APDe-MVS | | | 99.40 1 | 99.81 2 | 98.92 3 | 99.62 5 | 99.96 7 | 99.76 5 | 96.87 9 | 99.95 19 | 97.66 4 | 99.57 26 | 100.00 1 | 99.63 23 | 99.88 8 | 99.28 24 | 100.00 1 | 100.00 1 |
|
MSLP-MVS++ | | | 99.39 2 | 99.76 7 | 98.95 2 | 99.60 11 | 99.99 1 | 99.83 1 | 96.82 12 | 99.92 28 | 97.58 6 | 99.58 25 | 100.00 1 | 99.93 1 | 98.98 30 | 99.86 7 | 99.96 11 | 100.00 1 |
|
CNVR-MVS | | | 99.39 2 | 99.75 10 | 98.98 1 | 99.69 1 | 99.95 12 | 99.76 5 | 96.91 6 | 99.98 3 | 97.59 5 | 99.64 19 | 100.00 1 | 99.93 1 | 99.94 2 | 98.75 45 | 99.97 10 | 99.97 80 |
|
HSP-MVS | | | 99.36 4 | 99.79 4 | 98.85 6 | 99.61 9 | 99.96 7 | 99.71 18 | 96.94 4 | 99.97 6 | 97.11 8 | 99.60 22 | 100.00 1 | 99.70 15 | 99.96 1 | 99.12 29 | 100.00 1 | 99.96 99 |
|
APD-MVS | | | 99.33 5 | 99.85 1 | 98.73 9 | 99.61 9 | 99.92 34 | 99.77 4 | 96.91 6 | 99.93 23 | 96.31 15 | 99.59 23 | 99.95 32 | 99.84 7 | 99.73 15 | 99.84 8 | 99.95 13 | 100.00 1 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
ESAPD | | | 99.25 6 | 99.69 17 | 98.74 8 | 99.62 5 | 99.94 17 | 99.79 2 | 96.87 9 | 99.93 23 | 96.33 14 | 99.59 23 | 100.00 1 | 99.84 7 | 99.88 8 | 98.50 52 | 100.00 1 | 100.00 1 |
|
NCCC | | | 99.24 7 | 99.75 10 | 98.65 10 | 99.63 4 | 99.96 7 | 99.76 5 | 96.91 6 | 99.97 6 | 95.86 18 | 99.67 11 | 100.00 1 | 99.75 12 | 99.85 10 | 98.80 41 | 99.98 9 | 99.97 80 |
|
CNLPA | | | 99.24 7 | 99.58 28 | 98.85 6 | 99.34 27 | 99.95 12 | 99.32 30 | 96.65 21 | 99.96 15 | 98.44 2 | 98.97 50 | 100.00 1 | 99.57 26 | 98.66 38 | 99.56 14 | 99.76 72 | 99.97 80 |
|
AdaColmap | | | 99.21 9 | 99.45 34 | 98.92 3 | 99.67 3 | 99.95 12 | 99.65 22 | 96.77 16 | 99.97 6 | 97.67 3 | 100.00 1 | 99.69 45 | 99.93 1 | 99.26 26 | 97.25 84 | 99.85 27 | 100.00 1 |
|
HFP-MVS | | | 99.19 10 | 99.77 6 | 98.51 14 | 99.55 15 | 99.94 17 | 99.76 5 | 96.84 11 | 99.88 33 | 95.27 22 | 99.67 11 | 100.00 1 | 99.85 6 | 99.56 20 | 99.36 19 | 99.79 53 | 99.97 80 |
|
PLC | | 98.06 1 | 99.17 11 | 99.38 36 | 98.92 3 | 99.47 17 | 99.90 42 | 99.48 27 | 96.47 26 | 99.96 15 | 98.73 1 | 99.52 29 | 100.00 1 | 99.55 28 | 98.54 50 | 97.73 75 | 99.84 29 | 99.99 47 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
SD-MVS | | | 99.16 12 | 99.73 13 | 98.49 15 | 97.93 47 | 99.95 12 | 99.74 13 | 96.94 4 | 99.96 15 | 96.60 11 | 99.47 32 | 100.00 1 | 99.88 5 | 99.15 28 | 99.59 12 | 99.84 29 | 100.00 1 |
|
CP-MVS | | | 99.14 13 | 99.67 19 | 98.53 13 | 99.45 19 | 99.94 17 | 99.63 24 | 96.62 23 | 99.82 45 | 95.92 17 | 99.65 16 | 100.00 1 | 99.71 14 | 99.76 13 | 98.56 49 | 99.83 34 | 100.00 1 |
|
MPTG | | | 99.12 14 | 99.52 33 | 98.65 10 | 99.58 14 | 99.93 28 | 99.74 13 | 96.72 19 | 99.44 82 | 96.47 12 | 99.62 21 | 100.00 1 | 99.63 23 | 99.74 14 | 97.97 62 | 99.77 65 | 99.94 112 |
|
ACMMPR | | | 99.12 14 | 99.76 7 | 98.36 16 | 99.45 19 | 99.94 17 | 99.75 11 | 96.70 20 | 99.93 23 | 94.65 26 | 99.65 16 | 99.96 30 | 99.84 7 | 99.51 22 | 99.35 20 | 99.79 53 | 99.96 99 |
|
MCST-MVS | | | 99.08 16 | 99.72 15 | 98.33 17 | 99.59 13 | 99.97 3 | 99.78 3 | 96.96 2 | 99.95 19 | 93.72 30 | 99.67 11 | 100.00 1 | 99.90 4 | 99.91 5 | 98.55 50 | 100.00 1 | 100.00 1 |
|
CPTT-MVS | | | 99.08 16 | 99.53 32 | 98.57 12 | 99.44 21 | 99.93 28 | 99.60 25 | 95.92 31 | 99.77 52 | 97.01 9 | 99.67 11 | 100.00 1 | 99.72 13 | 99.56 20 | 97.76 72 | 99.70 109 | 99.98 67 |
|
DeepC-MVS_fast | | 98.03 2 | 99.05 18 | 99.78 5 | 98.21 20 | 99.47 17 | 99.97 3 | 99.75 11 | 96.80 13 | 99.97 6 | 93.58 33 | 98.68 61 | 99.94 33 | 99.69 16 | 99.93 4 | 99.95 2 | 99.96 11 | 99.98 67 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
TSAR-MVS + MP. | | | 98.99 19 | 99.61 25 | 98.27 18 | 97.88 48 | 99.92 34 | 99.71 18 | 96.80 13 | 99.96 15 | 95.58 20 | 98.71 60 | 100.00 1 | 99.68 18 | 99.91 5 | 98.78 43 | 99.99 6 | 100.00 1 |
|
HPM-MVS++ | | | 98.98 20 | 99.62 24 | 98.22 19 | 99.62 5 | 99.94 17 | 99.74 13 | 96.95 3 | 99.87 36 | 93.76 29 | 99.49 31 | 100.00 1 | 99.39 34 | 99.73 15 | 98.35 54 | 99.89 22 | 99.96 99 |
|
SteuartSystems-ACMMP | | | 98.95 21 | 99.80 3 | 97.95 23 | 99.43 22 | 99.96 7 | 99.76 5 | 96.45 27 | 99.82 45 | 93.63 31 | 99.64 19 | 100.00 1 | 98.56 71 | 99.90 7 | 99.31 22 | 99.84 29 | 100.00 1 |
Skip Steuart: Steuart Systems R&D Blog. |
PHI-MVS | | | 98.85 22 | 99.67 19 | 97.89 24 | 98.63 43 | 99.93 28 | 98.95 41 | 95.20 33 | 99.84 43 | 94.94 23 | 99.74 10 | 100.00 1 | 99.69 16 | 98.40 57 | 99.75 10 | 99.93 16 | 99.99 47 |
|
MP-MVS | | | 98.82 23 | 99.63 22 | 97.88 25 | 99.41 23 | 99.91 41 | 99.74 13 | 96.76 17 | 99.88 33 | 91.89 40 | 99.50 30 | 99.94 33 | 99.65 21 | 99.71 18 | 98.49 53 | 99.82 38 | 99.97 80 |
|
ACMMP_Plus | | | 98.68 24 | 99.58 28 | 97.62 26 | 99.62 5 | 99.92 34 | 99.72 17 | 96.78 15 | 99.71 60 | 90.13 67 | 99.66 15 | 99.99 25 | 99.64 22 | 99.78 12 | 98.14 59 | 99.82 38 | 99.89 135 |
|
train_agg | | | 98.62 25 | 99.76 7 | 97.28 28 | 99.03 36 | 99.93 28 | 99.65 22 | 96.37 28 | 99.98 3 | 89.24 77 | 99.53 27 | 99.83 38 | 99.59 25 | 99.85 10 | 99.19 27 | 99.80 49 | 100.00 1 |
|
X-MVS | | | 98.62 25 | 99.75 10 | 97.29 27 | 99.50 16 | 99.94 17 | 99.71 18 | 96.55 24 | 99.85 40 | 88.58 82 | 99.65 16 | 99.98 27 | 99.67 19 | 99.60 19 | 99.26 25 | 99.77 65 | 99.97 80 |
|
OMC-MVS | | | 98.59 27 | 99.07 38 | 98.03 22 | 99.41 23 | 99.90 42 | 99.26 33 | 94.33 35 | 99.94 21 | 96.03 16 | 96.68 86 | 99.72 44 | 99.42 31 | 98.86 33 | 98.84 38 | 99.72 105 | 99.58 173 |
|
PGM-MVS | | | 98.47 28 | 99.73 13 | 97.00 32 | 99.68 2 | 99.94 17 | 99.76 5 | 91.74 40 | 99.84 43 | 91.17 51 | 100.00 1 | 99.69 45 | 99.81 10 | 99.38 24 | 99.30 23 | 99.82 38 | 99.95 108 |
|
TSAR-MVS + ACMM | | | 98.30 29 | 99.64 21 | 96.74 35 | 99.08 35 | 99.94 17 | 99.67 21 | 96.73 18 | 99.97 6 | 86.30 95 | 98.30 66 | 99.99 25 | 98.78 65 | 99.73 15 | 99.57 13 | 99.88 25 | 99.98 67 |
|
CSCG | | | 98.22 30 | 98.37 59 | 98.04 21 | 99.60 11 | 99.82 55 | 99.45 28 | 93.59 36 | 99.16 97 | 96.46 13 | 98.22 72 | 95.86 90 | 99.41 33 | 96.33 122 | 99.22 26 | 99.75 85 | 99.94 112 |
|
3Dnovator+ | | 95.21 7 | 98.17 31 | 99.08 37 | 97.12 30 | 99.28 30 | 99.78 69 | 98.61 49 | 89.93 55 | 99.93 23 | 95.36 21 | 95.50 97 | 100.00 1 | 99.56 27 | 98.58 45 | 99.80 9 | 99.95 13 | 99.97 80 |
|
ACMMP | | | 98.16 32 | 99.01 39 | 97.18 29 | 98.86 38 | 99.92 34 | 98.77 46 | 95.73 32 | 99.31 93 | 91.15 52 | 100.00 1 | 99.81 40 | 98.82 64 | 98.11 71 | 95.91 121 | 99.77 65 | 99.97 80 |
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 |
MVS_111021_LR | | | 98.15 33 | 99.69 17 | 96.36 40 | 99.23 32 | 99.93 28 | 97.79 60 | 91.84 39 | 99.87 36 | 90.53 62 | 100.00 1 | 99.57 50 | 98.93 58 | 99.44 23 | 99.08 31 | 99.85 27 | 99.95 108 |
|
EPNet | | | 98.11 34 | 99.63 22 | 96.34 41 | 98.44 45 | 99.88 48 | 98.55 50 | 90.25 51 | 99.93 23 | 92.60 37 | 100.00 1 | 99.73 42 | 98.41 72 | 98.87 32 | 99.02 32 | 99.82 38 | 99.97 80 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
TSAR-MVS + GP. | | | 98.06 35 | 99.55 31 | 96.32 42 | 94.72 73 | 99.92 34 | 99.22 34 | 89.98 53 | 99.97 6 | 94.77 25 | 99.94 9 | 100.00 1 | 99.43 30 | 98.52 54 | 98.53 51 | 99.79 53 | 100.00 1 |
|
3Dnovator | | 95.01 8 | 97.98 36 | 98.89 42 | 96.92 34 | 99.36 25 | 99.76 71 | 98.72 47 | 89.98 53 | 99.98 3 | 93.99 28 | 94.60 111 | 99.43 55 | 99.50 29 | 98.55 47 | 99.91 4 | 99.99 6 | 99.98 67 |
|
MVS_111021_HR | | | 97.94 37 | 99.59 26 | 96.02 44 | 99.27 31 | 99.97 3 | 97.03 83 | 90.44 48 | 99.89 30 | 90.75 55 | 100.00 1 | 99.73 42 | 98.68 70 | 98.67 37 | 98.89 36 | 99.95 13 | 99.97 80 |
|
QAPM | | | 97.90 38 | 98.89 42 | 96.74 35 | 99.35 26 | 99.80 67 | 98.84 43 | 90.20 52 | 99.94 21 | 92.85 34 | 94.17 114 | 99.78 41 | 99.42 31 | 98.71 36 | 99.87 6 | 99.79 53 | 99.98 67 |
|
CDPH-MVS | | | 97.88 39 | 99.59 26 | 95.89 45 | 98.90 37 | 99.95 12 | 99.40 29 | 92.86 38 | 99.86 39 | 85.33 98 | 98.62 62 | 99.45 54 | 99.06 56 | 99.29 25 | 99.94 3 | 99.81 46 | 100.00 1 |
|
CANet | | | 97.62 40 | 98.94 41 | 96.08 43 | 97.19 52 | 99.93 28 | 99.29 32 | 90.38 49 | 99.87 36 | 91.00 53 | 95.79 96 | 99.51 51 | 98.72 69 | 98.53 51 | 99.00 33 | 99.90 21 | 99.99 47 |
|
TAPA-MVS | | 96.62 5 | 97.60 41 | 98.46 57 | 96.60 38 | 98.73 41 | 99.90 42 | 99.30 31 | 94.96 34 | 99.46 81 | 87.57 87 | 96.05 95 | 98.53 63 | 99.26 45 | 98.04 76 | 97.33 83 | 99.77 65 | 99.88 138 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
DeepPCF-MVS | | 97.16 4 | 97.58 42 | 99.72 15 | 95.07 56 | 98.45 44 | 99.96 7 | 93.83 133 | 95.93 30 | 100.00 1 | 90.79 54 | 98.38 65 | 99.85 37 | 95.28 123 | 99.94 2 | 99.97 1 | 96.15 219 | 99.97 80 |
|
PCF-MVS | | 97.20 3 | 97.49 43 | 98.20 65 | 96.66 37 | 97.62 50 | 99.92 34 | 98.93 42 | 96.64 22 | 98.53 124 | 88.31 85 | 94.04 116 | 99.58 49 | 98.94 57 | 97.53 90 | 97.79 70 | 99.54 134 | 99.97 80 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
MSDG | | | 97.29 44 | 97.55 80 | 97.00 32 | 98.66 42 | 99.71 73 | 99.03 39 | 96.15 29 | 99.59 68 | 89.67 74 | 92.77 128 | 94.86 94 | 98.75 66 | 98.22 66 | 97.94 63 | 99.72 105 | 99.76 158 |
|
CHOSEN 280x420 | | | 97.16 45 | 99.58 28 | 94.35 77 | 96.95 55 | 99.97 3 | 97.19 79 | 81.55 139 | 99.92 28 | 91.75 41 | 100.00 1 | 100.00 1 | 98.84 63 | 98.55 47 | 98.65 46 | 99.79 53 | 99.97 80 |
|
DELS-MVS | | | 97.05 46 | 98.05 69 | 95.88 47 | 97.09 53 | 99.99 1 | 98.82 44 | 90.30 50 | 98.44 129 | 91.40 46 | 92.91 125 | 96.57 83 | 97.68 93 | 98.56 46 | 99.88 5 | 100.00 1 | 100.00 1 |
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 |
DeepC-MVS | | 96.33 6 | 97.05 46 | 97.59 79 | 96.42 39 | 97.37 51 | 99.92 34 | 99.10 37 | 96.54 25 | 99.34 92 | 86.64 94 | 91.93 132 | 93.15 106 | 99.11 54 | 99.11 29 | 99.68 11 | 99.73 101 | 99.97 80 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MVS_0304 | | | 97.04 48 | 98.72 50 | 95.08 55 | 96.32 59 | 99.90 42 | 99.15 35 | 89.61 59 | 99.89 30 | 87.22 92 | 95.47 98 | 98.22 73 | 98.22 77 | 98.63 42 | 98.90 35 | 99.93 16 | 100.00 1 |
|
MAR-MVS | | | 97.03 49 | 98.00 71 | 95.89 45 | 99.32 28 | 99.74 72 | 96.76 89 | 84.89 101 | 99.97 6 | 94.86 24 | 98.29 67 | 90.58 113 | 99.67 19 | 98.02 78 | 99.50 15 | 99.82 38 | 99.92 119 |
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 |
MVSTER | | | 97.00 50 | 98.85 45 | 94.83 65 | 92.71 98 | 97.43 145 | 99.03 39 | 85.52 94 | 99.82 45 | 92.74 36 | 99.15 41 | 99.94 33 | 99.19 48 | 98.66 38 | 96.99 98 | 99.79 53 | 99.98 67 |
|
OpenMVS | | 94.03 11 | 96.87 51 | 98.10 68 | 95.44 51 | 99.29 29 | 99.78 69 | 98.46 55 | 89.92 56 | 99.47 80 | 85.78 96 | 91.05 135 | 98.50 64 | 99.30 38 | 98.49 55 | 99.41 16 | 99.89 22 | 99.98 67 |
|
tfpn_ndepth | | | 96.84 52 | 98.58 53 | 94.81 66 | 93.18 81 | 99.62 80 | 96.83 87 | 88.75 75 | 99.73 58 | 92.38 38 | 98.45 64 | 96.34 87 | 97.90 86 | 98.34 62 | 97.59 78 | 99.84 29 | 99.99 47 |
|
PatchMatch-RL | | | 96.84 52 | 98.03 70 | 95.47 48 | 98.84 39 | 99.81 63 | 95.61 108 | 89.20 63 | 99.65 63 | 91.28 49 | 99.39 33 | 93.46 104 | 98.18 78 | 98.05 74 | 96.28 108 | 99.69 115 | 99.55 178 |
|
IS_MVSNet | | | 96.66 54 | 98.62 52 | 94.38 74 | 92.41 108 | 99.70 74 | 97.19 79 | 87.67 88 | 99.05 104 | 91.27 50 | 95.09 103 | 98.46 68 | 97.95 85 | 98.64 40 | 99.37 17 | 99.79 53 | 100.00 1 |
|
tfpn1000 | | | 96.58 55 | 98.37 59 | 94.50 73 | 93.04 89 | 99.59 81 | 96.53 92 | 88.54 79 | 99.73 58 | 91.59 42 | 98.28 68 | 95.76 91 | 97.46 95 | 98.19 67 | 97.10 93 | 99.82 38 | 99.96 99 |
|
conf0.002 | | | 96.51 56 | 97.75 76 | 95.07 56 | 93.11 82 | 99.83 51 | 97.67 62 | 89.10 65 | 98.62 116 | 91.47 45 | 99.39 33 | 91.68 109 | 99.28 40 | 97.49 92 | 97.24 85 | 99.76 72 | 100.00 1 |
|
thresconf0.02 | | | 96.46 57 | 98.87 44 | 93.64 82 | 92.77 97 | 99.11 101 | 97.05 82 | 89.36 60 | 99.64 65 | 85.14 99 | 99.07 43 | 96.84 81 | 97.72 90 | 98.72 35 | 98.76 44 | 99.78 60 | 99.95 108 |
|
PMMVS | | | 96.45 58 | 98.24 62 | 94.36 76 | 92.58 100 | 99.01 108 | 97.08 81 | 87.42 89 | 99.88 33 | 90.06 68 | 99.39 33 | 94.63 95 | 99.33 37 | 97.85 83 | 96.99 98 | 99.70 109 | 99.96 99 |
|
LS3D | | | 96.44 59 | 97.31 85 | 95.41 52 | 97.06 54 | 99.87 49 | 99.51 26 | 97.48 1 | 99.57 69 | 79.00 121 | 95.39 99 | 89.19 119 | 99.81 10 | 98.55 47 | 98.84 38 | 99.62 123 | 99.78 156 |
|
diffmvs | | | 96.35 60 | 98.76 49 | 93.54 84 | 92.41 108 | 99.55 83 | 97.22 78 | 83.75 114 | 99.57 69 | 89.64 75 | 96.86 82 | 98.33 69 | 98.37 73 | 98.42 56 | 98.61 47 | 99.88 25 | 99.99 47 |
|
EPP-MVSNet | | | 96.29 61 | 98.34 61 | 93.90 79 | 91.77 119 | 99.38 93 | 95.45 113 | 87.25 91 | 99.38 88 | 91.36 48 | 94.86 110 | 98.49 66 | 97.83 88 | 98.01 79 | 98.23 56 | 99.75 85 | 99.99 47 |
|
DWT-MVSNet_training | | | 96.26 62 | 98.44 58 | 93.72 81 | 92.58 100 | 99.34 95 | 96.15 97 | 83.00 122 | 99.76 54 | 93.63 31 | 97.89 76 | 99.46 52 | 97.23 99 | 94.43 153 | 98.19 57 | 99.70 109 | 100.00 1 |
|
conf0.01 | | | 96.20 63 | 97.19 89 | 95.05 58 | 93.11 82 | 99.83 51 | 97.67 62 | 89.06 66 | 98.62 116 | 91.38 47 | 99.19 40 | 89.09 120 | 99.28 40 | 97.48 93 | 96.10 112 | 99.76 72 | 100.00 1 |
|
UGNet | | | 96.05 64 | 98.55 54 | 93.13 88 | 94.64 74 | 99.65 77 | 94.70 122 | 87.78 86 | 99.40 87 | 89.69 73 | 98.25 69 | 99.25 58 | 92.12 155 | 96.50 114 | 97.08 94 | 99.84 29 | 99.72 162 |
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 |
COLMAP_ROB | | 93.56 12 | 96.03 65 | 96.83 99 | 95.11 54 | 97.87 49 | 99.52 84 | 98.81 45 | 91.40 43 | 99.42 84 | 84.97 100 | 90.46 137 | 96.82 82 | 98.05 80 | 96.46 118 | 96.19 111 | 99.54 134 | 98.92 195 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
PVSNet_BlendedMVS | | | 96.01 66 | 96.48 109 | 95.46 49 | 96.47 57 | 99.89 46 | 95.64 105 | 91.23 44 | 99.75 56 | 91.59 42 | 96.80 83 | 82.44 144 | 98.05 80 | 98.53 51 | 97.92 67 | 99.80 49 | 100.00 1 |
|
PVSNet_Blended | | | 96.01 66 | 96.48 109 | 95.46 49 | 96.47 57 | 99.89 46 | 95.64 105 | 91.23 44 | 99.75 56 | 91.59 42 | 96.80 83 | 82.44 144 | 98.05 80 | 98.53 51 | 97.92 67 | 99.80 49 | 100.00 1 |
|
tfpn | | | 95.93 68 | 97.06 92 | 94.62 70 | 92.94 96 | 99.81 63 | 97.25 77 | 88.71 78 | 98.32 136 | 89.98 69 | 98.79 59 | 88.55 122 | 99.11 54 | 97.26 107 | 96.71 100 | 99.75 85 | 99.98 67 |
|
thres100view900 | | | 95.86 69 | 96.62 101 | 94.97 59 | 93.10 84 | 99.83 51 | 97.76 61 | 89.15 64 | 98.62 116 | 90.69 56 | 99.00 46 | 84.86 131 | 99.30 38 | 97.57 89 | 96.48 102 | 99.81 46 | 100.00 1 |
|
RPSCF | | | 95.86 69 | 96.94 98 | 94.61 71 | 96.52 56 | 98.67 122 | 98.54 51 | 88.43 82 | 99.56 71 | 90.51 65 | 99.39 33 | 98.70 61 | 97.72 90 | 93.77 166 | 92.00 174 | 95.93 220 | 96.50 215 |
|
canonicalmvs | | | 95.80 71 | 97.02 93 | 94.37 75 | 92.96 92 | 99.47 88 | 97.49 70 | 84.58 103 | 99.44 82 | 92.05 39 | 98.54 63 | 86.65 126 | 99.37 35 | 96.18 125 | 98.93 34 | 99.77 65 | 99.92 119 |
|
tfpn111 | | | 95.79 72 | 96.55 103 | 94.89 60 | 93.10 84 | 99.82 55 | 97.67 62 | 88.85 69 | 98.62 116 | 90.69 56 | 99.07 43 | 84.86 131 | 99.28 40 | 97.41 97 | 96.10 112 | 99.76 72 | 99.99 47 |
|
tfpnview11 | | | 95.78 73 | 98.17 67 | 93.01 92 | 92.58 100 | 99.04 107 | 96.64 90 | 88.72 77 | 99.63 67 | 83.08 108 | 98.90 51 | 94.24 99 | 97.25 98 | 98.35 61 | 97.21 86 | 99.77 65 | 99.80 155 |
|
conf200view11 | | | 95.78 73 | 96.54 105 | 94.89 60 | 93.10 84 | 99.82 55 | 97.67 62 | 88.85 69 | 98.62 116 | 90.69 56 | 99.00 46 | 84.86 131 | 99.28 40 | 97.41 97 | 96.10 112 | 99.76 72 | 99.99 47 |
|
tfpn200view9 | | | 95.78 73 | 96.54 105 | 94.89 60 | 93.10 84 | 99.82 55 | 97.67 62 | 88.85 69 | 98.62 116 | 90.69 56 | 99.00 46 | 84.86 131 | 99.28 40 | 97.41 97 | 96.10 112 | 99.76 72 | 99.99 47 |
|
thres200 | | | 95.77 76 | 96.55 103 | 94.86 63 | 93.09 88 | 99.82 55 | 97.63 68 | 88.85 69 | 98.49 125 | 90.66 60 | 98.99 49 | 84.86 131 | 99.20 46 | 97.41 97 | 96.28 108 | 99.76 72 | 100.00 1 |
|
tfpn_n400 | | | 95.76 77 | 98.21 63 | 92.90 94 | 92.57 104 | 99.05 105 | 96.42 93 | 88.50 80 | 99.49 75 | 83.08 108 | 98.90 51 | 94.24 99 | 97.07 100 | 98.10 72 | 97.93 65 | 99.74 89 | 99.76 158 |
|
tfpnconf | | | 95.76 77 | 98.21 63 | 92.90 94 | 92.57 104 | 99.05 105 | 96.42 93 | 88.50 80 | 99.49 75 | 83.08 108 | 98.90 51 | 94.24 99 | 97.07 100 | 98.10 72 | 97.93 65 | 99.74 89 | 99.76 158 |
|
MVS_Test | | | 95.74 79 | 98.18 66 | 92.90 94 | 92.16 112 | 99.49 87 | 97.36 76 | 84.30 108 | 99.79 49 | 84.94 101 | 96.65 87 | 93.63 103 | 98.85 62 | 98.61 44 | 99.10 30 | 99.81 46 | 100.00 1 |
|
thres400 | | | 95.72 80 | 96.48 109 | 94.84 64 | 93.00 91 | 99.83 51 | 97.55 69 | 88.93 67 | 98.49 125 | 90.61 61 | 98.86 54 | 84.63 136 | 99.20 46 | 97.45 94 | 96.10 112 | 99.77 65 | 99.99 47 |
|
view600 | | | 95.64 81 | 96.38 112 | 94.79 67 | 92.96 92 | 99.82 55 | 97.48 73 | 88.85 69 | 98.38 130 | 90.52 63 | 98.84 56 | 84.61 137 | 99.15 50 | 97.41 97 | 95.60 129 | 99.76 72 | 99.99 47 |
|
thres600view7 | | | 95.64 81 | 96.38 112 | 94.79 67 | 92.96 92 | 99.82 55 | 97.48 73 | 88.85 69 | 98.38 130 | 90.52 63 | 98.84 56 | 84.61 137 | 99.15 50 | 97.41 97 | 95.60 129 | 99.76 72 | 99.99 47 |
|
view800 | | | 95.62 83 | 96.38 112 | 94.73 69 | 92.96 92 | 99.81 63 | 97.38 75 | 88.75 75 | 98.35 135 | 90.43 66 | 98.81 58 | 84.54 139 | 99.13 53 | 97.35 103 | 95.82 124 | 99.76 72 | 99.98 67 |
|
Vis-MVSNet (Re-imp) | | | 95.60 84 | 98.52 56 | 92.19 100 | 92.37 110 | 99.56 82 | 96.37 95 | 87.41 90 | 98.95 107 | 84.77 103 | 94.88 109 | 98.48 67 | 92.44 152 | 98.63 42 | 99.37 17 | 99.76 72 | 99.77 157 |
|
FMVSNet3 | | | 95.59 85 | 97.51 81 | 93.34 86 | 89.48 139 | 96.57 156 | 97.67 62 | 84.17 109 | 99.48 77 | 89.76 70 | 95.09 103 | 94.35 96 | 99.14 52 | 98.37 59 | 98.86 37 | 99.82 38 | 99.89 135 |
|
PVSNet_Blended_VisFu | | | 95.37 86 | 97.44 83 | 92.95 93 | 95.20 66 | 99.80 67 | 92.68 140 | 88.41 83 | 99.12 99 | 87.64 86 | 88.31 145 | 99.10 59 | 94.07 138 | 98.27 64 | 97.51 80 | 99.73 101 | 100.00 1 |
|
DI_MVS_plusplus_trai | | | 95.29 87 | 97.02 93 | 93.28 87 | 91.76 120 | 99.52 84 | 97.84 59 | 85.67 93 | 99.08 103 | 87.29 90 | 87.76 148 | 97.46 79 | 97.31 97 | 97.83 84 | 97.48 81 | 99.83 34 | 100.00 1 |
|
TSAR-MVS + COLMAP | | | 95.20 88 | 95.03 130 | 95.41 52 | 96.17 60 | 98.69 121 | 99.11 36 | 93.40 37 | 99.97 6 | 84.89 102 | 98.23 71 | 75.01 163 | 99.34 36 | 97.27 106 | 96.37 107 | 99.58 128 | 99.64 168 |
|
GBi-Net | | | 95.19 89 | 96.99 96 | 93.09 90 | 89.11 140 | 96.47 158 | 96.90 84 | 84.17 109 | 99.48 77 | 89.76 70 | 95.09 103 | 94.35 96 | 98.87 59 | 96.50 114 | 97.21 86 | 99.74 89 | 99.81 151 |
|
test1 | | | 95.19 89 | 96.99 96 | 93.09 90 | 89.11 140 | 96.47 158 | 96.90 84 | 84.17 109 | 99.48 77 | 89.76 70 | 95.09 103 | 94.35 96 | 98.87 59 | 96.50 114 | 97.21 86 | 99.74 89 | 99.81 151 |
|
test0.0.03 1 | | | 95.15 91 | 97.87 75 | 91.99 101 | 91.69 122 | 98.82 118 | 93.04 138 | 83.60 115 | 99.65 63 | 88.80 80 | 94.15 115 | 97.67 77 | 94.97 125 | 96.62 113 | 98.16 58 | 99.83 34 | 100.00 1 |
|
EPNet_dtu | | | 95.10 92 | 98.81 47 | 90.78 106 | 98.38 46 | 98.47 124 | 96.54 91 | 89.36 60 | 99.78 51 | 65.65 188 | 99.31 37 | 98.24 72 | 94.79 128 | 98.28 63 | 99.35 20 | 99.93 16 | 98.27 200 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
UA-Net | | | 94.95 93 | 98.66 51 | 90.63 108 | 94.60 76 | 98.94 114 | 96.03 99 | 85.28 96 | 98.01 142 | 78.92 122 | 97.42 80 | 99.96 30 | 89.09 196 | 98.95 31 | 98.80 41 | 99.82 38 | 98.57 197 |
|
CANet_DTU | | | 94.90 94 | 98.98 40 | 90.13 114 | 94.74 72 | 99.81 63 | 98.53 52 | 82.23 130 | 99.97 6 | 66.76 176 | 100.00 1 | 98.50 64 | 98.74 67 | 97.52 91 | 97.19 92 | 99.76 72 | 99.88 138 |
|
FC-MVSNet-train | | | 94.61 95 | 96.27 116 | 92.68 98 | 92.35 111 | 97.14 148 | 93.45 137 | 87.73 87 | 98.93 108 | 87.31 89 | 96.42 89 | 89.35 117 | 95.67 118 | 96.06 131 | 96.01 119 | 99.56 131 | 99.98 67 |
|
CLD-MVS | | | 94.53 96 | 94.45 137 | 94.61 71 | 93.85 79 | 98.36 126 | 98.12 57 | 89.68 57 | 99.35 91 | 89.62 76 | 95.19 101 | 77.08 153 | 96.66 110 | 95.51 136 | 95.67 126 | 99.74 89 | 100.00 1 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
conf0.05thres1000 | | | 94.50 97 | 95.70 124 | 93.11 89 | 92.68 99 | 99.67 76 | 96.04 98 | 87.81 85 | 97.52 147 | 83.71 104 | 96.20 93 | 84.52 140 | 98.73 68 | 96.39 120 | 95.66 127 | 99.71 107 | 99.92 119 |
|
FMVSNet2 | | | 94.48 98 | 95.95 121 | 92.77 97 | 89.11 140 | 96.47 158 | 96.90 84 | 83.38 117 | 99.11 100 | 88.64 81 | 87.50 153 | 92.26 108 | 98.87 59 | 97.91 81 | 98.60 48 | 99.74 89 | 99.81 151 |
|
HQP-MVS | | | 94.48 98 | 95.39 128 | 93.42 85 | 95.10 67 | 98.35 127 | 98.19 56 | 91.41 42 | 99.77 52 | 79.79 118 | 99.30 38 | 77.08 153 | 96.25 113 | 96.93 108 | 96.28 108 | 99.76 72 | 99.99 47 |
|
MDTV_nov1_ep13 | | | 94.32 100 | 98.77 48 | 89.14 123 | 91.70 121 | 99.52 84 | 95.21 115 | 72.09 200 | 99.80 48 | 78.91 123 | 96.32 90 | 99.62 47 | 97.71 92 | 98.39 58 | 97.71 76 | 99.22 202 | 100.00 1 |
|
CDS-MVSNet | | | 94.32 100 | 97.00 95 | 91.19 105 | 89.82 137 | 98.71 120 | 95.51 110 | 85.14 100 | 96.85 151 | 82.33 113 | 92.48 129 | 96.40 86 | 94.71 129 | 96.86 110 | 97.76 72 | 99.63 121 | 99.92 119 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
dps | | | 94.29 102 | 97.33 84 | 90.75 107 | 92.02 115 | 99.21 98 | 94.31 127 | 66.97 208 | 99.50 74 | 95.61 19 | 96.22 92 | 98.64 62 | 96.08 114 | 93.71 168 | 94.03 150 | 99.52 138 | 99.98 67 |
|
ACMM | | 94.44 10 | 94.26 103 | 94.62 134 | 93.84 80 | 94.86 71 | 97.73 140 | 93.48 136 | 90.76 47 | 99.27 94 | 87.46 88 | 99.04 45 | 76.60 156 | 96.76 108 | 96.37 121 | 93.76 153 | 99.74 89 | 99.55 178 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
ACMP | | 94.49 9 | 94.19 104 | 94.74 133 | 93.56 83 | 94.25 77 | 98.32 129 | 96.02 100 | 89.35 62 | 98.90 111 | 87.28 91 | 99.14 42 | 76.41 159 | 94.94 126 | 96.07 130 | 94.35 147 | 99.49 145 | 99.99 47 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
EPMVS | | | 94.08 105 | 98.54 55 | 88.87 125 | 92.51 106 | 99.47 88 | 94.18 129 | 66.53 209 | 99.68 62 | 82.40 112 | 95.24 100 | 99.40 56 | 97.86 87 | 98.12 70 | 97.99 61 | 99.75 85 | 99.88 138 |
|
test-LLR | | | 93.71 106 | 97.23 87 | 89.60 118 | 91.69 122 | 99.10 102 | 94.68 124 | 83.60 115 | 99.36 89 | 71.94 146 | 93.82 118 | 96.51 84 | 95.96 116 | 97.42 95 | 94.37 144 | 99.74 89 | 99.99 47 |
|
CHOSEN 1792x2688 | | | 93.69 107 | 94.89 132 | 92.28 99 | 96.17 60 | 99.84 50 | 95.69 104 | 83.17 120 | 98.54 123 | 82.04 114 | 77.58 203 | 91.15 111 | 96.90 103 | 98.36 60 | 98.82 40 | 99.73 101 | 99.98 67 |
|
LGP-MVS_train | | | 93.60 108 | 95.05 129 | 91.90 102 | 94.90 70 | 98.29 130 | 97.93 58 | 88.06 84 | 99.14 98 | 74.83 136 | 99.26 39 | 76.50 157 | 96.07 115 | 96.31 123 | 95.90 123 | 99.59 126 | 99.97 80 |
|
FMVSNet5 | | | 93.53 109 | 96.09 120 | 90.56 110 | 86.74 153 | 92.84 205 | 92.64 141 | 77.50 166 | 99.41 86 | 88.97 79 | 98.02 74 | 97.81 75 | 98.00 83 | 94.85 147 | 95.43 131 | 99.50 144 | 94.25 220 |
|
OPM-MVS | | | 93.50 110 | 93.00 147 | 94.07 78 | 95.82 63 | 98.26 131 | 98.49 54 | 91.62 41 | 94.69 173 | 81.93 115 | 92.82 127 | 76.18 161 | 96.82 105 | 96.12 127 | 94.57 138 | 99.74 89 | 98.39 198 |
|
CostFormer | | | 93.50 110 | 96.50 108 | 90.00 115 | 91.69 122 | 98.65 123 | 93.88 132 | 67.64 205 | 98.97 105 | 89.16 78 | 97.79 77 | 88.92 121 | 97.97 84 | 95.14 144 | 96.06 117 | 99.63 121 | 100.00 1 |
|
IterMVS-LS | | | 93.50 110 | 96.22 117 | 90.33 113 | 90.93 127 | 95.50 187 | 94.83 120 | 80.54 143 | 98.92 109 | 79.11 120 | 90.64 136 | 93.70 102 | 96.79 106 | 96.93 108 | 97.85 69 | 99.78 60 | 99.99 47 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
PatchmatchNet | | | 93.48 113 | 98.84 46 | 87.22 142 | 91.93 116 | 99.39 92 | 92.55 142 | 66.06 213 | 99.71 60 | 75.61 133 | 98.24 70 | 99.59 48 | 97.35 96 | 97.87 82 | 97.64 77 | 99.83 34 | 99.43 183 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
MS-PatchMatch | | | 93.46 114 | 95.91 123 | 90.61 109 | 95.48 64 | 99.31 96 | 95.62 107 | 77.23 168 | 99.42 84 | 81.88 116 | 88.92 142 | 96.06 89 | 93.80 140 | 96.45 119 | 93.11 162 | 99.65 119 | 98.10 204 |
|
tpm cat1 | | | 93.29 115 | 96.53 107 | 89.50 120 | 91.84 117 | 99.18 100 | 94.70 122 | 67.70 204 | 98.38 130 | 86.67 93 | 89.16 140 | 99.38 57 | 96.66 110 | 94.33 154 | 95.30 132 | 99.43 164 | 100.00 1 |
|
Effi-MVS+-dtu | | | 93.13 116 | 97.13 90 | 88.47 133 | 88.86 146 | 99.19 99 | 96.79 88 | 79.08 156 | 99.64 65 | 70.01 156 | 97.51 79 | 89.38 116 | 96.53 112 | 97.60 87 | 96.55 101 | 99.57 129 | 100.00 1 |
|
HyFIR lowres test | | | 93.13 116 | 94.48 136 | 91.56 103 | 96.12 62 | 99.68 75 | 93.52 135 | 79.98 147 | 97.24 148 | 81.73 117 | 72.66 214 | 95.74 92 | 98.29 76 | 98.27 64 | 97.79 70 | 99.70 109 | 100.00 1 |
|
Vis-MVSNet | | | 93.08 118 | 96.76 100 | 88.78 129 | 91.14 126 | 99.63 79 | 94.85 119 | 83.34 118 | 97.19 149 | 74.78 137 | 91.92 133 | 93.15 106 | 88.81 199 | 97.59 88 | 98.35 54 | 99.78 60 | 99.49 182 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
Effi-MVS+ | | | 93.06 119 | 95.94 122 | 89.70 117 | 90.82 128 | 99.45 90 | 95.71 103 | 78.94 158 | 98.72 112 | 74.71 138 | 97.92 75 | 80.73 148 | 98.35 74 | 97.72 85 | 97.05 97 | 99.70 109 | 100.00 1 |
|
tpmp4_e23 | | | 92.95 120 | 96.28 115 | 89.06 124 | 91.80 118 | 98.81 119 | 94.95 118 | 67.56 207 | 99.21 95 | 82.97 111 | 96.54 88 | 88.52 123 | 97.47 94 | 94.47 152 | 96.42 105 | 99.61 124 | 100.00 1 |
|
ADS-MVSNet | | | 92.91 121 | 97.97 72 | 87.01 144 | 92.07 114 | 99.27 97 | 92.70 139 | 65.39 218 | 99.85 40 | 75.40 134 | 94.93 108 | 98.26 70 | 96.86 104 | 96.09 128 | 97.52 79 | 99.65 119 | 99.84 147 |
|
TESTMET0.1,1 | | | 92.87 122 | 97.23 87 | 87.79 139 | 86.96 152 | 99.10 102 | 94.68 124 | 77.46 167 | 99.36 89 | 71.94 146 | 93.82 118 | 96.51 84 | 95.96 116 | 97.42 95 | 94.37 144 | 99.74 89 | 99.99 47 |
|
FC-MVSNet-test | | | 92.78 123 | 96.19 119 | 88.80 128 | 88.00 149 | 97.54 142 | 93.60 134 | 82.36 129 | 98.16 137 | 79.71 119 | 91.55 134 | 95.41 93 | 89.65 191 | 96.09 128 | 95.23 133 | 99.49 145 | 99.31 186 |
|
Fast-Effi-MVS+-dtu | | | 92.73 124 | 97.62 78 | 87.02 143 | 88.91 144 | 98.83 117 | 95.79 101 | 73.98 187 | 99.89 30 | 68.62 161 | 97.73 78 | 93.30 105 | 95.21 124 | 97.67 86 | 95.96 120 | 99.59 126 | 100.00 1 |
|
IB-MVS | | 90.59 15 | 92.70 125 | 95.70 124 | 89.21 122 | 94.62 75 | 99.45 90 | 83.77 208 | 88.92 68 | 99.53 72 | 92.82 35 | 98.86 54 | 86.08 128 | 75.24 220 | 92.81 184 | 93.17 160 | 99.89 22 | 100.00 1 |
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 |
test-mter | | | 92.67 126 | 97.13 90 | 87.47 141 | 86.72 154 | 99.07 104 | 94.28 128 | 76.90 171 | 99.21 95 | 71.53 150 | 93.63 120 | 96.32 88 | 95.67 118 | 97.32 104 | 94.36 146 | 99.74 89 | 99.99 47 |
|
RPMNet | | | 92.64 127 | 97.88 74 | 86.53 149 | 90.79 129 | 98.95 112 | 95.13 116 | 64.44 222 | 99.09 101 | 72.36 142 | 93.58 121 | 99.01 60 | 96.74 109 | 98.05 74 | 96.45 104 | 99.71 107 | 100.00 1 |
|
FMVSNet1 | | | 92.55 128 | 93.66 141 | 91.26 104 | 87.91 150 | 96.12 165 | 94.75 121 | 81.69 138 | 97.67 144 | 85.63 97 | 80.56 178 | 87.88 125 | 98.15 79 | 96.50 114 | 97.21 86 | 99.41 182 | 99.71 163 |
|
tpmrst | | | 92.52 129 | 97.45 82 | 86.77 147 | 92.15 113 | 99.36 94 | 92.53 143 | 65.95 214 | 99.53 72 | 72.50 141 | 92.22 130 | 99.83 38 | 97.81 89 | 95.18 143 | 96.05 118 | 99.69 115 | 100.00 1 |
|
testgi | | | 92.47 130 | 95.68 126 | 88.73 130 | 90.68 130 | 98.35 127 | 91.67 150 | 79.50 152 | 98.96 106 | 77.12 129 | 95.17 102 | 85.84 129 | 93.95 139 | 95.75 134 | 96.47 103 | 99.45 158 | 99.21 189 |
|
TAMVS | | | 92.43 131 | 94.21 139 | 90.35 112 | 88.68 147 | 98.85 116 | 94.15 130 | 81.53 140 | 95.58 160 | 83.61 106 | 87.05 154 | 86.45 127 | 94.71 129 | 96.27 124 | 95.91 121 | 99.42 170 | 99.38 185 |
|
CR-MVSNet | | | 92.32 132 | 97.97 72 | 85.74 161 | 90.63 132 | 98.95 112 | 95.46 111 | 65.50 216 | 99.09 101 | 67.51 167 | 94.20 113 | 98.18 74 | 95.59 121 | 98.16 68 | 97.20 90 | 99.74 89 | 100.00 1 |
|
CVMVSNet | | | 92.13 133 | 95.40 127 | 88.32 136 | 91.29 125 | 97.29 147 | 91.85 147 | 86.42 92 | 96.71 153 | 71.84 148 | 89.56 139 | 91.18 110 | 88.98 198 | 96.17 126 | 97.76 72 | 99.51 142 | 99.14 191 |
|
Fast-Effi-MVS+ | | | 92.11 134 | 94.33 138 | 89.52 119 | 89.06 143 | 99.00 109 | 95.13 116 | 76.72 173 | 98.59 122 | 78.21 127 | 89.99 138 | 77.35 152 | 98.34 75 | 97.97 80 | 97.44 82 | 99.67 117 | 99.96 99 |
|
ACMH+ | | 92.61 13 | 91.80 135 | 93.03 145 | 90.37 111 | 93.03 90 | 98.17 132 | 94.00 131 | 84.13 112 | 98.12 139 | 77.39 128 | 91.95 131 | 74.62 164 | 94.36 135 | 94.62 151 | 93.82 152 | 99.32 192 | 99.87 142 |
|
IterMVS | | | 91.65 136 | 96.62 101 | 85.85 158 | 90.27 135 | 95.80 177 | 95.32 114 | 74.15 183 | 98.91 110 | 60.95 205 | 88.79 144 | 97.76 76 | 94.69 131 | 98.04 76 | 97.07 95 | 99.73 101 | 100.00 1 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
ACMH | | 92.34 14 | 91.59 137 | 93.02 146 | 89.92 116 | 93.97 78 | 97.98 136 | 90.10 178 | 84.70 102 | 98.46 127 | 76.80 130 | 93.38 123 | 71.94 178 | 94.39 133 | 95.34 140 | 94.04 149 | 99.54 134 | 100.00 1 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
pmmvs4 | | | 91.41 138 | 93.05 144 | 89.49 121 | 85.85 160 | 96.52 157 | 91.70 149 | 82.49 124 | 98.14 138 | 83.17 107 | 87.57 150 | 81.76 147 | 94.39 133 | 95.47 137 | 92.62 168 | 99.33 191 | 99.29 187 |
|
testpf | | | 91.26 139 | 97.28 86 | 84.23 184 | 89.52 138 | 97.45 144 | 88.08 197 | 56.08 231 | 99.76 54 | 78.71 124 | 95.06 107 | 98.26 70 | 93.44 144 | 94.72 149 | 95.69 125 | 99.57 129 | 99.99 47 |
|
PatchT | | | 91.06 140 | 97.66 77 | 83.36 197 | 90.32 134 | 98.96 111 | 82.30 213 | 64.72 221 | 98.45 128 | 67.51 167 | 93.28 124 | 97.60 78 | 95.59 121 | 98.16 68 | 97.20 90 | 99.70 109 | 100.00 1 |
|
MIMVSNet | | | 91.01 141 | 96.22 117 | 84.93 172 | 85.24 170 | 98.09 133 | 90.40 165 | 64.96 220 | 97.55 146 | 72.65 139 | 96.23 91 | 90.81 112 | 96.79 106 | 96.69 111 | 97.06 96 | 99.52 138 | 97.09 211 |
|
UniMVSNet_NR-MVSNet | | | 90.50 142 | 92.31 149 | 88.38 134 | 85.04 175 | 96.34 161 | 90.94 152 | 85.32 95 | 95.87 159 | 75.69 131 | 87.68 149 | 78.49 149 | 93.78 141 | 93.21 177 | 94.60 137 | 99.53 137 | 99.97 80 |
|
UniMVSNet (Re) | | | 90.41 143 | 91.96 151 | 88.59 132 | 85.71 161 | 96.73 153 | 90.82 155 | 84.11 113 | 95.23 166 | 78.54 125 | 88.91 143 | 76.41 159 | 92.84 149 | 93.40 175 | 93.05 163 | 99.55 133 | 100.00 1 |
|
GA-MVS | | | 90.38 144 | 94.59 135 | 85.46 166 | 88.30 148 | 98.44 125 | 92.18 144 | 83.30 119 | 97.89 143 | 58.05 212 | 92.86 126 | 84.25 142 | 91.27 180 | 96.65 112 | 92.61 169 | 99.66 118 | 99.43 183 |
|
USDC | | | 90.36 145 | 91.68 153 | 88.82 127 | 92.58 100 | 98.02 134 | 96.27 96 | 79.83 148 | 98.37 133 | 70.61 155 | 89.05 141 | 67.50 210 | 94.17 136 | 95.77 133 | 94.43 142 | 99.46 155 | 98.62 196 |
|
TinyColmap | | | 89.94 146 | 90.88 159 | 88.84 126 | 92.43 107 | 97.91 138 | 95.59 109 | 80.10 146 | 98.12 139 | 71.33 152 | 84.56 155 | 67.46 211 | 94.15 137 | 95.57 135 | 94.27 148 | 99.43 164 | 98.26 201 |
|
pm-mvs1 | | | 89.68 147 | 92.00 150 | 86.96 145 | 86.23 158 | 96.62 155 | 90.36 167 | 83.05 121 | 93.97 185 | 72.15 145 | 81.77 173 | 82.10 146 | 90.69 186 | 95.38 139 | 94.50 140 | 99.29 196 | 99.65 165 |
|
tpm | | | 89.60 148 | 94.93 131 | 83.39 195 | 89.94 136 | 97.11 149 | 90.09 179 | 65.28 219 | 98.67 114 | 60.03 209 | 96.79 85 | 84.38 141 | 95.66 120 | 91.90 189 | 95.65 128 | 99.32 192 | 99.98 67 |
|
NR-MVSNet | | | 89.52 149 | 90.71 160 | 88.14 138 | 86.19 159 | 96.20 162 | 92.07 145 | 84.58 103 | 95.54 161 | 75.27 135 | 87.52 151 | 67.96 209 | 91.24 182 | 94.33 154 | 93.45 157 | 99.49 145 | 99.97 80 |
|
DU-MVS | | | 89.49 150 | 90.60 161 | 88.19 137 | 84.71 189 | 96.20 162 | 90.94 152 | 84.58 103 | 95.54 161 | 75.69 131 | 87.52 151 | 68.74 208 | 93.78 141 | 91.10 207 | 95.13 134 | 99.47 152 | 99.97 80 |
|
Baseline_NR-MVSNet | | | 89.13 151 | 89.53 175 | 88.66 131 | 84.71 189 | 94.43 198 | 91.79 148 | 84.49 106 | 95.54 161 | 78.28 126 | 78.52 200 | 72.46 177 | 93.29 146 | 91.10 207 | 94.82 136 | 99.42 170 | 99.86 145 |
|
tfpnnormal | | | 89.09 152 | 89.71 168 | 88.38 134 | 87.37 151 | 96.78 152 | 91.46 151 | 85.20 98 | 90.33 215 | 72.35 143 | 83.45 159 | 69.30 206 | 94.45 132 | 95.29 141 | 92.86 165 | 99.44 163 | 99.93 115 |
|
TranMVSNet+NR-MVSNet | | | 88.88 153 | 89.90 166 | 87.69 140 | 84.06 201 | 95.68 179 | 91.88 146 | 85.23 97 | 95.16 167 | 72.54 140 | 83.06 162 | 70.14 200 | 92.93 148 | 90.81 210 | 94.53 139 | 99.48 149 | 99.89 135 |
|
WR-MVS_H | | | 88.47 154 | 90.55 162 | 86.04 152 | 85.13 172 | 96.07 170 | 89.86 187 | 79.80 149 | 94.37 182 | 72.32 144 | 83.12 161 | 74.44 167 | 89.60 192 | 93.52 172 | 92.40 170 | 99.51 142 | 99.96 99 |
|
SixPastTwentyTwo | | | 88.35 155 | 91.51 155 | 84.66 176 | 85.39 166 | 96.96 150 | 86.57 201 | 79.62 151 | 96.57 154 | 63.73 196 | 87.86 147 | 75.18 162 | 93.43 145 | 94.03 158 | 90.37 206 | 99.24 201 | 99.58 173 |
|
TransMVSNet (Re) | | | 88.33 156 | 89.55 174 | 86.91 146 | 86.65 155 | 95.56 184 | 90.48 161 | 84.44 107 | 92.02 214 | 71.07 154 | 80.13 180 | 72.48 176 | 89.41 193 | 95.05 146 | 94.44 141 | 99.39 184 | 97.14 210 |
|
LP | | | 88.31 157 | 93.18 143 | 82.63 200 | 90.66 131 | 97.98 136 | 87.32 200 | 63.49 225 | 97.17 150 | 63.02 199 | 82.08 165 | 90.47 114 | 91.92 157 | 92.75 185 | 93.42 158 | 99.38 186 | 98.37 199 |
|
MVS-HIRNet | | | 88.27 158 | 94.05 140 | 81.51 204 | 88.90 145 | 98.93 115 | 83.38 211 | 60.52 230 | 98.06 141 | 63.78 195 | 80.67 177 | 90.36 115 | 92.94 147 | 97.29 105 | 96.41 106 | 99.56 131 | 96.66 213 |
|
WR-MVS | | | 88.23 159 | 90.15 164 | 86.00 154 | 84.39 196 | 95.64 180 | 89.96 183 | 81.80 135 | 94.46 180 | 71.60 149 | 82.10 164 | 74.36 168 | 88.76 200 | 92.48 186 | 92.20 172 | 99.46 155 | 99.83 149 |
|
CP-MVSNet | | | 88.09 160 | 89.57 172 | 86.36 150 | 84.63 192 | 95.46 189 | 89.48 189 | 80.53 144 | 93.42 200 | 71.26 153 | 81.25 175 | 69.90 201 | 92.78 150 | 93.30 176 | 93.69 154 | 99.47 152 | 99.96 99 |
|
anonymousdsp | | | 87.98 161 | 92.38 148 | 82.85 198 | 83.68 205 | 96.79 151 | 90.78 156 | 74.06 186 | 95.29 165 | 57.91 213 | 83.33 160 | 83.12 143 | 91.15 184 | 95.96 132 | 92.37 171 | 99.52 138 | 99.76 158 |
|
v6 | | | 87.96 162 | 89.58 171 | 86.08 151 | 85.34 167 | 96.14 164 | 90.44 162 | 82.19 131 | 94.56 174 | 67.43 171 | 81.90 168 | 71.57 183 | 91.62 167 | 91.54 194 | 91.43 187 | 99.43 164 | 99.92 119 |
|
v1neww | | | 87.88 163 | 89.51 177 | 85.97 156 | 85.32 168 | 96.12 165 | 90.33 169 | 82.17 132 | 94.51 175 | 66.96 173 | 81.84 170 | 71.21 186 | 91.64 164 | 91.52 196 | 91.43 187 | 99.42 170 | 99.92 119 |
|
v7new | | | 87.88 163 | 89.51 177 | 85.97 156 | 85.32 168 | 96.12 165 | 90.33 169 | 82.17 132 | 94.51 175 | 66.96 173 | 81.84 170 | 71.21 186 | 91.64 164 | 91.52 196 | 91.43 187 | 99.42 170 | 99.92 119 |
|
LTVRE_ROB | | 88.65 16 | 87.87 165 | 91.11 158 | 84.10 187 | 86.64 156 | 97.47 143 | 94.40 126 | 78.41 162 | 96.13 157 | 52.02 220 | 87.95 146 | 65.92 216 | 93.59 143 | 95.29 141 | 95.09 135 | 99.52 138 | 99.95 108 |
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 |
V42 | | | 87.84 166 | 89.42 179 | 85.99 155 | 85.16 171 | 96.01 173 | 90.52 158 | 81.78 137 | 94.43 181 | 67.59 165 | 81.32 174 | 71.87 179 | 91.48 171 | 91.25 206 | 91.16 201 | 99.43 164 | 99.92 119 |
|
TDRefinement | | | 87.79 167 | 88.76 193 | 86.66 148 | 93.54 80 | 98.02 134 | 95.76 102 | 85.18 99 | 96.57 154 | 67.90 162 | 80.51 179 | 66.51 215 | 78.37 217 | 93.20 178 | 89.73 210 | 99.22 202 | 96.75 212 |
|
MDTV_nov1_ep13_2view | | | 87.75 168 | 93.32 142 | 81.26 206 | 83.74 204 | 96.64 154 | 85.66 204 | 66.20 212 | 98.36 134 | 61.61 203 | 84.34 157 | 87.95 124 | 91.12 185 | 94.01 159 | 92.66 167 | 99.22 202 | 99.27 188 |
|
v7 | | | 87.72 169 | 89.75 167 | 85.35 168 | 85.01 176 | 95.79 178 | 90.43 164 | 78.98 157 | 94.50 178 | 66.39 179 | 78.87 194 | 73.65 171 | 91.85 160 | 93.69 169 | 91.86 178 | 99.45 158 | 99.92 119 |
|
v8 | | | 87.54 170 | 89.33 180 | 85.45 167 | 85.41 165 | 95.50 187 | 90.32 172 | 78.94 158 | 94.35 183 | 66.93 175 | 81.90 168 | 70.99 191 | 91.62 167 | 91.49 199 | 91.22 198 | 99.48 149 | 99.87 142 |
|
v1144 | | | 87.49 171 | 89.64 169 | 84.97 171 | 84.73 188 | 95.84 176 | 90.17 177 | 79.30 153 | 93.96 186 | 64.65 193 | 78.83 196 | 73.38 173 | 91.51 170 | 93.77 166 | 91.77 179 | 99.45 158 | 99.93 115 |
|
v1 | | | 87.48 172 | 88.91 188 | 85.81 159 | 84.93 179 | 96.07 170 | 90.33 169 | 82.45 127 | 93.65 195 | 66.39 179 | 79.38 191 | 70.40 197 | 91.33 177 | 91.58 193 | 91.38 193 | 99.42 170 | 99.93 115 |
|
divwei89l23v2f112 | | | 87.46 173 | 88.97 185 | 85.70 163 | 84.85 184 | 96.08 168 | 90.23 175 | 82.46 125 | 93.69 194 | 65.83 186 | 79.57 188 | 70.54 194 | 91.39 176 | 91.60 192 | 91.39 191 | 99.43 164 | 99.92 119 |
|
v2v482 | | | 87.46 173 | 88.90 189 | 85.78 160 | 84.58 193 | 95.95 175 | 89.90 186 | 82.43 128 | 94.19 184 | 65.65 188 | 79.80 184 | 69.12 207 | 92.67 151 | 91.88 190 | 91.46 185 | 99.45 158 | 99.93 115 |
|
v1141 | | | 87.45 175 | 88.98 184 | 85.67 164 | 84.86 183 | 96.08 168 | 90.23 175 | 82.46 125 | 93.75 190 | 65.64 190 | 79.57 188 | 70.52 195 | 91.41 175 | 91.63 191 | 91.39 191 | 99.42 170 | 99.92 119 |
|
v10 | | | 87.40 176 | 89.62 170 | 84.80 174 | 84.93 179 | 95.07 195 | 90.44 162 | 75.63 177 | 94.51 175 | 66.52 177 | 78.87 194 | 73.47 172 | 91.86 159 | 93.69 169 | 91.87 177 | 99.45 158 | 99.86 145 |
|
pmmvs5 | | | 87.33 177 | 90.01 165 | 84.20 185 | 84.31 198 | 96.04 172 | 87.63 198 | 76.59 174 | 93.17 205 | 65.35 192 | 84.30 158 | 71.68 180 | 91.91 158 | 95.41 138 | 91.37 194 | 99.39 184 | 98.13 202 |
|
N_pmnet | | | 87.31 178 | 91.51 155 | 82.41 203 | 85.13 172 | 95.57 183 | 80.59 215 | 81.79 136 | 96.20 156 | 58.52 211 | 78.62 198 | 85.66 130 | 89.36 194 | 94.64 150 | 92.14 173 | 99.08 207 | 97.72 209 |
|
PS-CasMVS | | | 87.24 179 | 88.52 196 | 85.73 162 | 84.58 193 | 95.35 191 | 89.03 192 | 80.17 145 | 93.11 206 | 68.86 160 | 77.71 202 | 66.89 212 | 92.30 153 | 93.13 180 | 93.50 156 | 99.46 155 | 99.96 99 |
|
EU-MVSNet | | | 87.20 180 | 90.47 163 | 83.38 196 | 85.11 174 | 93.85 203 | 86.10 203 | 79.76 150 | 93.30 204 | 65.39 191 | 84.41 156 | 78.43 150 | 85.04 210 | 92.20 188 | 93.03 164 | 98.86 209 | 98.05 205 |
|
PEN-MVS | | | 87.20 180 | 88.22 200 | 86.01 153 | 84.01 203 | 94.93 197 | 90.00 181 | 81.52 142 | 93.46 199 | 69.29 158 | 79.69 186 | 65.51 217 | 91.72 161 | 91.01 209 | 93.12 161 | 99.49 145 | 99.84 147 |
|
v16 | | | 87.15 182 | 89.13 181 | 84.83 173 | 85.55 163 | 91.94 209 | 90.50 159 | 74.13 185 | 95.06 168 | 67.72 164 | 81.84 170 | 72.55 175 | 91.65 163 | 91.50 198 | 91.42 190 | 99.42 170 | 99.60 170 |
|
v18 | | | 87.14 183 | 88.96 186 | 85.01 170 | 85.57 162 | 92.03 207 | 90.89 154 | 74.62 181 | 94.80 172 | 67.90 162 | 82.02 166 | 71.28 185 | 91.63 166 | 91.53 195 | 91.44 186 | 99.47 152 | 99.60 170 |
|
v17 | | | 86.99 184 | 88.90 189 | 84.76 175 | 85.52 164 | 91.96 208 | 90.50 159 | 74.17 182 | 94.88 170 | 67.33 172 | 81.94 167 | 71.21 186 | 91.57 169 | 91.49 199 | 91.20 199 | 99.48 149 | 99.60 170 |
|
EG-PatchMatch MVS | | | 86.96 185 | 89.56 173 | 83.93 191 | 86.29 157 | 97.61 141 | 90.75 157 | 73.31 192 | 95.43 164 | 66.08 184 | 75.88 211 | 71.31 184 | 87.55 205 | 94.79 148 | 92.74 166 | 99.61 124 | 99.13 192 |
|
v1192 | | | 86.93 186 | 89.01 182 | 84.50 177 | 84.46 195 | 95.51 186 | 89.93 185 | 78.65 160 | 93.75 190 | 62.29 201 | 77.19 204 | 70.88 192 | 92.28 154 | 93.84 163 | 91.96 175 | 99.38 186 | 99.90 131 |
|
v1921920 | | | 86.81 187 | 88.93 187 | 84.33 182 | 84.23 199 | 95.41 190 | 90.09 179 | 78.10 163 | 93.74 192 | 62.17 202 | 76.98 206 | 71.14 189 | 92.05 156 | 93.69 169 | 91.69 182 | 99.32 192 | 99.88 138 |
|
v144192 | | | 86.80 188 | 88.90 189 | 84.35 179 | 84.33 197 | 95.56 184 | 89.34 190 | 77.74 165 | 93.60 196 | 64.03 194 | 77.82 201 | 70.76 193 | 91.28 179 | 92.91 183 | 91.74 181 | 99.37 188 | 99.90 131 |
|
v11 | | | 86.74 189 | 89.01 182 | 84.09 189 | 84.79 186 | 91.79 214 | 90.39 166 | 72.53 199 | 94.47 179 | 65.75 187 | 78.64 197 | 72.96 174 | 91.66 162 | 93.92 161 | 91.69 182 | 99.42 170 | 99.61 169 |
|
DTE-MVSNet | | | 86.70 190 | 87.66 208 | 85.58 165 | 83.30 206 | 94.29 199 | 89.74 188 | 81.53 140 | 92.77 208 | 68.93 159 | 80.13 180 | 64.00 220 | 90.62 187 | 89.45 214 | 93.34 159 | 99.32 192 | 99.67 164 |
|
gg-mvs-nofinetune | | | 86.69 191 | 91.30 157 | 81.30 205 | 90.42 133 | 99.64 78 | 98.50 53 | 61.68 227 | 79.23 228 | 40.35 231 | 66.58 222 | 97.14 80 | 96.92 102 | 98.64 40 | 97.94 63 | 99.91 20 | 99.97 80 |
|
v148 | | | 86.63 192 | 87.79 204 | 85.28 169 | 84.65 191 | 95.97 174 | 86.46 202 | 82.84 123 | 92.91 207 | 71.52 151 | 78.99 193 | 66.74 214 | 86.83 207 | 89.28 215 | 90.69 204 | 99.41 182 | 99.94 112 |
|
V14 | | | 86.54 193 | 88.41 197 | 84.35 179 | 84.94 178 | 91.83 211 | 90.28 174 | 73.48 190 | 93.73 193 | 66.50 178 | 79.89 183 | 71.12 190 | 91.46 172 | 91.48 201 | 91.25 196 | 99.42 170 | 99.58 173 |
|
v15 | | | 86.50 194 | 88.32 198 | 84.37 178 | 85.00 177 | 91.86 210 | 90.30 173 | 73.76 188 | 93.90 188 | 66.28 182 | 79.78 185 | 70.37 198 | 91.45 173 | 91.48 201 | 91.27 195 | 99.43 164 | 99.58 173 |
|
V9 | | | 86.42 195 | 88.26 199 | 84.27 183 | 84.88 181 | 91.80 212 | 90.34 168 | 73.18 194 | 93.92 187 | 66.37 181 | 79.68 187 | 70.25 199 | 91.42 174 | 91.43 203 | 91.23 197 | 99.42 170 | 99.55 178 |
|
v12 | | | 86.32 196 | 88.22 200 | 84.10 187 | 84.76 187 | 91.80 212 | 89.94 184 | 72.97 196 | 93.85 189 | 66.18 183 | 79.98 182 | 69.72 205 | 91.33 177 | 91.40 204 | 91.20 199 | 99.42 170 | 99.56 177 |
|
v13 | | | 86.27 197 | 88.16 202 | 84.06 190 | 84.85 184 | 91.77 215 | 90.00 181 | 72.77 198 | 93.56 197 | 66.06 185 | 79.25 192 | 70.50 196 | 91.25 181 | 91.35 205 | 91.15 202 | 99.42 170 | 99.55 178 |
|
v1240 | | | 86.24 198 | 88.56 195 | 83.54 192 | 84.05 202 | 95.21 194 | 89.27 191 | 76.76 172 | 93.42 200 | 60.68 208 | 75.99 210 | 69.80 203 | 91.21 183 | 93.83 165 | 91.76 180 | 99.29 196 | 99.91 130 |
|
v52 | | | 85.80 199 | 87.74 205 | 83.53 193 | 82.87 209 | 95.31 193 | 88.71 193 | 77.04 170 | 92.23 211 | 63.53 197 | 76.91 207 | 69.80 203 | 89.78 189 | 90.05 212 | 90.07 208 | 99.26 200 | 99.82 150 |
|
V4 | | | 85.78 200 | 87.74 205 | 83.50 194 | 82.90 208 | 95.33 192 | 88.62 194 | 77.05 169 | 92.14 213 | 63.45 198 | 76.91 207 | 69.85 202 | 89.72 190 | 90.07 211 | 90.05 209 | 99.27 199 | 99.81 151 |
|
pmmvs6 | | | 85.75 201 | 86.97 209 | 84.34 181 | 84.88 181 | 95.59 182 | 87.41 199 | 79.19 155 | 87.81 221 | 67.56 166 | 63.05 225 | 77.76 151 | 89.15 195 | 93.45 174 | 91.90 176 | 97.83 216 | 99.21 189 |
|
v7n | | | 85.39 202 | 87.70 207 | 82.70 199 | 82.77 211 | 95.64 180 | 88.27 196 | 74.83 179 | 92.30 210 | 62.58 200 | 76.37 209 | 64.80 219 | 88.38 202 | 94.29 156 | 90.61 205 | 99.34 189 | 99.87 142 |
|
gm-plane-assit | | | 84.93 203 | 91.61 154 | 77.14 213 | 84.14 200 | 91.29 217 | 66.18 228 | 69.70 202 | 85.22 224 | 47.95 226 | 78.58 199 | 89.24 118 | 94.90 127 | 98.82 34 | 98.12 60 | 99.99 6 | 100.00 1 |
|
CMPMVS | | 65.66 17 | 84.62 204 | 85.02 212 | 84.15 186 | 95.40 65 | 97.79 139 | 88.35 195 | 79.22 154 | 89.66 219 | 60.71 207 | 72.20 215 | 73.94 169 | 87.32 206 | 86.73 219 | 84.55 224 | 93.90 226 | 90.31 224 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
v748 | | | 84.47 205 | 86.06 210 | 82.62 201 | 82.85 210 | 95.02 196 | 83.73 209 | 78.48 161 | 90.20 217 | 67.45 170 | 75.86 212 | 61.27 222 | 83.84 211 | 89.87 213 | 90.28 207 | 99.34 189 | 99.90 131 |
|
Anonymous20231206 | | | 84.28 206 | 89.53 175 | 78.17 210 | 82.31 213 | 94.16 201 | 82.57 212 | 76.51 175 | 93.38 203 | 52.98 218 | 79.47 190 | 73.74 170 | 75.45 219 | 95.07 145 | 94.41 143 | 99.18 205 | 96.46 216 |
|
new_pmnet | | | 84.12 207 | 87.89 203 | 79.72 208 | 80.43 214 | 94.14 202 | 80.26 216 | 74.14 184 | 96.01 158 | 56.30 217 | 74.94 213 | 76.45 158 | 88.59 201 | 93.11 181 | 89.31 211 | 98.59 212 | 91.27 223 |
|
test20.03 | | | 83.86 208 | 88.73 194 | 78.16 211 | 82.60 212 | 93.00 204 | 81.61 214 | 74.68 180 | 92.36 209 | 57.50 214 | 83.01 163 | 74.48 166 | 73.30 223 | 92.40 187 | 91.14 203 | 99.29 196 | 94.75 219 |
|
test2356 | | | 83.84 209 | 91.77 152 | 74.59 217 | 78.71 216 | 89.10 221 | 78.24 220 | 72.07 201 | 96.78 152 | 45.18 229 | 96.19 94 | 76.77 155 | 74.87 221 | 93.17 179 | 94.01 151 | 98.44 213 | 96.38 217 |
|
pmmvs-eth3d | | | 82.92 210 | 83.31 215 | 82.47 202 | 76.97 218 | 91.76 216 | 83.79 207 | 76.10 176 | 90.33 215 | 69.95 157 | 71.04 218 | 48.09 226 | 89.02 197 | 93.85 162 | 89.14 212 | 99.02 208 | 98.96 194 |
|
PM-MVS | | | 82.79 211 | 84.51 213 | 80.77 207 | 77.22 217 | 92.13 206 | 83.61 210 | 73.31 192 | 93.50 198 | 61.06 204 | 77.15 205 | 46.52 229 | 90.55 188 | 94.14 157 | 89.05 214 | 98.85 210 | 99.12 193 |
|
testus | | | 82.22 212 | 88.82 192 | 74.52 218 | 79.14 215 | 89.37 220 | 78.38 218 | 72.99 195 | 97.57 145 | 44.54 230 | 93.44 122 | 58.13 224 | 74.20 222 | 92.96 182 | 93.67 155 | 97.89 215 | 96.58 214 |
|
pmmvs3 | | | 80.91 213 | 85.62 211 | 75.42 215 | 75.01 220 | 89.09 222 | 75.31 222 | 68.70 203 | 86.99 222 | 46.74 228 | 81.18 176 | 62.91 221 | 87.95 203 | 93.84 163 | 89.06 213 | 98.80 211 | 96.23 218 |
|
MIMVSNet1 | | | 80.64 214 | 83.97 214 | 76.76 214 | 68.91 229 | 91.15 219 | 78.32 219 | 75.47 178 | 89.58 220 | 56.64 216 | 65.10 223 | 65.17 218 | 82.14 212 | 93.51 173 | 91.64 184 | 99.10 206 | 91.66 222 |
|
MDA-MVSNet-bldmvs | | | 80.30 215 | 82.83 216 | 77.34 212 | 69.16 228 | 94.29 199 | 72.16 223 | 81.97 134 | 90.14 218 | 57.32 215 | 94.01 117 | 47.97 227 | 86.81 208 | 68.74 230 | 86.82 220 | 96.63 218 | 97.86 207 |
|
new-patchmatchnet | | | 78.17 216 | 80.82 217 | 75.07 216 | 76.93 219 | 91.20 218 | 71.90 224 | 73.32 191 | 86.59 223 | 48.91 223 | 67.11 221 | 47.85 228 | 81.19 213 | 88.18 216 | 87.02 219 | 98.19 214 | 97.79 208 |
|
Anonymous20231211 | | | 74.10 217 | 74.22 225 | 73.97 219 | 74.36 221 | 87.76 223 | 75.92 221 | 72.78 197 | 74.83 233 | 52.25 219 | 44.18 232 | 42.42 232 | 73.07 224 | 86.16 220 | 86.24 222 | 95.44 224 | 97.94 206 |
|
FPMVS | | | 73.80 218 | 74.62 223 | 72.84 220 | 83.09 207 | 84.44 225 | 83.89 206 | 73.64 189 | 92.20 212 | 48.50 224 | 72.19 216 | 59.51 223 | 63.16 226 | 69.13 229 | 66.26 234 | 84.74 231 | 78.59 234 |
|
1111 | | | 73.79 219 | 78.62 219 | 68.16 222 | 69.34 226 | 81.48 227 | 59.42 232 | 52.46 233 | 78.55 229 | 50.42 221 | 62.43 226 | 71.67 181 | 80.43 215 | 86.79 217 | 88.22 215 | 96.87 217 | 81.17 233 |
|
testmv | | | 71.50 220 | 77.62 220 | 64.36 223 | 72.64 222 | 81.28 229 | 59.32 234 | 66.24 210 | 83.91 225 | 35.02 235 | 69.74 219 | 46.18 230 | 57.12 229 | 85.60 222 | 87.48 217 | 95.84 221 | 89.16 226 |
|
test1235678 | | | 71.50 220 | 77.61 221 | 64.36 223 | 72.64 222 | 81.26 230 | 59.31 235 | 66.22 211 | 83.90 226 | 35.02 235 | 69.74 219 | 46.18 230 | 57.12 229 | 85.60 222 | 87.47 218 | 95.84 221 | 89.15 227 |
|
Gipuma | | | 71.02 222 | 72.60 227 | 69.19 221 | 71.31 224 | 75.11 233 | 66.36 227 | 61.65 228 | 94.93 169 | 47.29 227 | 38.74 233 | 38.52 234 | 75.52 218 | 86.09 221 | 85.92 223 | 93.01 227 | 88.87 228 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
.test1245 | | | 70.78 223 | 79.90 218 | 60.13 228 | 69.34 226 | 81.48 227 | 59.42 232 | 52.46 233 | 78.55 229 | 50.42 221 | 62.43 226 | 71.67 181 | 80.43 215 | 86.79 217 | 78.71 226 | 48.74 237 | 99.65 165 |
|
test12356 | | | 69.94 224 | 75.85 222 | 63.04 225 | 70.04 225 | 79.32 232 | 61.62 230 | 65.84 215 | 80.56 227 | 36.30 234 | 71.45 217 | 39.38 233 | 48.79 235 | 83.64 224 | 88.02 216 | 95.64 223 | 88.56 229 |
|
GG-mvs-BLEND | | | 69.85 225 | 99.39 35 | 35.39 235 | 3.67 239 | 99.94 17 | 99.10 37 | 1.69 237 | 99.85 40 | 3.19 242 | 98.13 73 | 99.46 52 | 4.92 237 | 99.23 27 | 99.14 28 | 99.80 49 | 100.00 1 |
|
PMMVS2 | | | 65.18 226 | 68.25 228 | 61.59 226 | 61.37 232 | 79.72 231 | 59.18 236 | 61.80 226 | 64.72 234 | 37.33 232 | 53.82 229 | 35.59 235 | 54.46 233 | 73.94 228 | 80.52 225 | 95.40 225 | 89.43 225 |
|
PMVS | | 60.14 18 | 62.67 227 | 64.05 229 | 61.06 227 | 68.32 230 | 53.27 240 | 52.23 237 | 67.63 206 | 75.07 232 | 48.30 225 | 58.27 228 | 57.43 225 | 49.99 234 | 67.20 231 | 62.42 235 | 79.87 235 | 74.68 236 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
testmvs | | | 61.76 228 | 72.90 226 | 48.76 232 | 21.21 237 | 68.61 235 | 66.11 229 | 37.38 235 | 94.83 171 | 33.06 237 | 64.31 224 | 29.72 236 | 86.08 209 | 74.44 227 | 78.71 226 | 48.74 237 | 99.65 165 |
|
E-PMN | | | 55.33 229 | 55.79 231 | 54.81 230 | 59.81 234 | 57.23 238 | 38.83 238 | 63.59 223 | 64.06 236 | 24.66 239 | 35.33 235 | 26.40 238 | 58.69 228 | 55.41 233 | 70.54 231 | 83.26 232 | 81.56 232 |
|
EMVS | | | 55.14 230 | 55.29 232 | 54.97 229 | 60.87 233 | 57.52 237 | 38.58 239 | 63.57 224 | 64.54 235 | 23.36 240 | 36.96 234 | 27.99 237 | 60.69 227 | 51.17 234 | 66.61 233 | 82.73 234 | 82.25 231 |
|
no-one | | | 52.34 231 | 53.36 234 | 51.14 231 | 57.63 235 | 69.39 234 | 35.07 241 | 61.58 229 | 44.14 238 | 37.06 233 | 34.80 236 | 26.36 239 | 32.65 236 | 50.68 235 | 70.83 230 | 82.88 233 | 77.30 235 |
|
MVE | | 58.81 19 | 52.07 232 | 55.15 233 | 48.48 233 | 42.45 236 | 62.35 236 | 36.41 240 | 54.70 232 | 49.88 237 | 27.65 238 | 29.98 237 | 18.08 240 | 54.87 232 | 65.93 232 | 77.26 228 | 74.79 236 | 82.59 230 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
test123 | | | 48.14 233 | 58.11 230 | 36.51 234 | 8.71 238 | 56.81 239 | 59.55 231 | 24.08 236 | 77.50 231 | 14.41 241 | 49.20 230 | 11.94 242 | 80.98 214 | 41.62 236 | 69.81 232 | 31.32 239 | 99.90 131 |
|
sosnet-low-res | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 240 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 243 | 0.00 238 | 0.00 243 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
sosnet | | | 0.00 234 | 0.00 235 | 0.00 236 | 0.00 240 | 0.00 241 | 0.00 242 | 0.00 238 | 0.00 239 | 0.00 243 | 0.00 238 | 0.00 243 | 0.00 238 | 0.00 237 | 0.00 236 | 0.00 240 | 0.00 237 |
|
ambc | | | | 74.33 224 | | 66.84 231 | 84.26 226 | 84.17 205 | | 93.39 202 | 58.99 210 | 45.93 231 | 18.06 241 | 70.61 225 | 93.94 160 | 86.62 221 | 92.61 229 | 98.13 202 |
|
MTAPA | | | | | | | | | | | 96.61 10 | | 100.00 1 | | | | | |
|
MTMP | | | | | | | | | | | 97.42 7 | | 100.00 1 | | | | | |
|
Patchmatch-RL test | | | | | | | | 68.01 226 | | | | | | | | | | |
|
tmp_tt | | | | | 78.81 209 | 98.80 40 | 85.73 224 | 70.08 225 | 77.87 164 | 98.68 113 | 83.71 104 | 99.53 27 | 74.55 165 | 54.97 231 | 78.28 226 | 72.43 229 | 87.45 230 | |
|
XVS | | | | | | 95.09 68 | 99.94 17 | 97.49 70 | | | 88.58 82 | | 99.98 27 | | | | 99.78 60 | |
|
X-MVStestdata | | | | | | 95.09 68 | 99.94 17 | 97.49 70 | | | 88.58 82 | | 99.98 27 | | | | 99.78 60 | |
|
abl_6 | | | | | 97.06 31 | 99.17 34 | 99.82 55 | 98.68 48 | 90.86 46 | 100.00 1 | 94.53 27 | 97.40 81 | 100.00 1 | 99.17 49 | | | 99.93 16 | 99.99 47 |
|
mPP-MVS | | | | | | 99.23 32 | | | | | | | 99.87 36 | | | | | |
|
NP-MVS | | | | | | | | | | 99.79 49 | | | | | | | | |
|
Patchmtry | | | | | | | 99.00 109 | 95.46 111 | 65.50 216 | | 67.51 167 | | | | | | | |
|
DeepMVS_CX | | | | | | | 97.31 146 | 79.48 217 | 89.65 58 | 98.66 115 | 60.89 206 | 94.40 112 | 66.89 212 | 87.65 204 | 81.69 225 | | 92.76 228 | 94.24 221 |
|