179 | 76.45 154 | 63.61 176 | 74.21 171 | 56.52 165 | 76.35 138 | 42.23 192 | 75.81 166 | 90.47 127 | 41.51 190 | 74.52 163 | 69.97 169 | 69.83 180 | 63.17 177 |
|
RPMNet | | | 67.02 167 | 63.99 184 | 70.56 145 | 71.55 170 | 67.63 163 | 75.81 163 | 69.44 67 | 59.93 194 | 63.24 157 | 64.32 196 | 47.51 207 | 59.68 149 | 70.37 183 | 69.64 170 | 83.64 151 | 68.49 163 |
|
CostFormer | | | 66.81 168 | 66.94 174 | 66.67 162 | 72.79 161 | 68.25 162 | 79.55 152 | 55.57 168 | 65.52 175 | 62.77 160 | 76.98 155 | 60.09 193 | 56.73 160 | 65.69 196 | 62.35 184 | 72.59 171 | 69.71 159 |
|
PatchT | | | 66.25 169 | 66.76 175 | 65.67 169 | 55.87 201 | 60.75 180 | 70.17 181 | 59.00 155 | 59.80 196 | 72.30 122 | 78.68 145 | 54.12 203 | 65.04 133 | 71.64 179 | 72.91 160 | 71.63 175 | 69.40 160 |
|
LP | | | 65.71 170 | 69.91 166 | 60.81 178 | 56.75 200 | 61.37 179 | 69.55 187 | 56.80 164 | 73.01 148 | 60.48 164 | 79.76 141 | 70.57 186 | 55.47 165 | 72.77 176 | 67.19 175 | 65.81 188 | 64.71 171 |
|
dps | | | 65.14 171 | 64.50 182 | 65.89 168 | 71.41 171 | 65.81 170 | 71.44 179 | 61.59 143 | 58.56 197 | 61.43 163 | 75.45 167 | 52.70 206 | 58.06 156 | 69.57 185 | 64.65 179 | 71.39 176 | 64.77 170 |
|
MDTV_nov1_ep13 | | | 64.96 172 | 64.77 181 | 65.18 171 | 67.08 183 | 62.46 177 | 75.80 164 | 51.10 186 | 62.27 190 | 69.74 134 | 74.12 172 | 62.65 190 | 55.64 164 | 68.19 188 | 62.16 188 | 71.70 173 | 61.57 183 |
|
PatchmatchNet | | | 64.81 173 | 63.74 186 | 66.06 167 | 69.21 176 | 58.62 183 | 73.16 174 | 60.01 153 | 65.92 172 | 66.19 153 | 76.27 159 | 59.09 194 | 60.45 148 | 66.58 193 | 61.47 191 | 67.33 185 | 58.24 189 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tpm cat1 | | | 64.79 174 | 62.74 190 | 67.17 159 | 74.61 156 | 65.91 169 | 76.18 162 | 59.32 154 | 64.88 179 | 66.41 152 | 71.21 183 | 53.56 205 | 59.17 152 | 61.53 202 | 58.16 195 | 67.33 185 | 63.95 173 |
|
DWT-MVSNet_training | | | 63.07 175 | 60.04 197 | 66.61 163 | 71.64 169 | 65.27 171 | 76.80 159 | 53.82 175 | 55.90 200 | 63.07 158 | 62.23 200 | 41.87 210 | 62.54 143 | 64.32 199 | 63.71 181 | 71.78 172 | 66.97 166 |
|
MIMVSNet | | | 63.02 176 | 69.02 169 | 56.01 185 | 68.20 178 | 59.26 182 | 70.01 183 | 53.79 176 | 71.56 156 | 41.26 196 | 71.38 181 | 82.38 161 | 36.38 193 | 71.43 181 | 67.32 174 | 66.45 187 | 59.83 186 |
|
TAMVS | | | 63.02 176 | 69.30 168 | 55.70 187 | 70.12 173 | 56.89 186 | 69.63 186 | 45.13 190 | 70.23 159 | 38.00 202 | 77.79 147 | 75.15 180 | 42.60 187 | 74.48 164 | 72.81 162 | 68.70 183 | 57.75 191 |
|
tpm | | | 62.79 178 | 63.25 187 | 62.26 175 | 70.09 174 | 53.78 192 | 71.65 178 | 47.31 188 | 65.72 174 | 76.70 99 | 80.62 134 | 56.40 201 | 48.11 181 | 64.20 200 | 58.54 193 | 59.70 197 | 63.47 175 |
|
pmmvs3 | | | 62.72 179 | 68.71 170 | 55.74 186 | 50.74 206 | 57.10 185 | 70.05 182 | 28.82 205 | 61.57 193 | 57.39 167 | 71.19 184 | 85.73 148 | 53.96 168 | 73.36 174 | 69.43 171 | 73.47 169 | 62.55 179 |
|
new-patchmatchnet | | | 62.59 180 | 73.79 161 | 49.53 198 | 76.98 146 | 53.57 193 | 53.46 208 | 54.64 170 | 85.43 65 | 28.81 208 | 91.94 42 | 96.41 35 | 25.28 205 | 76.80 156 | 53.66 202 | 57.99 199 | 58.69 188 |
|
test-LLR | | | 62.15 181 | 59.46 201 | 65.29 170 | 79.07 134 | 52.66 195 | 69.46 189 | 62.93 136 | 50.76 207 | 53.81 179 | 63.11 198 | 58.91 195 | 52.87 172 | 66.54 194 | 62.34 185 | 73.59 167 | 61.87 181 |
|
PMMVS | | | 61.98 182 | 65.61 178 | 57.74 182 | 45.03 208 | 51.76 199 | 69.54 188 | 35.05 200 | 55.49 202 | 55.32 173 | 68.23 193 | 78.39 172 | 58.09 155 | 70.21 184 | 71.56 165 | 83.42 153 | 63.66 174 |
|
test0.0.03 1 | | | 61.79 183 | 65.33 179 | 57.65 183 | 79.07 134 | 64.09 174 | 68.51 193 | 62.93 136 | 61.59 192 | 33.71 204 | 61.58 201 | 71.58 185 | 33.43 196 | 70.95 182 | 68.68 172 | 68.26 184 | 58.82 187 |
|
test1235678 | | | 60.73 184 | 68.46 171 | 51.71 195 | 61.76 191 | 56.73 188 | 73.40 172 | 42.24 194 | 67.34 169 | 39.55 198 | 70.90 185 | 92.54 103 | 28.75 200 | 73.84 168 | 66.00 177 | 64.57 190 | 51.90 197 |
|
testmv | | | 60.72 185 | 68.44 172 | 51.71 195 | 61.76 191 | 56.70 189 | 73.40 172 | 42.24 194 | 67.31 170 | 39.54 199 | 70.88 186 | 92.49 105 | 28.75 200 | 73.83 169 | 66.00 177 | 64.56 191 | 51.89 198 |
|
MVS-HIRNet | | | 59.74 186 | 58.74 204 | 60.92 177 | 57.74 199 | 45.81 206 | 56.02 206 | 58.69 157 | 55.69 201 | 65.17 154 | 70.86 187 | 71.66 183 | 56.75 159 | 61.11 203 | 53.74 201 | 71.17 177 | 52.28 196 |
|
tpmrst | | | 59.42 187 | 60.02 198 | 58.71 181 | 67.56 181 | 53.10 194 | 66.99 194 | 51.88 182 | 63.80 182 | 57.68 166 | 76.73 156 | 56.49 200 | 48.73 180 | 56.47 206 | 55.55 198 | 59.43 198 | 58.02 190 |
|
test-mter | | | 59.39 188 | 61.59 192 | 56.82 184 | 53.21 202 | 54.82 190 | 73.12 175 | 26.57 207 | 53.19 204 | 56.31 169 | 64.71 194 | 60.47 192 | 56.36 161 | 68.69 187 | 64.27 180 | 75.38 166 | 65.00 169 |
|
E-PMN | | | 59.07 189 | 62.79 189 | 54.72 188 | 67.01 185 | 47.81 205 | 60.44 203 | 43.40 191 | 72.95 150 | 44.63 190 | 70.42 191 | 73.17 182 | 58.73 154 | 80.97 144 | 51.98 203 | 54.14 203 | 42.26 206 |
|
EMVS | | | 58.97 190 | 62.63 191 | 54.70 189 | 66.26 188 | 48.71 201 | 61.74 201 | 42.71 192 | 72.80 152 | 46.00 189 | 73.01 177 | 71.66 183 | 57.91 157 | 80.41 148 | 50.68 206 | 53.55 204 | 41.11 207 |
|
testus | | | 57.41 191 | 64.98 180 | 48.58 200 | 59.39 195 | 57.17 184 | 68.81 192 | 32.86 202 | 62.32 189 | 43.25 191 | 57.59 203 | 88.49 140 | 24.19 206 | 71.68 178 | 63.20 182 | 62.99 193 | 54.42 194 |
|
TESTMET0.1,1 | | | 57.21 192 | 59.46 201 | 54.60 190 | 50.95 205 | 52.66 195 | 69.46 189 | 26.91 206 | 50.76 207 | 53.81 179 | 63.11 198 | 58.91 195 | 52.87 172 | 66.54 194 | 62.34 185 | 73.59 167 | 61.87 181 |
|
ADS-MVSNet | | | 56.89 193 | 61.09 193 | 52.00 193 | 59.48 194 | 48.10 204 | 58.02 204 | 54.37 173 | 72.82 151 | 49.19 187 | 75.32 168 | 65.97 188 | 37.96 192 | 59.34 205 | 54.66 200 | 52.99 205 | 51.42 199 |
|
EPMVS | | | 56.62 194 | 59.77 199 | 52.94 192 | 62.41 190 | 50.55 200 | 60.66 202 | 52.83 180 | 65.15 178 | 41.80 194 | 77.46 152 | 57.28 197 | 42.68 186 | 59.81 204 | 54.82 199 | 57.23 200 | 53.35 195 |
|
FMVSNet5 | | | 56.37 195 | 60.14 196 | 51.98 194 | 60.83 193 | 59.58 181 | 66.85 195 | 42.37 193 | 52.68 205 | 41.33 195 | 47.09 208 | 54.68 202 | 35.28 194 | 73.88 167 | 70.77 166 | 65.24 189 | 62.26 180 |
|
CHOSEN 280x420 | | | 56.32 196 | 58.85 203 | 53.36 191 | 51.63 204 | 39.91 209 | 69.12 191 | 38.61 199 | 56.29 199 | 36.79 203 | 48.84 207 | 62.59 191 | 63.39 141 | 73.61 172 | 67.66 173 | 60.61 195 | 63.07 178 |
|
testpf | | | 55.64 197 | 50.84 206 | 61.24 176 | 67.03 184 | 54.45 191 | 72.29 176 | 65.04 109 | 37.23 209 | 54.99 176 | 53.99 204 | 43.12 209 | 44.34 185 | 55.22 207 | 51.59 205 | 63.76 192 | 60.25 185 |
|
1111 | | | 55.38 198 | 59.51 200 | 50.57 197 | 72.41 165 | 48.16 202 | 69.76 184 | 57.08 161 | 76.79 136 | 32.10 205 | 80.12 135 | 35.41 212 | 25.87 202 | 67.23 189 | 57.74 196 | 46.17 207 | 51.09 200 |
|
N_pmnet | | | 54.95 199 | 65.90 177 | 42.18 203 | 66.37 186 | 43.86 208 | 57.92 205 | 39.79 198 | 79.54 121 | 17.24 212 | 86.31 104 | 87.91 142 | 25.44 204 | 64.68 197 | 51.76 204 | 46.33 206 | 47.23 202 |
|
test12356 | | | 54.63 200 | 63.78 185 | 43.96 201 | 51.77 203 | 51.90 198 | 65.92 196 | 30.12 203 | 62.44 188 | 30.38 207 | 64.65 195 | 89.07 134 | 30.62 198 | 73.53 173 | 62.11 189 | 54.92 201 | 42.78 205 |
|
new_pmnet | | | 52.29 201 | 63.16 188 | 39.61 205 | 58.89 197 | 44.70 207 | 48.78 210 | 34.73 201 | 65.88 173 | 17.85 211 | 73.42 174 | 80.00 167 | 23.06 207 | 67.00 192 | 62.28 187 | 54.36 202 | 48.81 201 |
|
MVE | | 41.12 19 | 51.80 202 | 60.92 194 | 41.16 204 | 35.21 210 | 34.14 211 | 48.45 211 | 41.39 196 | 69.11 163 | 19.53 210 | 63.33 197 | 73.80 181 | 63.56 138 | 67.19 191 | 61.51 190 | 38.85 208 | 57.38 192 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test2356 | | | 51.28 203 | 53.40 205 | 48.80 199 | 58.53 198 | 52.10 197 | 63.63 199 | 40.83 197 | 51.94 206 | 39.35 201 | 53.46 205 | 45.22 208 | 28.78 199 | 64.39 198 | 60.77 192 | 61.70 194 | 45.92 203 |
|
PMMVS2 | | | 48.13 204 | 64.06 183 | 29.55 206 | 44.06 209 | 36.69 210 | 51.95 209 | 29.97 204 | 74.75 146 | 8.90 214 | 76.02 164 | 91.24 123 | 7.53 208 | 73.78 170 | 55.91 197 | 34.87 209 | 40.01 208 |
|
.test1245 | | | 43.71 205 | 44.35 207 | 42.95 202 | 72.41 165 | 48.16 202 | 69.76 184 | 57.08 161 | 76.79 136 | 32.10 205 | 80.12 135 | 35.41 212 | 25.87 202 | 67.23 189 | 1.08 209 | 0.48 212 | 1.68 209 |
|
GG-mvs-BLEND | | | 41.63 206 | 60.36 195 | 19.78 207 | 0.14 214 | 66.04 168 | 55.66 207 | 0.17 212 | 57.64 198 | 2.42 215 | 51.82 206 | 69.42 187 | 0.28 212 | 64.11 201 | 58.29 194 | 60.02 196 | 55.18 193 |
|
test123 | | | 1.06 207 | 1.41 208 | 0.64 209 | 0.39 212 | 0.48 214 | 0.52 216 | 0.25 211 | 1.11 213 | 1.37 216 | 2.01 212 | 1.98 216 | 0.87 210 | 1.43 210 | 1.27 208 | 0.46 214 | 1.62 211 |
|
testmvs | | | 0.93 208 | 1.37 209 | 0.41 210 | 0.36 213 | 0.36 215 | 0.62 215 | 0.39 210 | 1.48 212 | 0.18 217 | 2.41 211 | 1.31 217 | 0.41 211 | 1.25 211 | 1.08 209 | 0.48 212 | 1.68 209 |
|
sosnet-low-res | | | 0.00 209 | 0.00 210 | 0.00 211 | 0.00 215 | 0.00 216 | 0.00 217 | 0.00 213 | 0.00 214 | 0.00 218 | 0.00 213 | 0.00 218 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 212 |
|
sosnet | | | 0.00 209 | 0.00 210 | 0.00 211 | 0.00 215 | 0.00 216 | 0.00 217 | 0.00 213 | 0.00 214 | 0.00 218 | 0.00 213 | 0.00 218 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 212 |
|
ambc | | | | 88.38 57 | | 91.62 15 | 87.97 46 | 84.48 125 | | 88.64 40 | 87.93 16 | 87.38 93 | 94.82 82 | 74.53 70 | 89.14 79 | 83.86 98 | 85.94 138 | 86.84 71 |
|
MTAPA | | | | | | | | | | | 89.37 9 | | 94.85 80 | | | | | |
|
MTMP | | | | | | | | | | | 90.54 5 | | 95.16 72 | | | | | |
|
Patchmatch-RL test | | | | | | | | 4.13 214 | | | | | | | | | | |
|
tmp_tt | | | | | 13.54 208 | 16.73 211 | 6.42 213 | 8.49 213 | 2.36 209 | 28.69 211 | 27.44 209 | 18.40 210 | 13.51 215 | 3.70 209 | 33.23 208 | 36.26 207 | 22.54 211 | |
|
XVS | | | | | | 91.28 22 | 91.23 8 | 96.89 2 | | | 87.14 27 | | 94.53 84 | | | | 95.84 15 | |
|
X-MVStestdata | | | | | | 91.28 22 | 91.23 8 | 96.89 2 | | | 87.14 27 | | 94.53 84 | | | | 95.84 15 | |
|
abl_6 | | | | | 79.30 97 | 84.98 72 | 85.78 61 | 90.50 59 | 66.88 86 | 77.08 135 | 74.02 115 | 73.29 176 | 89.34 132 | 68.94 117 | | | 90.49 79 | 85.98 76 |
|
mPP-MVS | | | | | | 93.05 4 | | | | | | | 95.77 54 | | | | | |
|
NP-MVS | | | | | | | | | | 78.65 130 | | | | | | | | |
|
Patchmtry | | | | | | | 56.88 187 | 64.47 197 | 67.74 82 | | 72.30 122 | | | | | | | |
|
DeepMVS_CX | | | | | | | 17.78 212 | 20.40 212 | 6.69 208 | 31.41 210 | 9.80 213 | 38.61 209 | 34.88 214 | 33.78 195 | 28.41 209 | | 23.59 210 | 45.77 204 |
|