MCST-MVS | | | 85.13 16 | 86.62 17 | 83.39 11 | 90.55 9 | 89.82 10 | 89.29 14 | 73.89 16 | 84.38 24 | 76.03 21 | 79.01 24 | 85.90 14 | 78.47 7 | 87.81 11 | 86.11 25 | 92.11 1 | 93.29 16 |
|
CSCG | | | 85.28 15 | 87.68 12 | 82.49 18 | 89.95 17 | 91.99 1 | 88.82 17 | 71.20 29 | 86.41 15 | 79.63 9 | 79.26 22 | 88.36 4 | 73.94 31 | 86.64 24 | 86.67 18 | 91.40 2 | 94.41 2 |
|
SteuartSystems-ACMMP | | | 85.99 9 | 88.31 10 | 83.27 14 | 90.73 5 | 89.84 8 | 90.27 7 | 74.31 9 | 84.56 23 | 75.88 22 | 87.32 9 | 85.04 17 | 77.31 17 | 89.01 3 | 88.46 2 | 91.14 3 | 93.96 6 |
Skip Steuart: Steuart Systems R&D Blog. |
APDe-MVS | | | 88.00 1 | 90.50 1 | 85.08 1 | 90.95 4 | 91.58 3 | 92.03 1 | 75.53 7 | 91.15 1 | 80.10 7 | 92.27 2 | 88.34 5 | 80.80 2 | 88.00 9 | 86.99 12 | 91.09 4 | 95.16 1 |
|
DeepC-MVS | | 78.47 2 | 84.81 19 | 86.03 22 | 83.37 12 | 89.29 24 | 90.38 5 | 88.61 19 | 76.50 1 | 86.25 16 | 77.22 17 | 75.12 32 | 80.28 34 | 77.59 15 | 88.39 5 | 88.17 5 | 91.02 5 | 93.66 12 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
ACMMPR | | | 85.52 11 | 87.53 13 | 83.17 15 | 90.13 13 | 89.27 14 | 89.30 13 | 73.97 14 | 86.89 13 | 77.14 18 | 86.09 11 | 83.18 23 | 77.74 13 | 87.42 14 | 87.20 10 | 90.77 6 | 92.63 18 |
|
ACMMP | | | 83.42 24 | 85.27 25 | 81.26 23 | 88.47 28 | 88.49 25 | 88.31 23 | 72.09 24 | 83.42 27 | 72.77 32 | 82.65 17 | 78.22 38 | 75.18 26 | 86.24 29 | 85.76 27 | 90.74 7 | 92.13 24 |
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 | | | 84.42 21 | 86.29 21 | 82.23 19 | 90.04 15 | 88.82 19 | 89.23 15 | 71.74 27 | 82.82 29 | 74.61 25 | 84.41 16 | 82.09 25 | 77.03 21 | 87.13 18 | 86.73 17 | 90.73 8 | 92.06 25 |
|
DeepC-MVS_fast | | 78.24 3 | 84.27 22 | 85.50 24 | 82.85 16 | 90.46 11 | 89.24 15 | 87.83 25 | 74.24 10 | 84.88 19 | 76.23 20 | 75.26 31 | 81.05 32 | 77.62 14 | 88.02 8 | 87.62 8 | 90.69 9 | 92.41 21 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
SD-MVS | | | 86.96 4 | 89.45 3 | 84.05 8 | 90.13 13 | 89.23 16 | 89.77 11 | 74.59 8 | 89.17 4 | 80.70 5 | 89.93 6 | 89.67 1 | 78.47 7 | 87.57 13 | 86.79 15 | 90.67 10 | 93.76 10 |
|
TSAR-MVS + MP. | | | 86.88 5 | 89.23 4 | 84.14 7 | 89.78 19 | 88.67 23 | 90.59 3 | 73.46 19 | 88.99 5 | 80.52 6 | 91.26 3 | 88.65 3 | 79.91 4 | 86.96 22 | 86.22 23 | 90.59 11 | 93.83 8 |
|
APD-MVS | | | 86.84 6 | 88.91 8 | 84.41 3 | 90.66 6 | 90.10 6 | 90.78 2 | 75.64 4 | 87.38 10 | 78.72 11 | 90.68 5 | 86.82 9 | 80.15 3 | 87.13 18 | 86.45 21 | 90.51 12 | 93.83 8 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
CP-MVS | | | 84.74 20 | 86.43 20 | 82.77 17 | 89.48 22 | 88.13 29 | 88.64 18 | 73.93 15 | 84.92 18 | 76.77 19 | 81.94 19 | 83.50 22 | 77.29 19 | 86.92 23 | 86.49 20 | 90.49 13 | 93.14 17 |
|
XVS | | | | | | 86.63 36 | 88.68 20 | 85.00 37 | | | 71.81 37 | | 81.92 26 | | | | 90.47 14 | |
|
X-MVStestdata | | | | | | 86.63 36 | 88.68 20 | 85.00 37 | | | 71.81 37 | | 81.92 26 | | | | 90.47 14 | |
|
X-MVS | | | 83.23 25 | 85.20 26 | 80.92 26 | 89.71 20 | 88.68 20 | 88.21 24 | 73.60 17 | 82.57 30 | 71.81 37 | 77.07 26 | 81.92 26 | 71.72 45 | 86.98 21 | 86.86 13 | 90.47 14 | 92.36 22 |
|
NCCC | | | 85.34 13 | 86.59 18 | 83.88 9 | 91.48 2 | 88.88 17 | 89.79 10 | 75.54 6 | 86.67 14 | 77.94 16 | 76.55 28 | 84.99 18 | 78.07 10 | 88.04 7 | 87.68 7 | 90.46 17 | 93.31 15 |
|
CNVR-MVS | | | 86.36 8 | 88.19 11 | 84.23 5 | 91.33 3 | 89.84 8 | 90.34 5 | 75.56 5 | 87.36 11 | 78.97 10 | 81.19 21 | 86.76 10 | 78.74 6 | 89.30 2 | 88.58 1 | 90.45 18 | 94.33 4 |
|
MP-MVS | | | 85.50 12 | 87.40 14 | 83.28 13 | 90.65 7 | 89.51 13 | 89.16 16 | 74.11 12 | 83.70 26 | 78.06 15 | 85.54 13 | 84.89 20 | 77.31 17 | 87.40 15 | 87.14 11 | 90.41 19 | 93.65 13 |
|
3Dnovator+ | | 75.73 4 | 82.40 27 | 82.76 30 | 81.97 21 | 88.02 29 | 89.67 11 | 86.60 29 | 71.48 28 | 81.28 35 | 78.18 14 | 64.78 64 | 77.96 40 | 77.13 20 | 87.32 16 | 86.83 14 | 90.41 19 | 91.48 29 |
|
ACMMP_Plus | | | 86.52 7 | 89.01 5 | 83.62 10 | 90.28 12 | 90.09 7 | 90.32 6 | 74.05 13 | 88.32 8 | 79.74 8 | 87.04 10 | 85.59 16 | 76.97 22 | 89.35 1 | 88.44 3 | 90.35 21 | 94.27 5 |
|
MPTG | | | 85.71 10 | 86.88 16 | 84.34 4 | 90.54 10 | 87.11 34 | 89.77 11 | 74.17 11 | 88.54 7 | 83.08 2 | 78.60 25 | 86.10 12 | 78.11 9 | 87.80 12 | 87.46 9 | 90.35 21 | 92.56 19 |
|
CDPH-MVS | | | 82.64 26 | 85.03 27 | 79.86 31 | 89.41 23 | 88.31 26 | 88.32 22 | 71.84 26 | 80.11 37 | 67.47 51 | 82.09 18 | 81.44 30 | 71.85 43 | 85.89 30 | 86.15 24 | 90.24 23 | 91.25 31 |
|
LGP-MVS_train | | | 79.83 34 | 81.22 37 | 78.22 40 | 86.28 39 | 85.36 48 | 86.76 28 | 69.59 38 | 77.34 42 | 65.14 58 | 75.68 30 | 70.79 61 | 71.37 48 | 84.60 36 | 84.01 37 | 90.18 24 | 90.74 34 |
|
HPM-MVS++ | | | 87.09 3 | 88.92 7 | 84.95 2 | 92.61 1 | 87.91 30 | 90.23 8 | 76.06 2 | 88.85 6 | 81.20 4 | 87.33 8 | 87.93 6 | 79.47 5 | 88.59 4 | 88.23 4 | 90.15 25 | 93.60 14 |
|
3Dnovator | | 73.76 5 | 79.75 36 | 80.52 41 | 78.84 35 | 84.94 49 | 87.35 31 | 84.43 42 | 65.54 62 | 78.29 41 | 73.97 26 | 63.00 70 | 75.62 47 | 74.07 30 | 85.00 35 | 85.34 30 | 90.11 26 | 89.04 43 |
|
ACMP | | 73.23 7 | 79.79 35 | 80.53 40 | 78.94 34 | 85.61 42 | 85.68 43 | 85.61 34 | 69.59 38 | 77.33 43 | 71.00 43 | 74.45 33 | 69.16 69 | 71.88 41 | 83.15 49 | 83.37 42 | 89.92 27 | 90.57 37 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
train_agg | | | 84.86 18 | 87.21 15 | 82.11 20 | 90.59 8 | 85.47 45 | 89.81 9 | 73.55 18 | 83.95 25 | 73.30 29 | 89.84 7 | 87.23 8 | 75.61 25 | 86.47 26 | 85.46 29 | 89.78 28 | 92.06 25 |
|
TSAR-MVS + GP. | | | 83.69 23 | 86.58 19 | 80.32 28 | 85.14 44 | 86.96 35 | 84.91 40 | 70.25 33 | 84.71 22 | 73.91 27 | 85.16 14 | 85.63 15 | 77.92 11 | 85.44 31 | 85.71 28 | 89.77 29 | 92.45 20 |
|
ACMM | | 72.26 8 | 78.86 45 | 78.13 49 | 79.71 32 | 86.89 35 | 83.40 59 | 86.02 31 | 70.50 31 | 75.28 46 | 71.49 41 | 63.01 69 | 69.26 68 | 73.57 33 | 84.11 40 | 83.98 38 | 89.76 30 | 87.84 50 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
OPM-MVS | | | 79.68 38 | 79.28 47 | 80.15 30 | 87.99 30 | 86.77 37 | 88.52 21 | 72.72 21 | 64.55 76 | 67.65 50 | 67.87 55 | 74.33 51 | 74.31 29 | 86.37 28 | 85.25 31 | 89.73 31 | 89.81 40 |
|
HSP-MVS | | | 87.45 2 | 90.22 2 | 84.22 6 | 90.00 16 | 91.80 2 | 90.59 3 | 75.80 3 | 89.93 3 | 78.35 13 | 92.54 1 | 89.18 2 | 80.89 1 | 87.99 10 | 86.29 22 | 89.70 32 | 93.85 7 |
|
abl_6 | | | | | 79.05 33 | 87.27 33 | 88.85 18 | 83.62 45 | 68.25 46 | 81.68 33 | 72.94 31 | 73.79 36 | 84.45 21 | 72.55 38 | | | 89.66 33 | 90.64 35 |
|
MVS_111021_HR | | | 80.13 33 | 81.46 35 | 78.58 37 | 85.77 41 | 85.17 49 | 83.45 46 | 69.28 41 | 74.08 51 | 70.31 44 | 74.31 34 | 75.26 48 | 73.13 35 | 86.46 27 | 85.15 32 | 89.53 34 | 89.81 40 |
|
IS_MVSNet | | | 73.33 62 | 77.34 57 | 68.65 105 | 81.29 59 | 83.47 58 | 74.45 114 | 63.58 74 | 65.75 68 | 48.49 140 | 67.11 59 | 70.61 62 | 54.63 149 | 84.51 37 | 83.58 41 | 89.48 35 | 86.34 59 |
|
DELS-MVS | | | 79.15 43 | 81.07 38 | 76.91 45 | 83.54 51 | 87.31 32 | 84.45 41 | 64.92 66 | 69.98 56 | 69.34 45 | 71.62 42 | 76.26 43 | 69.84 53 | 86.57 25 | 85.90 26 | 89.39 36 | 89.88 39 |
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 |
UniMVSNet_NR-MVSNet | | | 70.59 77 | 72.19 75 | 68.72 103 | 77.72 80 | 80.72 78 | 73.81 120 | 69.65 37 | 61.99 89 | 43.23 157 | 60.54 74 | 57.50 104 | 58.57 125 | 79.56 81 | 81.07 55 | 89.34 37 | 83.97 85 |
|
PHI-MVS | | | 82.36 28 | 85.89 23 | 78.24 39 | 86.40 38 | 89.52 12 | 85.52 35 | 69.52 40 | 82.38 32 | 65.67 56 | 81.35 20 | 82.36 24 | 73.07 36 | 87.31 17 | 86.76 16 | 89.24 38 | 91.56 28 |
|
canonicalmvs | | | 79.16 42 | 82.37 33 | 75.41 50 | 82.33 57 | 86.38 41 | 80.80 52 | 63.18 76 | 82.90 28 | 67.34 52 | 72.79 38 | 76.07 44 | 69.62 54 | 83.46 48 | 84.41 36 | 89.20 39 | 90.60 36 |
|
MSLP-MVS++ | | | 82.09 29 | 82.66 31 | 81.42 22 | 87.03 34 | 87.22 33 | 85.82 33 | 70.04 34 | 80.30 36 | 78.66 12 | 68.67 51 | 81.04 33 | 77.81 12 | 85.19 34 | 84.88 34 | 89.19 40 | 91.31 30 |
|
HQP-MVS | | | 81.19 31 | 83.27 28 | 78.76 36 | 87.40 32 | 85.45 46 | 86.95 27 | 70.47 32 | 81.31 34 | 66.91 54 | 79.24 23 | 76.63 42 | 71.67 46 | 84.43 38 | 83.78 39 | 89.19 40 | 92.05 27 |
|
EPP-MVSNet | | | 74.00 61 | 77.41 56 | 70.02 93 | 80.53 66 | 83.91 55 | 74.99 111 | 62.68 85 | 65.06 71 | 49.77 137 | 68.68 50 | 72.09 57 | 63.06 102 | 82.49 54 | 80.73 58 | 89.12 42 | 88.91 44 |
|
NR-MVSNet | | | 68.79 111 | 70.56 83 | 66.71 127 | 77.48 83 | 79.54 92 | 73.52 124 | 69.20 42 | 61.20 97 | 39.76 164 | 58.52 85 | 50.11 166 | 51.37 156 | 80.26 74 | 80.71 63 | 88.97 43 | 83.59 92 |
|
PVSNet_Blended_VisFu | | | 76.57 51 | 77.90 50 | 75.02 52 | 80.56 65 | 86.58 39 | 79.24 60 | 66.18 56 | 64.81 73 | 68.18 48 | 65.61 60 | 71.45 58 | 67.05 63 | 84.16 39 | 81.80 49 | 88.90 44 | 90.92 33 |
|
TranMVSNet+NR-MVSNet | | | 69.25 106 | 70.81 82 | 67.43 117 | 77.23 85 | 79.46 94 | 73.48 125 | 69.66 36 | 60.43 102 | 39.56 165 | 58.82 84 | 53.48 145 | 55.74 144 | 79.59 79 | 81.21 54 | 88.89 45 | 82.70 106 |
|
QAPM | | | 78.47 46 | 80.22 44 | 76.43 47 | 85.03 46 | 86.75 38 | 80.62 53 | 66.00 59 | 73.77 52 | 65.35 57 | 65.54 62 | 78.02 39 | 72.69 37 | 83.71 43 | 83.36 43 | 88.87 46 | 90.41 38 |
|
CPTT-MVS | | | 81.77 30 | 83.10 29 | 80.21 29 | 85.93 40 | 86.45 40 | 87.72 26 | 70.98 30 | 82.54 31 | 71.53 40 | 74.23 35 | 81.49 29 | 76.31 24 | 82.85 52 | 81.87 48 | 88.79 47 | 92.26 23 |
|
Effi-MVS+ | | | 75.28 57 | 76.20 61 | 74.20 58 | 81.15 60 | 83.24 60 | 81.11 50 | 63.13 78 | 66.37 62 | 60.27 72 | 64.30 66 | 68.88 73 | 70.93 51 | 81.56 58 | 81.69 50 | 88.61 48 | 87.35 53 |
|
UniMVSNet (Re) | | | 69.53 100 | 71.90 76 | 66.76 125 | 76.42 88 | 80.93 74 | 72.59 130 | 68.03 49 | 61.75 93 | 41.68 162 | 58.34 91 | 57.23 112 | 53.27 153 | 79.53 82 | 80.62 67 | 88.57 49 | 84.90 79 |
|
PCF-MVS | | 73.28 6 | 79.42 39 | 80.41 42 | 78.26 38 | 84.88 50 | 88.17 27 | 86.08 30 | 69.85 35 | 75.23 47 | 68.43 46 | 68.03 54 | 78.38 37 | 71.76 44 | 81.26 65 | 80.65 66 | 88.56 50 | 91.18 32 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MAR-MVS | | | 79.21 41 | 80.32 43 | 77.92 41 | 87.46 31 | 88.15 28 | 83.95 43 | 67.48 52 | 74.28 49 | 68.25 47 | 64.70 65 | 77.04 41 | 72.17 40 | 85.42 32 | 85.00 33 | 88.22 51 | 87.62 52 |
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 |
Fast-Effi-MVS+ | | | 73.11 64 | 73.66 67 | 72.48 63 | 77.72 80 | 80.88 77 | 78.55 80 | 58.83 134 | 65.19 70 | 60.36 71 | 59.98 78 | 62.42 91 | 71.22 49 | 81.66 56 | 80.61 68 | 88.20 52 | 84.88 80 |
|
OMC-MVS | | | 80.26 32 | 82.59 32 | 77.54 42 | 83.04 52 | 85.54 44 | 83.25 47 | 65.05 65 | 87.32 12 | 72.42 33 | 72.04 40 | 78.97 36 | 73.30 34 | 83.86 41 | 81.60 51 | 88.15 53 | 88.83 45 |
|
AdaColmap | | | 79.74 37 | 78.62 48 | 81.05 25 | 89.23 25 | 86.06 42 | 84.95 39 | 71.96 25 | 79.39 40 | 75.51 23 | 63.16 68 | 68.84 74 | 76.51 23 | 83.55 45 | 82.85 44 | 88.13 54 | 86.46 58 |
|
OpenMVS | | 70.44 10 | 76.15 54 | 76.82 60 | 75.37 51 | 85.01 47 | 84.79 51 | 78.99 65 | 62.07 89 | 71.27 55 | 67.88 49 | 57.91 94 | 72.36 56 | 70.15 52 | 82.23 55 | 81.41 52 | 88.12 55 | 87.78 51 |
|
UA-Net | | | 74.47 59 | 77.80 51 | 70.59 78 | 85.33 43 | 85.40 47 | 73.54 123 | 65.98 60 | 60.65 100 | 56.00 102 | 72.11 39 | 79.15 35 | 54.63 149 | 83.13 50 | 82.25 46 | 88.04 56 | 81.92 116 |
|
DU-MVS | | | 69.63 95 | 70.91 81 | 68.13 109 | 75.99 91 | 79.54 92 | 73.81 120 | 69.20 42 | 61.20 97 | 43.23 157 | 58.52 85 | 53.50 143 | 58.57 125 | 79.22 85 | 80.45 69 | 87.97 57 | 83.97 85 |
|
FC-MVSNet-train | | | 72.60 67 | 75.07 65 | 69.71 97 | 81.10 62 | 78.79 98 | 73.74 122 | 65.23 64 | 66.10 65 | 53.34 115 | 70.36 44 | 63.40 88 | 56.92 139 | 81.44 59 | 80.96 56 | 87.93 58 | 84.46 82 |
|
IB-MVS | | 66.94 12 | 71.21 73 | 71.66 78 | 70.68 75 | 79.18 72 | 82.83 64 | 72.61 129 | 61.77 93 | 59.66 107 | 63.44 65 | 53.26 139 | 59.65 98 | 59.16 124 | 76.78 122 | 82.11 47 | 87.90 59 | 87.33 54 |
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 |
PVSNet_BlendedMVS | | | 76.21 52 | 77.52 54 | 74.69 55 | 79.46 70 | 83.79 56 | 77.50 94 | 64.34 70 | 69.88 57 | 71.88 35 | 68.54 52 | 70.42 63 | 67.05 63 | 83.48 46 | 79.63 74 | 87.89 60 | 86.87 56 |
|
PVSNet_Blended | | | 76.21 52 | 77.52 54 | 74.69 55 | 79.46 70 | 83.79 56 | 77.50 94 | 64.34 70 | 69.88 57 | 71.88 35 | 68.54 52 | 70.42 63 | 67.05 63 | 83.48 46 | 79.63 74 | 87.89 60 | 86.87 56 |
|
DeepPCF-MVS | | 79.04 1 | 85.30 14 | 88.93 6 | 81.06 24 | 88.77 27 | 90.48 4 | 85.46 36 | 73.08 20 | 90.97 2 | 73.77 28 | 84.81 15 | 85.95 13 | 77.43 16 | 88.22 6 | 87.73 6 | 87.85 62 | 94.34 3 |
|
TAPA-MVS | | 71.42 9 | 77.69 49 | 80.05 45 | 74.94 53 | 80.68 64 | 84.52 52 | 81.36 49 | 63.14 77 | 84.77 20 | 64.82 60 | 68.72 49 | 75.91 46 | 71.86 42 | 81.62 57 | 79.55 78 | 87.80 63 | 85.24 72 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
MVS_Test | | | 75.37 56 | 77.13 59 | 73.31 61 | 79.07 73 | 81.32 70 | 79.98 55 | 60.12 115 | 69.72 59 | 64.11 62 | 70.53 43 | 73.22 53 | 68.90 57 | 80.14 75 | 79.48 80 | 87.67 64 | 85.50 67 |
|
DI_MVS_plusplus_trai | | | 75.13 58 | 76.12 62 | 73.96 59 | 78.18 78 | 81.55 67 | 80.97 51 | 62.54 87 | 68.59 60 | 65.13 59 | 61.43 71 | 74.81 49 | 69.32 56 | 81.01 69 | 79.59 76 | 87.64 65 | 85.89 61 |
|
MVSTER | | | 72.06 68 | 74.24 66 | 69.51 98 | 70.39 153 | 75.97 134 | 76.91 98 | 57.36 144 | 64.64 75 | 61.39 69 | 68.86 48 | 63.76 86 | 63.46 99 | 81.44 59 | 79.70 73 | 87.56 66 | 85.31 71 |
|
TSAR-MVS + ACMM | | | 85.10 17 | 88.81 9 | 80.77 27 | 89.55 21 | 88.53 24 | 88.59 20 | 72.55 22 | 87.39 9 | 71.90 34 | 90.95 4 | 87.55 7 | 74.57 27 | 87.08 20 | 86.54 19 | 87.47 67 | 93.67 11 |
|
CLD-MVS | | | 79.35 40 | 81.23 36 | 77.16 44 | 85.01 47 | 86.92 36 | 85.87 32 | 60.89 98 | 80.07 39 | 75.35 24 | 72.96 37 | 73.21 54 | 68.43 61 | 85.41 33 | 84.63 35 | 87.41 68 | 85.44 69 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
Effi-MVS+-dtu | | | 71.82 69 | 71.86 77 | 71.78 64 | 78.77 74 | 80.47 86 | 78.55 80 | 61.67 95 | 60.68 99 | 55.49 103 | 58.48 87 | 65.48 82 | 68.85 58 | 76.92 119 | 75.55 133 | 87.35 69 | 85.46 68 |
|
WR-MVS | | | 63.03 146 | 67.40 128 | 57.92 165 | 75.14 97 | 77.60 110 | 60.56 176 | 66.10 57 | 54.11 153 | 23.88 187 | 53.94 133 | 53.58 142 | 34.50 183 | 73.93 139 | 77.71 91 | 87.35 69 | 80.94 122 |
|
v13 | | | 69.52 101 | 68.76 117 | 70.41 85 | 74.88 101 | 77.02 121 | 78.52 84 | 58.86 128 | 56.61 135 | 56.91 90 | 54.00 132 | 56.17 124 | 66.11 87 | 77.93 96 | 76.74 113 | 87.21 71 | 82.83 99 |
|
v12 | | | 69.54 99 | 68.79 115 | 70.41 85 | 74.88 101 | 77.03 119 | 78.54 83 | 58.85 130 | 56.71 133 | 56.87 92 | 54.13 130 | 56.23 123 | 66.15 83 | 77.89 97 | 76.74 113 | 87.17 72 | 82.80 100 |
|
v144192 | | | 69.34 105 | 68.68 118 | 70.12 91 | 74.06 124 | 80.54 82 | 78.08 90 | 60.54 103 | 54.99 147 | 54.13 109 | 52.92 144 | 52.80 150 | 66.73 71 | 77.13 117 | 76.72 118 | 87.15 73 | 85.63 62 |
|
EPNet | | | 79.08 44 | 80.62 39 | 77.28 43 | 88.90 26 | 83.17 62 | 83.65 44 | 72.41 23 | 74.41 48 | 67.15 53 | 76.78 27 | 74.37 50 | 64.43 96 | 83.70 44 | 83.69 40 | 87.15 73 | 88.19 47 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EG-PatchMatch MVS | | | 67.24 127 | 66.94 131 | 67.60 115 | 78.73 75 | 81.35 69 | 73.28 127 | 59.49 120 | 46.89 179 | 51.42 126 | 43.65 172 | 53.49 144 | 55.50 146 | 81.38 61 | 80.66 65 | 87.15 73 | 81.17 121 |
|
v1144 | | | 69.93 94 | 69.36 106 | 70.61 77 | 74.89 100 | 80.93 74 | 79.11 63 | 60.64 100 | 55.97 139 | 55.31 105 | 53.85 134 | 54.14 137 | 66.54 73 | 78.10 95 | 77.44 97 | 87.14 76 | 85.09 74 |
|
anonymousdsp | | | 65.28 134 | 67.98 122 | 62.13 146 | 58.73 188 | 73.98 146 | 67.10 151 | 50.69 171 | 48.41 175 | 47.66 147 | 54.27 123 | 52.75 151 | 61.45 113 | 76.71 123 | 80.20 71 | 87.13 77 | 89.53 42 |
|
V9 | | | 69.58 98 | 68.83 113 | 70.46 82 | 74.85 104 | 77.04 117 | 78.65 78 | 58.85 130 | 56.83 132 | 57.12 88 | 54.26 125 | 56.31 118 | 66.14 85 | 77.83 99 | 76.76 108 | 87.13 77 | 82.79 102 |
|
PLC | | 68.99 11 | 75.68 55 | 75.31 64 | 76.12 49 | 82.94 53 | 81.26 71 | 79.94 56 | 66.10 57 | 77.15 44 | 66.86 55 | 59.13 83 | 68.53 75 | 73.73 32 | 80.38 72 | 79.04 82 | 87.13 77 | 81.68 118 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
v7 | | | 70.33 83 | 69.87 91 | 70.88 66 | 74.79 107 | 81.04 73 | 79.22 61 | 60.57 102 | 57.70 123 | 56.65 98 | 54.23 127 | 55.29 130 | 66.95 66 | 78.28 93 | 77.47 95 | 87.12 80 | 85.05 76 |
|
v10 | | | 70.22 85 | 69.76 95 | 70.74 72 | 74.79 107 | 80.30 89 | 79.22 61 | 59.81 118 | 57.71 122 | 56.58 99 | 54.22 129 | 55.31 128 | 66.95 66 | 78.28 93 | 77.47 95 | 87.12 80 | 85.07 75 |
|
V14 | | | 69.59 97 | 68.86 112 | 70.45 84 | 74.83 105 | 77.04 117 | 78.70 77 | 58.83 134 | 56.95 129 | 57.08 89 | 54.41 121 | 56.34 117 | 66.15 83 | 77.77 100 | 76.76 108 | 87.08 82 | 82.74 105 |
|
v1192 | | | 69.50 102 | 68.83 113 | 70.29 88 | 74.49 120 | 80.92 76 | 78.55 80 | 60.54 103 | 55.04 145 | 54.21 108 | 52.79 145 | 52.33 154 | 66.92 68 | 77.88 98 | 77.35 100 | 87.04 83 | 85.51 66 |
|
v15 | | | 69.61 96 | 68.88 111 | 70.46 82 | 74.81 106 | 77.03 119 | 78.75 76 | 58.83 134 | 57.06 126 | 57.18 87 | 54.55 120 | 56.37 116 | 66.13 86 | 77.70 101 | 76.76 108 | 87.03 84 | 82.69 107 |
|
v11 | | | 69.37 104 | 68.65 119 | 70.20 89 | 74.87 103 | 76.97 122 | 78.29 87 | 58.55 138 | 56.38 137 | 56.04 101 | 54.02 131 | 54.98 132 | 66.47 74 | 78.30 92 | 76.91 105 | 86.97 85 | 83.02 98 |
|
v1921920 | | | 69.03 108 | 68.32 120 | 69.86 94 | 74.03 125 | 80.37 87 | 77.55 92 | 60.25 110 | 54.62 148 | 53.59 114 | 52.36 147 | 51.50 159 | 66.75 70 | 77.17 116 | 76.69 123 | 86.96 86 | 85.56 63 |
|
v2v482 | | | 70.05 89 | 69.46 100 | 70.74 72 | 74.62 119 | 80.32 88 | 79.00 64 | 60.62 101 | 57.41 124 | 56.89 91 | 55.43 107 | 55.14 131 | 66.39 75 | 77.25 115 | 77.14 102 | 86.90 87 | 83.57 95 |
|
Vis-MVSNet | | | 72.77 66 | 77.20 58 | 67.59 116 | 74.19 122 | 84.01 54 | 76.61 101 | 61.69 94 | 60.62 101 | 50.61 131 | 70.25 45 | 71.31 60 | 55.57 145 | 83.85 42 | 82.28 45 | 86.90 87 | 88.08 48 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
v1 | | | 69.97 91 | 69.45 102 | 70.59 78 | 74.78 109 | 80.51 83 | 78.84 68 | 60.30 106 | 56.98 127 | 56.81 93 | 54.69 117 | 56.29 120 | 65.91 91 | 77.37 113 | 76.71 121 | 86.89 89 | 83.59 92 |
|
v1141 | | | 69.96 93 | 69.44 103 | 70.58 80 | 74.78 109 | 80.50 84 | 78.85 66 | 60.30 106 | 56.95 129 | 56.74 95 | 54.68 118 | 56.26 122 | 65.93 89 | 77.38 112 | 76.72 118 | 86.88 90 | 83.57 95 |
|
divwei89l23v2f112 | | | 69.97 91 | 69.44 103 | 70.58 80 | 74.78 109 | 80.50 84 | 78.85 66 | 60.30 106 | 56.97 128 | 56.75 94 | 54.67 119 | 56.27 121 | 65.92 90 | 77.37 113 | 76.72 118 | 86.88 90 | 83.58 94 |
|
PEN-MVS | | | 62.96 147 | 65.77 140 | 59.70 158 | 73.98 126 | 75.45 135 | 63.39 169 | 67.61 51 | 52.49 158 | 25.49 186 | 53.39 136 | 49.12 171 | 40.85 176 | 71.94 152 | 77.26 101 | 86.86 92 | 80.72 124 |
|
v17 | | | 70.03 90 | 69.43 105 | 70.72 74 | 74.75 112 | 77.09 114 | 78.78 75 | 58.85 130 | 59.53 109 | 58.72 78 | 54.87 114 | 57.39 106 | 66.38 76 | 77.60 105 | 76.75 111 | 86.83 93 | 82.80 100 |
|
v1neww | | | 70.34 81 | 69.93 89 | 70.82 68 | 74.68 115 | 80.61 80 | 78.80 71 | 60.17 111 | 58.74 114 | 58.10 82 | 55.00 110 | 57.28 110 | 66.33 79 | 77.53 106 | 76.74 113 | 86.82 94 | 83.61 90 |
|
v7new | | | 70.34 81 | 69.93 89 | 70.82 68 | 74.68 115 | 80.61 80 | 78.80 71 | 60.17 111 | 58.74 114 | 58.10 82 | 55.00 110 | 57.28 110 | 66.33 79 | 77.53 106 | 76.74 113 | 86.82 94 | 83.61 90 |
|
v8 | | | 70.23 84 | 69.86 93 | 70.67 76 | 74.69 114 | 79.82 91 | 78.79 73 | 59.18 124 | 58.80 113 | 58.20 80 | 55.00 110 | 57.33 107 | 66.31 82 | 77.51 109 | 76.71 121 | 86.82 94 | 83.88 88 |
|
v6 | | | 70.35 80 | 69.94 88 | 70.83 67 | 74.68 115 | 80.62 79 | 78.81 70 | 60.16 114 | 58.81 112 | 58.17 81 | 55.01 109 | 57.31 109 | 66.32 81 | 77.53 106 | 76.73 117 | 86.82 94 | 83.62 89 |
|
ACMH+ | | 66.54 13 | 71.36 72 | 70.09 86 | 72.85 62 | 82.59 55 | 81.13 72 | 78.56 79 | 68.04 48 | 61.55 94 | 52.52 121 | 51.50 151 | 54.14 137 | 68.56 60 | 78.85 89 | 79.50 79 | 86.82 94 | 83.94 87 |
|
v16 | | | 70.07 88 | 69.46 100 | 70.79 70 | 74.74 113 | 77.08 115 | 78.79 73 | 58.86 128 | 59.75 106 | 59.15 75 | 54.87 114 | 57.33 107 | 66.38 76 | 77.61 104 | 76.77 106 | 86.81 99 | 82.79 102 |
|
v1240 | | | 68.64 113 | 67.89 124 | 69.51 98 | 73.89 127 | 80.26 90 | 76.73 99 | 59.97 117 | 53.43 155 | 53.08 116 | 51.82 150 | 50.84 162 | 66.62 72 | 76.79 121 | 76.77 106 | 86.78 100 | 85.34 70 |
|
v18 | | | 70.10 87 | 69.52 98 | 70.77 71 | 74.66 118 | 77.06 116 | 78.84 68 | 58.84 133 | 60.01 105 | 59.23 74 | 55.06 108 | 57.47 105 | 66.34 78 | 77.50 110 | 76.75 111 | 86.71 101 | 82.77 104 |
|
GBi-Net | | | 70.78 74 | 73.37 70 | 67.76 110 | 72.95 133 | 78.00 101 | 75.15 106 | 62.72 81 | 64.13 77 | 51.44 123 | 58.37 88 | 69.02 70 | 57.59 131 | 81.33 62 | 80.72 59 | 86.70 102 | 82.02 110 |
|
test1 | | | 70.78 74 | 73.37 70 | 67.76 110 | 72.95 133 | 78.00 101 | 75.15 106 | 62.72 81 | 64.13 77 | 51.44 123 | 58.37 88 | 69.02 70 | 57.59 131 | 81.33 62 | 80.72 59 | 86.70 102 | 82.02 110 |
|
FMVSNet2 | | | 70.39 79 | 72.67 74 | 67.72 113 | 72.95 133 | 78.00 101 | 75.15 106 | 62.69 84 | 63.29 82 | 51.25 127 | 55.64 104 | 68.49 76 | 57.59 131 | 80.91 70 | 80.35 70 | 86.70 102 | 82.02 110 |
|
v7n | | | 67.05 129 | 66.94 131 | 67.17 120 | 72.35 138 | 78.97 96 | 73.26 128 | 58.88 127 | 51.16 165 | 50.90 128 | 48.21 162 | 50.11 166 | 60.96 114 | 77.70 101 | 77.38 98 | 86.68 105 | 85.05 76 |
|
WR-MVS_H | | | 61.83 161 | 65.87 139 | 57.12 168 | 71.72 143 | 76.87 123 | 61.45 174 | 66.19 55 | 51.97 162 | 22.92 194 | 53.13 143 | 52.30 156 | 33.80 184 | 71.03 159 | 75.00 138 | 86.65 106 | 80.78 123 |
|
MSDG | | | 71.52 71 | 69.87 91 | 73.44 60 | 82.21 58 | 79.35 95 | 79.52 59 | 64.59 68 | 66.15 64 | 61.87 66 | 53.21 141 | 56.09 125 | 65.85 92 | 78.94 88 | 78.50 86 | 86.60 107 | 76.85 146 |
|
FMVSNet3 | | | 70.49 78 | 72.90 72 | 67.67 114 | 72.88 136 | 77.98 104 | 74.96 112 | 62.72 81 | 64.13 77 | 51.44 123 | 58.37 88 | 69.02 70 | 57.43 134 | 79.43 83 | 79.57 77 | 86.59 108 | 81.81 117 |
|
MVS_111021_LR | | | 78.13 48 | 79.85 46 | 76.13 48 | 81.12 61 | 81.50 68 | 80.28 54 | 65.25 63 | 76.09 45 | 71.32 42 | 76.49 29 | 72.87 55 | 72.21 39 | 82.79 53 | 81.29 53 | 86.59 108 | 87.91 49 |
|
DTE-MVSNet | | | 61.85 159 | 64.96 150 | 58.22 164 | 74.32 121 | 74.39 144 | 61.01 175 | 67.85 50 | 51.76 164 | 21.91 197 | 53.28 138 | 48.17 172 | 37.74 179 | 72.22 149 | 76.44 124 | 86.52 110 | 78.49 133 |
|
FMVSNet1 | | | 68.84 110 | 70.47 85 | 66.94 123 | 71.35 150 | 77.68 108 | 74.71 113 | 62.35 88 | 56.93 131 | 49.94 136 | 50.01 157 | 64.59 84 | 57.07 137 | 81.33 62 | 80.72 59 | 86.25 111 | 82.00 113 |
|
UGNet | | | 72.78 65 | 77.67 52 | 67.07 121 | 71.65 145 | 83.24 60 | 75.20 105 | 63.62 73 | 64.93 72 | 56.72 96 | 71.82 41 | 73.30 52 | 49.02 160 | 81.02 68 | 80.70 64 | 86.22 112 | 88.67 46 |
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 |
CP-MVSNet | | | 62.68 149 | 65.49 143 | 59.40 161 | 71.84 141 | 75.34 136 | 62.87 171 | 67.04 53 | 52.64 157 | 27.19 184 | 53.38 137 | 48.15 173 | 41.40 174 | 71.26 155 | 75.68 131 | 86.07 113 | 82.00 113 |
|
ACMH | | 65.37 14 | 70.71 76 | 70.00 87 | 71.54 65 | 82.51 56 | 82.47 66 | 77.78 91 | 68.13 47 | 56.19 138 | 46.06 150 | 54.30 122 | 51.20 160 | 68.68 59 | 80.66 71 | 80.72 59 | 86.07 113 | 84.45 83 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CNLPA | | | 77.20 50 | 77.54 53 | 76.80 46 | 82.63 54 | 84.31 53 | 79.77 57 | 64.64 67 | 85.17 17 | 73.18 30 | 56.37 102 | 69.81 65 | 74.53 28 | 81.12 67 | 78.69 85 | 86.04 115 | 87.29 55 |
|
TSAR-MVS + COLMAP | | | 78.34 47 | 81.64 34 | 74.48 57 | 80.13 68 | 85.01 50 | 81.73 48 | 65.93 61 | 84.75 21 | 61.68 67 | 85.79 12 | 66.27 80 | 71.39 47 | 82.91 51 | 80.78 57 | 86.01 116 | 85.98 60 |
|
LS3D | | | 74.08 60 | 73.39 69 | 74.88 54 | 85.05 45 | 82.62 65 | 79.71 58 | 68.66 44 | 72.82 53 | 58.80 77 | 57.61 95 | 61.31 93 | 71.07 50 | 80.32 73 | 78.87 84 | 86.00 117 | 80.18 126 |
|
PS-CasMVS | | | 62.38 155 | 65.06 147 | 59.25 162 | 71.73 142 | 75.21 140 | 62.77 172 | 66.99 54 | 51.94 163 | 26.96 185 | 52.00 149 | 47.52 176 | 41.06 175 | 71.16 158 | 75.60 132 | 85.97 118 | 81.97 115 |
|
v52 | | | 65.23 135 | 66.24 135 | 64.06 137 | 61.94 178 | 76.42 128 | 72.06 133 | 54.30 153 | 49.94 169 | 50.04 134 | 47.41 166 | 52.42 152 | 60.23 121 | 75.71 128 | 76.22 128 | 85.78 119 | 85.56 63 |
|
V4 | | | 65.23 135 | 66.23 136 | 64.06 137 | 61.94 178 | 76.42 128 | 72.05 134 | 54.31 152 | 49.91 171 | 50.06 133 | 47.42 165 | 52.40 153 | 60.24 120 | 75.71 128 | 76.22 128 | 85.78 119 | 85.56 63 |
|
v748 | | | 65.12 137 | 65.24 144 | 64.98 131 | 69.77 156 | 76.45 127 | 69.47 141 | 57.06 146 | 49.93 170 | 50.70 129 | 47.87 164 | 49.50 170 | 57.14 136 | 73.64 142 | 75.18 136 | 85.75 121 | 84.14 84 |
|
Vis-MVSNet (Re-imp) | | | 67.83 120 | 73.52 68 | 61.19 150 | 78.37 77 | 76.72 125 | 66.80 153 | 62.96 79 | 65.50 69 | 34.17 175 | 67.19 58 | 69.68 66 | 39.20 178 | 79.39 84 | 79.44 81 | 85.68 122 | 76.73 147 |
|
V42 | | | 68.76 112 | 69.63 96 | 67.74 112 | 64.93 174 | 78.01 100 | 78.30 86 | 56.48 148 | 58.65 116 | 56.30 100 | 54.26 125 | 57.03 113 | 64.85 95 | 77.47 111 | 77.01 104 | 85.60 123 | 84.96 78 |
|
TransMVSNet (Re) | | | 64.74 140 | 65.66 141 | 63.66 141 | 77.40 84 | 75.33 137 | 69.86 138 | 62.67 86 | 47.63 177 | 41.21 163 | 50.01 157 | 52.33 154 | 45.31 167 | 79.57 80 | 77.69 92 | 85.49 124 | 77.07 144 |
|
IterMVS-LS | | | 71.69 70 | 72.82 73 | 70.37 87 | 77.54 82 | 76.34 131 | 75.13 109 | 60.46 105 | 61.53 95 | 57.57 85 | 64.89 63 | 67.33 77 | 66.04 88 | 77.09 118 | 77.37 99 | 85.48 125 | 85.18 73 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
Baseline_NR-MVSNet | | | 67.53 125 | 68.77 116 | 66.09 128 | 75.99 91 | 74.75 142 | 72.43 131 | 68.41 45 | 61.33 96 | 38.33 168 | 51.31 152 | 54.13 139 | 56.03 141 | 79.22 85 | 78.19 88 | 85.37 126 | 82.45 108 |
|
Fast-Effi-MVS+-dtu | | | 68.34 114 | 69.47 99 | 67.01 122 | 75.15 96 | 77.97 106 | 77.12 97 | 55.40 151 | 57.87 117 | 46.68 148 | 56.17 103 | 60.39 94 | 62.36 105 | 76.32 126 | 76.25 127 | 85.35 127 | 81.34 119 |
|
GA-MVS | | | 68.14 116 | 69.17 108 | 66.93 124 | 73.77 128 | 78.50 99 | 74.45 114 | 58.28 139 | 55.11 144 | 48.44 141 | 60.08 76 | 53.99 140 | 61.50 112 | 78.43 91 | 77.57 93 | 85.13 128 | 80.54 125 |
|
v148 | | | 67.85 119 | 67.53 125 | 68.23 107 | 73.25 131 | 77.57 111 | 74.26 119 | 57.36 144 | 55.70 140 | 57.45 86 | 53.53 135 | 55.42 127 | 61.96 108 | 75.23 132 | 73.92 142 | 85.08 129 | 81.32 120 |
|
HyFIR lowres test | | | 69.47 103 | 68.94 110 | 70.09 92 | 76.77 87 | 82.93 63 | 76.63 100 | 60.17 111 | 59.00 111 | 54.03 110 | 40.54 180 | 65.23 83 | 67.89 62 | 76.54 125 | 78.30 87 | 85.03 130 | 80.07 127 |
|
gg-mvs-nofinetune | | | 62.55 150 | 65.05 148 | 59.62 159 | 78.72 76 | 77.61 109 | 70.83 137 | 53.63 154 | 39.71 191 | 22.04 196 | 36.36 184 | 64.32 85 | 47.53 162 | 81.16 66 | 79.03 83 | 85.00 131 | 77.17 141 |
|
COLMAP_ROB | | 62.73 15 | 67.66 122 | 66.76 133 | 68.70 104 | 80.49 67 | 77.98 104 | 75.29 104 | 62.95 80 | 63.62 80 | 49.96 135 | 47.32 168 | 50.72 163 | 58.57 125 | 76.87 120 | 75.50 134 | 84.94 132 | 75.33 156 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
pm-mvs1 | | | 65.62 132 | 67.42 127 | 63.53 142 | 73.66 129 | 76.39 130 | 69.66 139 | 60.87 99 | 49.73 172 | 43.97 156 | 51.24 153 | 57.00 114 | 48.16 161 | 79.89 76 | 77.84 90 | 84.85 133 | 79.82 128 |
|
gm-plane-assit | | | 57.00 174 | 57.62 180 | 56.28 171 | 76.10 90 | 62.43 187 | 47.62 196 | 46.57 184 | 33.84 199 | 23.24 190 | 37.52 181 | 40.19 188 | 59.61 123 | 79.81 77 | 77.55 94 | 84.55 134 | 72.03 167 |
|
USDC | | | 67.36 126 | 67.90 123 | 66.74 126 | 71.72 143 | 75.23 138 | 71.58 135 | 60.28 109 | 67.45 61 | 50.54 132 | 60.93 72 | 45.20 181 | 62.08 106 | 76.56 124 | 74.50 140 | 84.25 135 | 75.38 155 |
|
MS-PatchMatch | | | 70.17 86 | 70.49 84 | 69.79 95 | 80.98 63 | 77.97 106 | 77.51 93 | 58.95 126 | 62.33 87 | 55.22 106 | 53.14 142 | 65.90 81 | 62.03 107 | 79.08 87 | 77.11 103 | 84.08 136 | 77.91 136 |
|
TDRefinement | | | 66.09 131 | 65.03 149 | 67.31 118 | 69.73 157 | 76.75 124 | 75.33 102 | 64.55 69 | 60.28 103 | 49.72 138 | 45.63 170 | 42.83 183 | 60.46 119 | 75.75 127 | 75.95 130 | 84.08 136 | 78.04 135 |
|
pmmvs4 | | | 67.89 118 | 67.39 129 | 68.48 106 | 71.60 147 | 73.57 147 | 74.45 114 | 60.98 97 | 64.65 74 | 57.97 84 | 54.95 113 | 51.73 158 | 61.88 109 | 73.78 140 | 75.11 137 | 83.99 138 | 77.91 136 |
|
pmmvs5 | | | 62.37 156 | 64.04 155 | 60.42 153 | 65.03 172 | 71.67 153 | 67.17 150 | 52.70 161 | 50.30 166 | 44.80 154 | 54.23 127 | 51.19 161 | 49.37 159 | 72.88 144 | 73.48 145 | 83.45 139 | 74.55 159 |
|
pmmvs-eth3d | | | 63.52 145 | 62.44 166 | 64.77 133 | 66.82 168 | 70.12 157 | 69.41 142 | 59.48 121 | 54.34 152 | 52.71 117 | 46.24 169 | 44.35 182 | 56.93 138 | 72.37 145 | 73.77 143 | 83.30 140 | 75.91 149 |
|
pmmvs6 | | | 62.41 153 | 62.88 160 | 61.87 147 | 71.38 149 | 75.18 141 | 67.76 148 | 59.45 122 | 41.64 187 | 42.52 161 | 37.33 182 | 52.91 149 | 46.87 163 | 77.67 103 | 76.26 126 | 83.23 141 | 79.18 130 |
|
CDS-MVSNet | | | 67.65 123 | 69.83 94 | 65.09 130 | 75.39 95 | 76.55 126 | 74.42 117 | 63.75 72 | 53.55 154 | 49.37 139 | 59.41 81 | 62.45 90 | 44.44 168 | 79.71 78 | 79.82 72 | 83.17 142 | 77.36 140 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
PMMVS | | | 65.06 138 | 69.17 108 | 60.26 155 | 55.25 197 | 63.43 180 | 66.71 154 | 43.01 195 | 62.41 86 | 50.64 130 | 69.44 46 | 67.04 78 | 63.29 101 | 74.36 137 | 73.54 144 | 82.68 143 | 73.99 163 |
|
diffmvs | | | 73.13 63 | 75.65 63 | 70.19 90 | 74.07 123 | 77.17 113 | 78.24 88 | 57.45 142 | 72.44 54 | 64.02 63 | 69.05 47 | 75.92 45 | 64.86 94 | 75.18 133 | 75.27 135 | 82.47 144 | 84.53 81 |
|
SixPastTwentyTwo | | | 61.84 160 | 62.45 165 | 61.12 151 | 69.20 161 | 72.20 150 | 62.03 173 | 57.40 143 | 46.54 180 | 38.03 170 | 57.14 100 | 41.72 185 | 58.12 129 | 69.67 170 | 71.58 152 | 81.94 145 | 78.30 134 |
|
IterMVS | | | 66.36 130 | 68.30 121 | 64.10 136 | 69.48 160 | 74.61 143 | 73.41 126 | 50.79 170 | 57.30 125 | 48.28 142 | 60.64 73 | 59.92 97 | 60.85 118 | 74.14 138 | 72.66 148 | 81.80 146 | 78.82 132 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchMatch-RL | | | 67.78 121 | 66.65 134 | 69.10 101 | 73.01 132 | 72.69 149 | 68.49 145 | 61.85 92 | 62.93 85 | 60.20 73 | 56.83 101 | 50.42 164 | 69.52 55 | 75.62 130 | 74.46 141 | 81.51 147 | 73.62 165 |
|
TinyColmap | | | 62.84 148 | 61.03 172 | 64.96 132 | 69.61 158 | 71.69 152 | 68.48 146 | 59.76 119 | 55.41 141 | 47.69 146 | 47.33 167 | 34.20 193 | 62.76 104 | 74.52 135 | 72.59 149 | 81.44 148 | 71.47 168 |
|
EPNet_dtu | | | 68.08 117 | 71.00 80 | 64.67 134 | 79.64 69 | 68.62 163 | 75.05 110 | 63.30 75 | 66.36 63 | 45.27 153 | 67.40 57 | 66.84 79 | 43.64 170 | 75.37 131 | 74.98 139 | 81.15 149 | 77.44 139 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CR-MVSNet | | | 64.83 139 | 65.54 142 | 64.01 139 | 70.64 152 | 69.41 158 | 65.97 158 | 52.74 159 | 57.81 119 | 52.65 118 | 54.27 123 | 56.31 118 | 60.92 115 | 72.20 150 | 73.09 146 | 81.12 150 | 75.69 152 |
|
RPMNet | | | 61.71 163 | 62.88 160 | 60.34 154 | 69.51 159 | 69.41 158 | 63.48 168 | 49.23 174 | 57.81 119 | 45.64 152 | 50.51 155 | 50.12 165 | 53.13 154 | 68.17 177 | 68.49 169 | 81.07 151 | 75.62 154 |
|
test-mter | | | 60.84 165 | 64.62 152 | 56.42 170 | 55.99 195 | 64.18 175 | 65.39 160 | 34.23 203 | 54.39 151 | 46.21 149 | 57.40 98 | 59.49 99 | 55.86 142 | 71.02 160 | 69.65 159 | 80.87 152 | 76.20 148 |
|
test-LLR | | | 64.42 141 | 64.36 153 | 64.49 135 | 75.02 98 | 63.93 177 | 66.61 155 | 61.96 90 | 54.41 149 | 47.77 144 | 57.46 96 | 60.25 95 | 55.20 147 | 70.80 162 | 69.33 161 | 80.40 153 | 74.38 160 |
|
TESTMET0.1,1 | | | 61.10 164 | 64.36 153 | 57.29 167 | 57.53 190 | 63.93 177 | 66.61 155 | 36.22 201 | 54.41 149 | 47.77 144 | 57.46 96 | 60.25 95 | 55.20 147 | 70.80 162 | 69.33 161 | 80.40 153 | 74.38 160 |
|
Anonymous20231211 | | | 51.46 185 | 50.59 187 | 52.46 182 | 67.30 164 | 66.70 170 | 55.00 185 | 59.22 123 | 29.96 202 | 17.62 202 | 19.11 204 | 28.74 202 | 35.72 181 | 66.42 179 | 69.52 160 | 79.92 155 | 73.71 164 |
|
DWT-MVSNet_training | | | 67.24 127 | 65.96 137 | 68.74 102 | 76.15 89 | 74.36 145 | 74.37 118 | 56.66 147 | 61.82 92 | 60.51 70 | 58.23 93 | 49.76 168 | 65.07 93 | 70.04 169 | 70.39 156 | 79.70 156 | 77.11 143 |
|
CostFormer | | | 68.92 109 | 69.58 97 | 68.15 108 | 75.98 93 | 76.17 133 | 78.22 89 | 51.86 164 | 65.80 67 | 61.56 68 | 63.57 67 | 62.83 89 | 61.85 110 | 70.40 168 | 68.67 166 | 79.42 157 | 79.62 129 |
|
CVMVSNet | | | 62.55 150 | 65.89 138 | 58.64 163 | 66.95 166 | 69.15 160 | 66.49 157 | 56.29 149 | 52.46 159 | 32.70 176 | 59.27 82 | 58.21 103 | 50.09 158 | 71.77 153 | 71.39 153 | 79.31 158 | 78.99 131 |
|
CHOSEN 1792x2688 | | | 69.20 107 | 69.26 107 | 69.13 100 | 76.86 86 | 78.93 97 | 77.27 96 | 60.12 115 | 61.86 91 | 54.42 107 | 42.54 174 | 61.61 92 | 66.91 69 | 78.55 90 | 78.14 89 | 79.23 159 | 83.23 97 |
|
PM-MVS | | | 60.48 166 | 60.94 173 | 59.94 156 | 58.85 187 | 66.83 169 | 64.27 166 | 51.39 167 | 55.03 146 | 48.03 143 | 50.00 159 | 40.79 187 | 58.26 128 | 69.20 173 | 67.13 176 | 78.84 160 | 77.60 138 |
|
tpmp4_e23 | | | 68.32 115 | 67.08 130 | 69.76 96 | 77.86 79 | 75.22 139 | 78.37 85 | 56.17 150 | 66.06 66 | 64.27 61 | 57.15 99 | 54.89 133 | 63.40 100 | 70.97 161 | 68.29 171 | 78.46 161 | 77.00 145 |
|
PatchT | | | 61.97 158 | 64.04 155 | 59.55 160 | 60.49 182 | 67.40 166 | 56.54 183 | 48.65 178 | 56.69 134 | 52.65 118 | 51.10 154 | 52.14 157 | 60.92 115 | 72.20 150 | 73.09 146 | 78.03 162 | 75.69 152 |
|
RPSCF | | | 67.64 124 | 71.25 79 | 63.43 143 | 61.86 180 | 70.73 155 | 67.26 149 | 50.86 169 | 74.20 50 | 58.91 76 | 67.49 56 | 69.33 67 | 64.10 97 | 71.41 154 | 68.45 170 | 77.61 163 | 77.17 141 |
|
MDTV_nov1_ep13_2view | | | 60.16 167 | 60.51 174 | 59.75 157 | 65.39 171 | 69.05 161 | 68.00 147 | 48.29 180 | 51.99 160 | 45.95 151 | 48.01 163 | 49.64 169 | 53.39 152 | 68.83 174 | 66.52 177 | 77.47 164 | 69.55 173 |
|
MDTV_nov1_ep13 | | | 64.37 142 | 65.24 144 | 63.37 144 | 68.94 162 | 70.81 154 | 72.40 132 | 50.29 173 | 60.10 104 | 53.91 112 | 60.07 77 | 59.15 100 | 57.21 135 | 69.43 172 | 67.30 173 | 77.47 164 | 69.78 172 |
|
LTVRE_ROB | | 59.44 16 | 61.82 162 | 62.64 163 | 60.87 152 | 72.83 137 | 77.19 112 | 64.37 165 | 58.97 125 | 33.56 200 | 28.00 183 | 52.59 146 | 42.21 184 | 63.93 98 | 74.52 135 | 76.28 125 | 77.15 166 | 82.13 109 |
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 |
MDA-MVSNet-bldmvs | | | 53.37 183 | 53.01 185 | 53.79 180 | 43.67 205 | 67.95 165 | 59.69 179 | 57.92 140 | 43.69 183 | 32.41 177 | 41.47 175 | 27.89 203 | 52.38 155 | 56.97 199 | 65.99 179 | 76.68 167 | 67.13 177 |
|
MVS-HIRNet | | | 54.41 179 | 52.10 186 | 57.11 169 | 58.99 186 | 56.10 192 | 49.68 193 | 49.10 175 | 46.18 181 | 52.15 122 | 33.18 191 | 46.11 179 | 56.10 140 | 63.19 186 | 59.70 194 | 76.64 168 | 60.25 190 |
|
dps | | | 64.00 144 | 62.99 159 | 65.18 129 | 73.29 130 | 72.07 151 | 68.98 144 | 53.07 157 | 57.74 121 | 58.41 79 | 55.55 106 | 47.74 175 | 60.89 117 | 69.53 171 | 67.14 175 | 76.44 169 | 71.19 169 |
|
test0.0.03 1 | | | 58.80 170 | 61.58 170 | 55.56 173 | 75.02 98 | 68.45 164 | 59.58 180 | 61.96 90 | 52.74 156 | 29.57 179 | 49.75 160 | 54.56 135 | 31.46 186 | 71.19 156 | 69.77 158 | 75.75 170 | 64.57 181 |
|
EU-MVSNet | | | 54.63 178 | 58.69 176 | 49.90 185 | 56.99 191 | 62.70 185 | 56.41 184 | 50.64 172 | 45.95 182 | 23.14 191 | 50.42 156 | 46.51 178 | 36.63 180 | 65.51 181 | 64.85 180 | 75.57 171 | 74.91 157 |
|
PatchmatchNet | | | 64.21 143 | 64.65 151 | 63.69 140 | 71.29 151 | 68.66 162 | 69.63 140 | 51.70 166 | 63.04 83 | 53.77 113 | 59.83 80 | 58.34 102 | 60.23 121 | 68.54 175 | 66.06 178 | 75.56 172 | 68.08 176 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
Anonymous20231206 | | | 56.36 176 | 57.80 179 | 54.67 176 | 70.08 154 | 66.39 171 | 60.46 177 | 57.54 141 | 49.50 174 | 29.30 180 | 33.86 190 | 46.64 177 | 35.18 182 | 70.44 166 | 68.88 165 | 75.47 173 | 68.88 175 |
|
CMPMVS | | 47.78 17 | 62.49 152 | 62.52 164 | 62.46 145 | 70.01 155 | 70.66 156 | 62.97 170 | 51.84 165 | 51.98 161 | 56.71 97 | 42.87 173 | 53.62 141 | 57.80 130 | 72.23 148 | 70.37 157 | 75.45 174 | 75.91 149 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MIMVSNet | | | 58.52 172 | 61.34 171 | 55.22 174 | 60.76 181 | 67.01 168 | 66.81 152 | 49.02 176 | 56.43 136 | 38.90 167 | 40.59 179 | 54.54 136 | 40.57 177 | 73.16 143 | 71.65 151 | 75.30 175 | 66.00 179 |
|
TAMVS | | | 59.58 169 | 62.81 162 | 55.81 172 | 66.03 170 | 65.64 174 | 63.86 167 | 48.74 177 | 49.95 168 | 37.07 172 | 54.77 116 | 58.54 101 | 44.44 168 | 72.29 147 | 71.79 150 | 74.70 176 | 66.66 178 |
|
tpm cat1 | | | 65.41 133 | 63.81 157 | 67.28 119 | 75.61 94 | 72.88 148 | 75.32 103 | 52.85 158 | 62.97 84 | 63.66 64 | 53.24 140 | 53.29 148 | 61.83 111 | 65.54 180 | 64.14 183 | 74.43 177 | 74.60 158 |
|
test20.03 | | | 53.93 181 | 56.28 181 | 51.19 183 | 72.19 140 | 65.83 172 | 53.20 188 | 61.08 96 | 42.74 185 | 22.08 195 | 37.07 183 | 45.76 180 | 24.29 199 | 70.44 166 | 69.04 163 | 74.31 178 | 63.05 185 |
|
FMVSNet5 | | | 57.24 173 | 60.02 175 | 53.99 178 | 56.45 192 | 62.74 184 | 65.27 161 | 47.03 183 | 55.14 143 | 39.55 166 | 40.88 177 | 53.42 146 | 41.83 171 | 72.35 146 | 71.10 155 | 73.79 179 | 64.50 182 |
|
testgi | | | 54.39 180 | 57.86 178 | 50.35 184 | 71.59 148 | 67.24 167 | 54.95 186 | 53.25 156 | 43.36 184 | 23.78 188 | 44.64 171 | 47.87 174 | 24.96 195 | 70.45 165 | 68.66 167 | 73.60 180 | 62.78 186 |
|
MIMVSNet1 | | | 49.27 186 | 53.25 184 | 44.62 191 | 44.61 202 | 61.52 188 | 53.61 187 | 52.18 162 | 41.62 188 | 18.68 199 | 28.14 198 | 41.58 186 | 25.50 193 | 68.46 176 | 69.04 163 | 73.15 181 | 62.37 187 |
|
pmmvs3 | | | 47.65 187 | 49.08 191 | 45.99 189 | 44.61 202 | 54.79 195 | 50.04 191 | 31.95 206 | 33.91 198 | 29.90 178 | 30.37 192 | 33.53 194 | 46.31 165 | 63.50 184 | 63.67 184 | 73.14 182 | 63.77 184 |
|
FC-MVSNet-test | | | 56.90 175 | 65.20 146 | 47.21 187 | 66.98 165 | 63.20 182 | 49.11 194 | 58.60 137 | 59.38 110 | 11.50 207 | 65.60 61 | 56.68 115 | 24.66 198 | 71.17 157 | 71.36 154 | 72.38 183 | 69.02 174 |
|
LP | | | 53.62 182 | 53.43 182 | 53.83 179 | 58.51 189 | 62.59 186 | 57.31 182 | 46.04 187 | 47.86 176 | 42.69 160 | 36.08 186 | 36.86 191 | 46.53 164 | 64.38 183 | 64.25 182 | 71.92 184 | 62.00 188 |
|
GG-mvs-BLEND | | | 46.86 192 | 67.51 126 | 22.75 205 | 0.05 212 | 76.21 132 | 64.69 163 | 0.04 210 | 61.90 90 | 0.09 215 | 55.57 105 | 71.32 59 | 0.08 210 | 70.54 164 | 67.19 174 | 71.58 185 | 69.86 171 |
|
ambc | | | | 53.42 183 | | 64.99 173 | 63.36 181 | 49.96 192 | | 47.07 178 | 37.12 171 | 28.97 194 | 16.36 209 | 41.82 172 | 75.10 134 | 67.34 172 | 71.55 186 | 75.72 151 |
|
tpmrst | | | 62.00 157 | 62.35 167 | 61.58 148 | 71.62 146 | 64.14 176 | 69.07 143 | 48.22 182 | 62.21 88 | 53.93 111 | 58.26 92 | 55.30 129 | 55.81 143 | 63.22 185 | 62.62 186 | 70.85 187 | 70.70 170 |
|
tpm | | | 62.41 153 | 63.15 158 | 61.55 149 | 72.24 139 | 63.79 179 | 71.31 136 | 46.12 186 | 57.82 118 | 55.33 104 | 59.90 79 | 54.74 134 | 53.63 151 | 67.24 178 | 64.29 181 | 70.65 188 | 74.25 162 |
|
FPMVS | | | 51.87 184 | 50.00 189 | 54.07 177 | 66.83 167 | 57.25 190 | 60.25 178 | 50.91 168 | 50.25 167 | 34.36 174 | 36.04 187 | 32.02 195 | 41.49 173 | 58.98 197 | 56.07 197 | 70.56 189 | 59.36 192 |
|
EPMVS | | | 60.00 168 | 61.97 168 | 57.71 166 | 68.46 163 | 63.17 183 | 64.54 164 | 48.23 181 | 63.30 81 | 44.72 155 | 60.19 75 | 56.05 126 | 50.85 157 | 65.27 182 | 62.02 188 | 69.44 190 | 63.81 183 |
|
PMVS | | 39.38 18 | 46.06 193 | 43.30 198 | 49.28 186 | 62.93 175 | 38.75 206 | 41.88 199 | 53.50 155 | 33.33 201 | 35.46 173 | 28.90 195 | 31.01 198 | 33.04 185 | 58.61 198 | 54.63 200 | 68.86 191 | 57.88 195 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
test2356 | | | 47.20 190 | 48.62 193 | 45.54 190 | 56.38 193 | 54.89 194 | 50.62 190 | 45.08 190 | 38.65 192 | 23.40 189 | 36.23 185 | 31.10 197 | 29.31 189 | 62.76 187 | 62.49 187 | 68.48 192 | 54.23 198 |
|
CHOSEN 280x420 | | | 58.70 171 | 61.88 169 | 54.98 175 | 55.45 196 | 50.55 200 | 64.92 162 | 40.36 197 | 55.21 142 | 38.13 169 | 48.31 161 | 63.76 86 | 63.03 103 | 73.73 141 | 68.58 168 | 68.00 193 | 73.04 166 |
|
testus | | | 45.61 194 | 49.06 192 | 41.59 195 | 56.13 194 | 55.28 193 | 43.51 198 | 39.64 199 | 37.74 193 | 18.23 200 | 35.52 189 | 31.28 196 | 24.69 197 | 62.46 188 | 62.90 185 | 67.33 194 | 58.26 194 |
|
testmv | | | 42.58 196 | 44.36 195 | 40.49 196 | 54.63 198 | 52.76 196 | 41.21 202 | 44.37 192 | 28.83 203 | 12.87 204 | 27.16 199 | 25.03 204 | 23.01 200 | 60.83 191 | 61.13 189 | 66.88 195 | 54.81 196 |
|
test1235678 | | | 42.57 197 | 44.36 195 | 40.49 196 | 54.63 198 | 52.75 197 | 41.21 202 | 44.37 192 | 28.82 204 | 12.87 204 | 27.15 200 | 25.01 205 | 23.01 200 | 60.83 191 | 61.13 189 | 66.88 195 | 54.81 196 |
|
no-one | | | 36.35 200 | 37.59 201 | 34.91 199 | 46.13 200 | 49.89 201 | 27.99 207 | 43.56 194 | 20.91 208 | 7.03 210 | 14.64 206 | 15.50 210 | 18.92 205 | 42.95 203 | 60.20 192 | 65.84 197 | 59.03 193 |
|
new-patchmatchnet | | | 46.97 191 | 49.47 190 | 44.05 193 | 62.82 176 | 56.55 191 | 45.35 197 | 52.01 163 | 42.47 186 | 17.04 203 | 35.73 188 | 35.21 192 | 21.84 204 | 61.27 190 | 54.83 199 | 65.26 198 | 60.26 189 |
|
ADS-MVSNet | | | 55.94 177 | 58.01 177 | 53.54 181 | 62.48 177 | 58.48 189 | 59.12 181 | 46.20 185 | 59.65 108 | 42.88 159 | 52.34 148 | 53.31 147 | 46.31 165 | 62.00 189 | 60.02 193 | 64.23 199 | 60.24 191 |
|
testpf | | | 47.41 188 | 48.47 194 | 46.18 188 | 66.30 169 | 50.67 199 | 48.15 195 | 42.60 196 | 37.10 195 | 28.75 181 | 40.97 176 | 39.01 190 | 30.82 187 | 52.95 202 | 53.74 201 | 60.46 200 | 64.87 180 |
|
N_pmnet | | | 47.35 189 | 50.13 188 | 44.11 192 | 59.98 183 | 51.64 198 | 51.86 189 | 44.80 191 | 49.58 173 | 20.76 198 | 40.65 178 | 40.05 189 | 29.64 188 | 59.84 195 | 55.15 198 | 57.63 201 | 54.00 199 |
|
new_pmnet | | | 38.40 198 | 42.64 199 | 33.44 200 | 37.54 208 | 45.00 204 | 36.60 204 | 32.72 205 | 40.27 189 | 12.72 206 | 29.89 193 | 28.90 201 | 24.78 196 | 53.17 201 | 52.90 202 | 56.31 202 | 48.34 200 |
|
test12356 | | | 35.10 201 | 38.50 200 | 31.13 202 | 44.14 204 | 43.70 205 | 32.27 205 | 34.42 202 | 26.51 206 | 9.47 208 | 25.22 202 | 20.34 206 | 10.86 207 | 53.47 200 | 56.15 196 | 55.59 203 | 44.11 202 |
|
1111 | | | 43.08 195 | 44.02 197 | 41.98 194 | 59.22 184 | 49.27 202 | 41.48 200 | 45.63 188 | 35.01 196 | 23.06 192 | 28.60 196 | 30.15 199 | 27.22 190 | 60.42 193 | 57.97 195 | 55.27 204 | 46.74 201 |
|
Gipuma | | | 36.38 199 | 35.80 202 | 37.07 198 | 45.76 201 | 33.90 207 | 29.81 206 | 48.47 179 | 39.91 190 | 18.02 201 | 8.00 210 | 8.14 212 | 25.14 194 | 59.29 196 | 61.02 191 | 55.19 205 | 40.31 203 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
PMMVS2 | | | 25.60 203 | 29.75 203 | 20.76 206 | 28.00 209 | 30.93 208 | 23.10 208 | 29.18 207 | 23.14 207 | 1.46 214 | 18.23 205 | 16.54 208 | 5.08 208 | 40.22 204 | 41.40 204 | 37.76 206 | 37.79 205 |
|
E-PMN | | | 21.77 204 | 18.24 206 | 25.89 203 | 40.22 206 | 19.58 210 | 12.46 211 | 39.87 198 | 18.68 210 | 6.71 211 | 9.57 207 | 4.31 215 | 22.36 203 | 19.89 208 | 27.28 206 | 33.73 207 | 28.34 207 |
|
EMVS | | | 20.98 205 | 17.15 207 | 25.44 204 | 39.51 207 | 19.37 211 | 12.66 210 | 39.59 200 | 19.10 209 | 6.62 212 | 9.27 208 | 4.40 214 | 22.43 202 | 17.99 209 | 24.40 207 | 31.81 208 | 25.53 208 |
|
tmp_tt | | | | | 14.50 208 | 14.68 210 | 7.17 213 | 10.46 213 | 2.21 209 | 37.73 194 | 28.71 182 | 25.26 201 | 16.98 207 | 4.37 209 | 31.49 205 | 29.77 205 | 26.56 209 | |
|
MVE | | 19.12 19 | 20.47 206 | 23.27 205 | 17.20 207 | 12.66 211 | 25.41 209 | 10.52 212 | 34.14 204 | 14.79 211 | 6.53 213 | 8.79 209 | 4.68 213 | 16.64 206 | 29.49 206 | 41.63 203 | 22.73 210 | 38.11 204 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
DeepMVS_CX | | | | | | | 18.74 212 | 18.55 209 | 8.02 208 | 26.96 205 | 7.33 209 | 23.81 203 | 13.05 211 | 25.99 192 | 25.17 207 | | 22.45 211 | 36.25 206 |
|
.test1245 | | | 30.81 202 | 29.14 204 | 32.77 201 | 59.22 184 | 49.27 202 | 41.48 200 | 45.63 188 | 35.01 196 | 23.06 192 | 28.60 196 | 30.15 199 | 27.22 190 | 60.42 193 | 0.10 208 | 0.01 212 | 0.43 210 |
|
testmvs | | | 0.09 207 | 0.15 208 | 0.02 209 | 0.01 213 | 0.02 214 | 0.05 215 | 0.01 211 | 0.11 212 | 0.01 216 | 0.26 212 | 0.01 216 | 0.06 212 | 0.10 210 | 0.10 208 | 0.01 212 | 0.43 210 |
|
test123 | | | 0.09 207 | 0.14 209 | 0.02 209 | 0.00 214 | 0.02 214 | 0.02 216 | 0.01 211 | 0.09 213 | 0.00 217 | 0.30 211 | 0.00 217 | 0.08 210 | 0.03 211 | 0.09 210 | 0.01 212 | 0.45 209 |
|
sosnet-low-res | | | 0.00 209 | 0.00 210 | 0.00 211 | 0.00 214 | 0.00 216 | 0.00 217 | 0.00 213 | 0.00 214 | 0.00 217 | 0.00 213 | 0.00 217 | 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 | 0.00 214 | 0.00 216 | 0.00 217 | 0.00 213 | 0.00 214 | 0.00 217 | 0.00 213 | 0.00 217 | 0.00 213 | 0.00 212 | 0.00 211 | 0.00 215 | 0.00 212 |
|
MTAPA | | | | | | | | | | | 83.48 1 | | 86.45 11 | | | | | |
|
MTMP | | | | | | | | | | | 82.66 3 | | 84.91 19 | | | | | |
|
Patchmatch-RL test | | | | | | | | 2.85 214 | | | | | | | | | | |
|
mPP-MVS | | | | | | 89.90 18 | | | | | | | 81.29 31 | | | | | |
|
NP-MVS | | | | | | | | | | 80.10 38 | | | | | | | | |
|
Patchmtry | | | | | | | 65.80 173 | 65.97 158 | 52.74 159 | | 52.65 118 | | | | | | | |
|