DeepPCF-MVS | | 97.16 4 | 97.58 43 | 99.72 17 | 95.07 58 | 98.45 46 | 99.96 7 | 93.83 143 | 95.93 32 | 100.00 1 | 90.79 57 | 98.38 68 | 99.85 41 | 95.28 133 | 99.94 2 | 99.97 1 | 96.15 230 | 99.97 82 |
|
DeepC-MVS_fast | | 98.03 2 | 99.05 19 | 99.78 6 | 98.21 22 | 99.47 19 | 99.97 3 | 99.75 12 | 96.80 15 | 99.97 7 | 93.58 35 | 98.68 64 | 99.94 37 | 99.69 18 | 99.93 4 | 99.95 2 | 99.96 12 | 99.98 68 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CDPH-MVS | | | 97.88 40 | 99.59 27 | 95.89 47 | 98.90 39 | 99.95 12 | 99.40 31 | 92.86 40 | 99.86 44 | 85.33 107 | 98.62 65 | 99.45 58 | 99.06 58 | 99.29 27 | 99.94 3 | 99.81 49 | 100.00 1 |
|
3Dnovator | | 95.01 8 | 97.98 37 | 98.89 43 | 96.92 36 | 99.36 27 | 99.76 73 | 98.72 49 | 89.98 55 | 99.98 3 | 93.99 30 | 94.60 119 | 99.43 59 | 99.50 31 | 98.55 49 | 99.91 4 | 99.99 6 | 99.98 68 |
|
DELS-MVS | | | 97.05 47 | 98.05 72 | 95.88 49 | 97.09 55 | 99.99 1 | 98.82 46 | 90.30 52 | 98.44 138 | 91.40 49 | 92.91 133 | 96.57 86 | 97.68 103 | 98.56 48 | 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 |
QAPM | | | 97.90 39 | 98.89 43 | 96.74 37 | 99.35 28 | 99.80 69 | 98.84 45 | 90.20 54 | 99.94 22 | 92.85 36 | 94.17 122 | 99.78 45 | 99.42 33 | 98.71 38 | 99.87 6 | 99.79 56 | 99.98 68 |
|
MSLP-MVS++ | | | 99.39 2 | 99.76 8 | 98.95 2 | 99.60 12 | 99.99 1 | 99.83 2 | 96.82 14 | 99.92 30 | 97.58 6 | 99.58 26 | 100.00 1 | 99.93 1 | 98.98 32 | 99.86 7 | 99.96 12 | 100.00 1 |
|
APD-MVS | | | 99.33 7 | 99.85 1 | 98.73 11 | 99.61 10 | 99.92 36 | 99.77 5 | 96.91 7 | 99.93 25 | 96.31 17 | 99.59 24 | 99.95 36 | 99.84 8 | 99.73 17 | 99.84 8 | 99.95 14 | 100.00 1 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
3Dnovator+ | | 95.21 7 | 98.17 32 | 99.08 38 | 97.12 32 | 99.28 32 | 99.78 71 | 98.61 51 | 89.93 57 | 99.93 25 | 95.36 23 | 95.50 105 | 100.00 1 | 99.56 29 | 98.58 47 | 99.80 9 | 99.95 14 | 99.97 82 |
|
PHI-MVS | | | 98.85 23 | 99.67 20 | 97.89 26 | 98.63 45 | 99.93 30 | 98.95 43 | 95.20 35 | 99.84 48 | 94.94 25 | 99.74 10 | 100.00 1 | 99.69 18 | 98.40 59 | 99.75 10 | 99.93 18 | 99.99 49 |
|
DeepC-MVS | | 96.33 6 | 97.05 47 | 97.59 84 | 96.42 41 | 97.37 53 | 99.92 36 | 99.10 39 | 96.54 27 | 99.34 99 | 86.64 101 | 91.93 140 | 93.15 111 | 99.11 56 | 99.11 31 | 99.68 11 | 99.73 105 | 99.97 82 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
SD-MVS | | | 99.16 13 | 99.73 15 | 98.49 17 | 97.93 49 | 99.95 12 | 99.74 15 | 96.94 5 | 99.96 16 | 96.60 13 | 99.47 33 | 100.00 1 | 99.88 6 | 99.15 30 | 99.59 12 | 99.84 31 | 100.00 1 |
|
TSAR-MVS + ACMM | | | 98.30 30 | 99.64 22 | 96.74 37 | 99.08 37 | 99.94 17 | 99.67 23 | 96.73 20 | 99.97 7 | 86.30 104 | 98.30 69 | 99.99 27 | 98.78 70 | 99.73 17 | 99.57 13 | 99.88 28 | 99.98 68 |
|
CNLPA | | | 99.24 8 | 99.58 29 | 98.85 7 | 99.34 29 | 99.95 12 | 99.32 32 | 96.65 23 | 99.96 16 | 98.44 2 | 98.97 52 | 100.00 1 | 99.57 28 | 98.66 40 | 99.56 14 | 99.76 75 | 99.97 82 |
|
MAR-MVS | | | 97.03 50 | 98.00 74 | 95.89 47 | 99.32 30 | 99.74 76 | 96.76 95 | 84.89 107 | 99.97 7 | 94.86 26 | 98.29 70 | 90.58 122 | 99.67 21 | 98.02 82 | 99.50 15 | 99.82 41 | 99.92 124 |
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 |
SMA-MVS | | | 99.34 6 | 99.79 4 | 98.81 9 | 99.69 1 | 99.94 17 | 99.75 12 | 96.91 7 | 99.98 3 | 96.76 11 | 99.37 38 | 100.00 1 | 99.90 4 | 99.88 9 | 99.46 16 | 99.84 31 | 99.92 124 |
|
OpenMVS | | 94.03 11 | 96.87 52 | 98.10 71 | 95.44 53 | 99.29 31 | 99.78 71 | 98.46 57 | 89.92 58 | 99.47 86 | 85.78 105 | 91.05 143 | 98.50 68 | 99.30 40 | 98.49 57 | 99.41 17 | 99.89 25 | 99.98 68 |
|
IS_MVSNet | | | 96.66 55 | 98.62 52 | 94.38 77 | 92.41 114 | 99.70 78 | 97.19 82 | 87.67 92 | 99.05 112 | 91.27 53 | 95.09 111 | 98.46 72 | 97.95 93 | 98.64 42 | 99.37 18 | 99.79 56 | 100.00 1 |
|
Vis-MVSNet (Re-imp) | | | 95.60 90 | 98.52 56 | 92.19 109 | 92.37 115 | 99.56 86 | 96.37 102 | 87.41 96 | 98.95 115 | 84.77 112 | 94.88 117 | 98.48 71 | 92.44 162 | 98.63 44 | 99.37 18 | 99.76 75 | 99.77 167 |
|
HFP-MVS | | | 99.19 11 | 99.77 7 | 98.51 16 | 99.55 17 | 99.94 17 | 99.76 6 | 96.84 13 | 99.88 37 | 95.27 24 | 99.67 11 | 100.00 1 | 99.85 7 | 99.56 22 | 99.36 20 | 99.79 56 | 99.97 82 |
|
ACMMPR | | | 99.12 15 | 99.76 8 | 98.36 18 | 99.45 21 | 99.94 17 | 99.75 12 | 96.70 22 | 99.93 25 | 94.65 28 | 99.65 16 | 99.96 34 | 99.84 8 | 99.51 24 | 99.35 21 | 99.79 56 | 99.96 101 |
|
EPNet_dtu | | | 95.10 99 | 98.81 48 | 90.78 115 | 98.38 48 | 98.47 133 | 96.54 98 | 89.36 62 | 99.78 56 | 65.65 198 | 99.31 39 | 98.24 75 | 94.79 138 | 98.28 66 | 99.35 21 | 99.93 18 | 98.27 210 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
SteuartSystems-ACMMP | | | 98.95 22 | 99.80 3 | 97.95 25 | 99.43 24 | 99.96 7 | 99.76 6 | 96.45 29 | 99.82 50 | 93.63 33 | 99.64 19 | 100.00 1 | 98.56 77 | 99.90 8 | 99.31 23 | 99.84 31 | 100.00 1 |
Skip Steuart: Steuart Systems R&D Blog. |
PGM-MVS | | | 98.47 29 | 99.73 15 | 97.00 34 | 99.68 3 | 99.94 17 | 99.76 6 | 91.74 42 | 99.84 48 | 91.17 54 | 100.00 1 | 99.69 49 | 99.81 11 | 99.38 26 | 99.30 24 | 99.82 41 | 99.95 110 |
|
APDe-MVS | | | 99.40 1 | 99.81 2 | 98.92 4 | 99.62 6 | 99.96 7 | 99.76 6 | 96.87 11 | 99.95 20 | 97.66 4 | 99.57 27 | 100.00 1 | 99.63 25 | 99.88 9 | 99.28 25 | 100.00 1 | 100.00 1 |
|
X-MVS | | | 98.62 26 | 99.75 11 | 97.29 29 | 99.50 18 | 99.94 17 | 99.71 20 | 96.55 26 | 99.85 45 | 88.58 87 | 99.65 16 | 99.98 29 | 99.67 21 | 99.60 21 | 99.26 26 | 99.77 68 | 99.97 82 |
|
CSCG | | | 98.22 31 | 98.37 59 | 98.04 23 | 99.60 12 | 99.82 57 | 99.45 30 | 93.59 38 | 99.16 104 | 96.46 15 | 98.22 77 | 95.86 93 | 99.41 35 | 96.33 131 | 99.22 27 | 99.75 88 | 99.94 116 |
|
train_agg | | | 98.62 26 | 99.76 8 | 97.28 30 | 99.03 38 | 99.93 30 | 99.65 24 | 96.37 30 | 99.98 3 | 89.24 82 | 99.53 28 | 99.83 42 | 99.59 27 | 99.85 12 | 99.19 28 | 99.80 52 | 100.00 1 |
|
GG-mvs-BLEND | | | 69.85 234 | 99.39 36 | 35.39 244 | 3.67 250 | 99.94 17 | 99.10 39 | 1.69 247 | 99.85 45 | 3.19 251 | 98.13 78 | 99.46 56 | 4.92 247 | 99.23 29 | 99.14 29 | 99.80 52 | 100.00 1 |
|
HSP-MVS | | | 99.36 5 | 99.79 4 | 98.85 7 | 99.61 10 | 99.96 7 | 99.71 20 | 96.94 5 | 99.97 7 | 97.11 9 | 99.60 23 | 100.00 1 | 99.70 17 | 99.96 1 | 99.12 30 | 100.00 1 | 99.96 101 |
|
MVS_Test | | | 95.74 84 | 98.18 69 | 92.90 103 | 92.16 118 | 99.49 92 | 97.36 78 | 84.30 116 | 99.79 54 | 84.94 110 | 96.65 95 | 93.63 108 | 98.85 66 | 98.61 46 | 99.10 31 | 99.81 49 | 100.00 1 |
|
MVS_111021_LR | | | 98.15 34 | 99.69 19 | 96.36 42 | 99.23 34 | 99.93 30 | 97.79 62 | 91.84 41 | 99.87 40 | 90.53 65 | 100.00 1 | 99.57 54 | 98.93 62 | 99.44 25 | 99.08 32 | 99.85 29 | 99.95 110 |
|
EPNet | | | 98.11 35 | 99.63 23 | 96.34 43 | 98.44 47 | 99.88 50 | 98.55 52 | 90.25 53 | 99.93 25 | 92.60 40 | 100.00 1 | 99.73 46 | 98.41 79 | 98.87 34 | 99.02 33 | 99.82 41 | 99.97 82 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
CANet | | | 97.62 41 | 98.94 42 | 96.08 45 | 97.19 54 | 99.93 30 | 99.29 34 | 90.38 51 | 99.87 40 | 91.00 56 | 95.79 104 | 99.51 55 | 98.72 75 | 98.53 53 | 99.00 34 | 99.90 24 | 99.99 49 |
|
canonicalmvs | | | 95.80 76 | 97.02 99 | 94.37 78 | 92.96 97 | 99.47 93 | 97.49 72 | 84.58 110 | 99.44 88 | 92.05 42 | 98.54 66 | 86.65 137 | 99.37 37 | 96.18 134 | 98.93 35 | 99.77 68 | 99.92 124 |
|
MVS_0304 | | | 97.04 49 | 98.72 50 | 95.08 57 | 96.32 61 | 99.90 44 | 99.15 37 | 89.61 61 | 99.89 34 | 87.22 98 | 95.47 106 | 98.22 76 | 98.22 85 | 98.63 44 | 98.90 36 | 99.93 18 | 100.00 1 |
|
MVS_111021_HR | | | 97.94 38 | 99.59 27 | 96.02 46 | 99.27 33 | 99.97 3 | 97.03 88 | 90.44 50 | 99.89 34 | 90.75 58 | 100.00 1 | 99.73 46 | 98.68 76 | 98.67 39 | 98.89 37 | 99.95 14 | 99.97 82 |
|
FMVSNet3 | | | 95.59 91 | 97.51 87 | 93.34 91 | 89.48 149 | 96.57 167 | 97.67 64 | 84.17 118 | 99.48 83 | 89.76 75 | 95.09 111 | 94.35 99 | 99.14 54 | 98.37 61 | 98.86 38 | 99.82 41 | 99.89 142 |
|
OMC-MVS | | | 98.59 28 | 99.07 39 | 98.03 24 | 99.41 25 | 99.90 44 | 99.26 35 | 94.33 37 | 99.94 22 | 96.03 18 | 96.68 94 | 99.72 48 | 99.42 33 | 98.86 35 | 98.84 39 | 99.72 109 | 99.58 183 |
|
LS3D | | | 96.44 60 | 97.31 91 | 95.41 54 | 97.06 56 | 99.87 51 | 99.51 28 | 97.48 1 | 99.57 75 | 79.00 130 | 95.39 107 | 89.19 129 | 99.81 11 | 98.55 49 | 98.84 39 | 99.62 132 | 99.78 166 |
|
CHOSEN 1792x2688 | | | 93.69 115 | 94.89 141 | 92.28 108 | 96.17 62 | 99.84 52 | 95.69 112 | 83.17 130 | 98.54 132 | 82.04 123 | 77.58 214 | 91.15 118 | 96.90 113 | 98.36 62 | 98.82 41 | 99.73 105 | 99.98 68 |
|
UA-Net | | | 94.95 101 | 98.66 51 | 90.63 117 | 94.60 78 | 98.94 123 | 96.03 107 | 85.28 102 | 98.01 151 | 78.92 131 | 97.42 88 | 99.96 34 | 89.09 206 | 98.95 33 | 98.80 42 | 99.82 41 | 98.57 207 |
|
NCCC | | | 99.24 8 | 99.75 11 | 98.65 12 | 99.63 5 | 99.96 7 | 99.76 6 | 96.91 7 | 99.97 7 | 95.86 20 | 99.67 11 | 100.00 1 | 99.75 14 | 99.85 12 | 98.80 42 | 99.98 9 | 99.97 82 |
|
ESAPD | | | 99.37 4 | 99.74 14 | 98.94 3 | 99.60 12 | 99.94 17 | 99.87 1 | 96.95 3 | 99.94 22 | 97.42 7 | 99.62 21 | 100.00 1 | 99.80 13 | 99.91 5 | 98.78 44 | 99.98 9 | 100.00 1 |
|
TSAR-MVS + MP. | | | 98.99 20 | 99.61 26 | 98.27 20 | 97.88 50 | 99.92 36 | 99.71 20 | 96.80 15 | 99.96 16 | 95.58 22 | 98.71 63 | 100.00 1 | 99.68 20 | 99.91 5 | 98.78 44 | 99.99 6 | 100.00 1 |
|
thresconf0.02 | | | 96.46 58 | 98.87 45 | 93.64 87 | 92.77 102 | 99.11 110 | 97.05 87 | 89.36 62 | 99.64 71 | 85.14 108 | 99.07 45 | 96.84 84 | 97.72 98 | 98.72 37 | 98.76 46 | 99.78 63 | 99.95 110 |
|
CNVR-MVS | | | 99.39 2 | 99.75 11 | 98.98 1 | 99.69 1 | 99.95 12 | 99.76 6 | 96.91 7 | 99.98 3 | 97.59 5 | 99.64 19 | 100.00 1 | 99.93 1 | 99.94 2 | 98.75 47 | 99.97 11 | 99.97 82 |
|
CHOSEN 280x420 | | | 97.16 46 | 99.58 29 | 94.35 80 | 96.95 57 | 99.97 3 | 97.19 82 | 81.55 150 | 99.92 30 | 91.75 44 | 100.00 1 | 100.00 1 | 98.84 67 | 98.55 49 | 98.65 48 | 99.79 56 | 99.97 82 |
|
FMVSNet2 | | | 94.48 106 | 95.95 128 | 92.77 106 | 89.11 150 | 96.47 169 | 96.90 89 | 83.38 126 | 99.11 108 | 88.64 86 | 87.50 163 | 92.26 113 | 98.87 63 | 97.91 86 | 98.60 49 | 99.74 93 | 99.81 161 |
|
CP-MVS | | | 99.14 14 | 99.67 20 | 98.53 15 | 99.45 21 | 99.94 17 | 99.63 26 | 96.62 25 | 99.82 50 | 95.92 19 | 99.65 16 | 100.00 1 | 99.71 16 | 99.76 15 | 98.56 50 | 99.83 37 | 100.00 1 |
|
MCST-MVS | | | 99.08 17 | 99.72 17 | 98.33 19 | 99.59 15 | 99.97 3 | 99.78 4 | 96.96 2 | 99.95 20 | 93.72 32 | 99.67 11 | 100.00 1 | 99.90 4 | 99.91 5 | 98.55 51 | 100.00 1 | 100.00 1 |
|
TSAR-MVS + GP. | | | 98.06 36 | 99.55 32 | 96.32 44 | 94.72 75 | 99.92 36 | 99.22 36 | 89.98 55 | 99.97 7 | 94.77 27 | 99.94 9 | 100.00 1 | 99.43 32 | 98.52 56 | 98.53 52 | 99.79 56 | 100.00 1 |
|
casdiffmvs1 | | | 96.22 63 | 98.26 64 | 93.85 84 | 92.52 111 | 99.45 95 | 97.35 79 | 84.50 113 | 99.87 40 | 89.96 74 | 97.60 86 | 93.89 105 | 98.79 69 | 98.49 57 | 98.51 53 | 99.95 14 | 100.00 1 |
|
v1.0 | | | 91.56 146 | 85.41 222 | 98.74 10 | 99.62 6 | 99.94 17 | 99.79 3 | 96.87 11 | 99.93 25 | 96.33 16 | 99.59 24 | 100.00 1 | 99.84 8 | 99.88 9 | 98.50 54 | 100.00 1 | 0.00 246 |
|
MP-MVS | | | 98.82 24 | 99.63 23 | 97.88 27 | 99.41 25 | 99.91 43 | 99.74 15 | 96.76 19 | 99.88 37 | 91.89 43 | 99.50 31 | 99.94 37 | 99.65 23 | 99.71 20 | 98.49 55 | 99.82 41 | 99.97 82 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
HPM-MVS++ | | | 98.98 21 | 99.62 25 | 98.22 21 | 99.62 6 | 99.94 17 | 99.74 15 | 96.95 3 | 99.87 40 | 93.76 31 | 99.49 32 | 100.00 1 | 99.39 36 | 99.73 17 | 98.35 56 | 99.89 25 | 99.96 101 |
|
Vis-MVSNet | | | 93.08 126 | 96.76 107 | 88.78 138 | 91.14 136 | 99.63 83 | 94.85 129 | 83.34 128 | 97.19 159 | 74.78 146 | 91.92 141 | 93.15 111 | 88.81 209 | 97.59 93 | 98.35 56 | 99.78 63 | 99.49 192 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
EPP-MVSNet | | | 96.29 61 | 98.34 61 | 93.90 82 | 91.77 127 | 99.38 102 | 95.45 121 | 87.25 97 | 99.38 95 | 91.36 51 | 94.86 118 | 98.49 70 | 97.83 96 | 98.01 83 | 98.23 58 | 99.75 88 | 99.99 49 |
|
DWT-MVSNet_training | | | 96.26 62 | 98.44 58 | 93.72 86 | 92.58 105 | 99.34 104 | 96.15 105 | 83.00 133 | 99.76 59 | 93.63 33 | 97.89 82 | 99.46 56 | 97.23 109 | 94.43 164 | 98.19 59 | 99.70 113 | 100.00 1 |
|
test0.0.03 1 | | | 95.15 97 | 97.87 78 | 91.99 110 | 91.69 132 | 98.82 127 | 93.04 148 | 83.60 124 | 99.65 69 | 88.80 85 | 94.15 123 | 97.67 80 | 94.97 135 | 96.62 120 | 98.16 60 | 99.83 37 | 100.00 1 |
|
diffmvs1 | | | 95.69 86 | 97.82 80 | 93.21 93 | 92.34 117 | 99.45 95 | 97.12 85 | 83.38 126 | 99.66 68 | 87.92 91 | 97.90 81 | 91.50 116 | 98.73 73 | 98.08 77 | 98.16 60 | 99.75 88 | 99.98 68 |
|
ACMMP_Plus | | | 98.68 25 | 99.58 29 | 97.62 28 | 99.62 6 | 99.92 36 | 99.72 19 | 96.78 17 | 99.71 65 | 90.13 71 | 99.66 15 | 99.99 27 | 99.64 24 | 99.78 14 | 98.14 62 | 99.82 41 | 99.89 142 |
|
gm-plane-assit | | | 84.93 213 | 91.61 164 | 77.14 223 | 84.14 212 | 91.29 229 | 66.18 239 | 69.70 212 | 85.22 235 | 47.95 235 | 78.58 210 | 89.24 128 | 94.90 137 | 98.82 36 | 98.12 63 | 99.99 6 | 100.00 1 |
|
casdiffmvs | | | 95.82 75 | 97.83 79 | 93.47 89 | 92.15 119 | 99.52 87 | 96.32 103 | 84.29 117 | 99.50 79 | 89.73 78 | 97.82 83 | 91.67 115 | 98.38 80 | 98.30 65 | 98.00 64 | 99.92 22 | 100.00 1 |
|
EPMVS | | | 94.08 113 | 98.54 55 | 88.87 134 | 92.51 112 | 99.47 93 | 94.18 139 | 66.53 219 | 99.68 67 | 82.40 121 | 95.24 108 | 99.40 60 | 97.86 95 | 98.12 73 | 97.99 65 | 99.75 88 | 99.88 147 |
|
zzz-MVS | | | 99.12 15 | 99.52 34 | 98.65 12 | 99.58 16 | 99.93 30 | 99.74 15 | 96.72 21 | 99.44 88 | 96.47 14 | 99.62 21 | 100.00 1 | 99.63 25 | 99.74 16 | 97.97 66 | 99.77 68 | 99.94 116 |
|
gg-mvs-nofinetune | | | 86.69 201 | 91.30 167 | 81.30 215 | 90.42 143 | 99.64 82 | 98.50 55 | 61.68 237 | 79.23 239 | 40.35 240 | 66.58 233 | 97.14 83 | 96.92 112 | 98.64 42 | 97.94 67 | 99.91 23 | 99.97 82 |
|
MSDG | | | 97.29 45 | 97.55 85 | 97.00 34 | 98.66 44 | 99.71 77 | 99.03 41 | 96.15 31 | 99.59 74 | 89.67 80 | 92.77 136 | 94.86 97 | 98.75 71 | 98.22 69 | 97.94 67 | 99.72 109 | 99.76 168 |
|
tfpn_n400 | | | 95.76 82 | 98.21 66 | 92.90 103 | 92.57 109 | 99.05 114 | 96.42 100 | 88.50 83 | 99.49 81 | 83.08 117 | 98.90 54 | 94.24 102 | 97.07 110 | 98.10 75 | 97.93 69 | 99.74 93 | 99.76 168 |
|
tfpnconf | | | 95.76 82 | 98.21 66 | 92.90 103 | 92.57 109 | 99.05 114 | 96.42 100 | 88.50 83 | 99.49 81 | 83.08 117 | 98.90 54 | 94.24 102 | 97.07 110 | 98.10 75 | 97.93 69 | 99.74 93 | 99.76 168 |
|
diffmvs | | | 95.11 98 | 96.95 104 | 92.96 101 | 92.09 121 | 99.44 99 | 97.26 80 | 83.80 123 | 99.44 88 | 86.43 103 | 96.77 93 | 87.25 136 | 98.49 78 | 97.92 85 | 97.93 69 | 99.70 113 | 99.90 137 |
|
PVSNet_BlendedMVS | | | 96.01 67 | 96.48 116 | 95.46 51 | 96.47 59 | 99.89 48 | 95.64 113 | 91.23 46 | 99.75 61 | 91.59 45 | 96.80 90 | 82.44 155 | 98.05 88 | 98.53 53 | 97.92 72 | 99.80 52 | 100.00 1 |
|
PVSNet_Blended | | | 96.01 67 | 96.48 116 | 95.46 51 | 96.47 59 | 99.89 48 | 95.64 113 | 91.23 46 | 99.75 61 | 91.59 45 | 96.80 90 | 82.44 155 | 98.05 88 | 98.53 53 | 97.92 72 | 99.80 52 | 100.00 1 |
|
IterMVS-LS | | | 93.50 118 | 96.22 124 | 90.33 122 | 90.93 137 | 95.50 199 | 94.83 130 | 80.54 154 | 98.92 117 | 79.11 129 | 90.64 144 | 93.70 107 | 96.79 116 | 96.93 115 | 97.85 74 | 99.78 63 | 99.99 49 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
HyFIR lowres test | | | 93.13 124 | 94.48 145 | 91.56 112 | 96.12 64 | 99.68 79 | 93.52 145 | 79.98 158 | 97.24 158 | 81.73 126 | 72.66 225 | 95.74 95 | 98.29 83 | 98.27 67 | 97.79 75 | 99.70 113 | 100.00 1 |
|
PCF-MVS | | 97.20 3 | 97.49 44 | 98.20 68 | 96.66 39 | 97.62 52 | 99.92 36 | 98.93 44 | 96.64 24 | 98.53 133 | 88.31 90 | 94.04 124 | 99.58 53 | 98.94 60 | 97.53 95 | 97.79 75 | 99.54 144 | 99.97 82 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
CVMVSNet | | | 92.13 141 | 95.40 135 | 88.32 145 | 91.29 135 | 97.29 156 | 91.85 157 | 86.42 98 | 96.71 164 | 71.84 158 | 89.56 148 | 91.18 117 | 88.98 208 | 96.17 135 | 97.76 77 | 99.51 152 | 99.14 201 |
|
CPTT-MVS | | | 99.08 17 | 99.53 33 | 98.57 14 | 99.44 23 | 99.93 30 | 99.60 27 | 95.92 33 | 99.77 57 | 97.01 10 | 99.67 11 | 100.00 1 | 99.72 15 | 99.56 22 | 97.76 77 | 99.70 113 | 99.98 68 |
|
CDS-MVSNet | | | 94.32 108 | 97.00 101 | 91.19 114 | 89.82 147 | 98.71 129 | 95.51 118 | 85.14 106 | 96.85 161 | 82.33 122 | 92.48 137 | 96.40 89 | 94.71 139 | 96.86 117 | 97.76 77 | 99.63 130 | 99.92 124 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
PLC | | 98.06 1 | 99.17 12 | 99.38 37 | 98.92 4 | 99.47 19 | 99.90 44 | 99.48 29 | 96.47 28 | 99.96 16 | 98.73 1 | 99.52 30 | 100.00 1 | 99.55 30 | 98.54 52 | 97.73 80 | 99.84 31 | 99.99 49 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
MDTV_nov1_ep13 | | | 94.32 108 | 98.77 49 | 89.14 132 | 91.70 131 | 99.52 87 | 95.21 123 | 72.09 210 | 99.80 53 | 78.91 132 | 96.32 98 | 99.62 51 | 97.71 102 | 98.39 60 | 97.71 81 | 99.22 213 | 100.00 1 |
|
PatchmatchNet | | | 93.48 121 | 98.84 47 | 87.22 151 | 91.93 124 | 99.39 101 | 92.55 152 | 66.06 223 | 99.71 65 | 75.61 142 | 98.24 75 | 99.59 52 | 97.35 106 | 97.87 87 | 97.64 82 | 99.83 37 | 99.43 193 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
tfpn_ndepth | | | 96.84 53 | 98.58 53 | 94.81 69 | 93.18 85 | 99.62 84 | 96.83 93 | 88.75 77 | 99.73 63 | 92.38 41 | 98.45 67 | 96.34 90 | 97.90 94 | 98.34 64 | 97.59 83 | 99.84 31 | 99.99 49 |
|
ADS-MVSNet | | | 92.91 129 | 97.97 75 | 87.01 153 | 92.07 122 | 99.27 106 | 92.70 149 | 65.39 228 | 99.85 45 | 75.40 143 | 94.93 116 | 98.26 73 | 96.86 114 | 96.09 137 | 97.52 84 | 99.65 124 | 99.84 157 |
|
PVSNet_Blended_VisFu | | | 95.37 92 | 97.44 89 | 92.95 102 | 95.20 68 | 99.80 69 | 92.68 150 | 88.41 87 | 99.12 107 | 87.64 92 | 88.31 155 | 99.10 63 | 94.07 148 | 98.27 67 | 97.51 85 | 99.73 105 | 100.00 1 |
|
DI_MVS_plusplus_trai | | | 95.29 93 | 97.02 99 | 93.28 92 | 91.76 128 | 99.52 87 | 97.84 61 | 85.67 99 | 99.08 111 | 87.29 96 | 87.76 158 | 97.46 82 | 97.31 107 | 97.83 89 | 97.48 86 | 99.83 37 | 100.00 1 |
|
Fast-Effi-MVS+ | | | 92.11 142 | 94.33 147 | 89.52 128 | 89.06 153 | 99.00 118 | 95.13 124 | 76.72 184 | 98.59 131 | 78.21 136 | 89.99 146 | 77.35 163 | 98.34 82 | 97.97 84 | 97.44 87 | 99.67 122 | 99.96 101 |
|
Anonymous20240521 | | | 95.85 74 | 97.53 86 | 93.89 83 | 93.20 84 | 97.01 160 | 97.14 84 | 84.77 108 | 99.16 104 | 90.38 70 | 98.96 53 | 93.73 106 | 98.23 84 | 96.57 121 | 97.37 88 | 99.64 128 | 99.93 119 |
|
TAPA-MVS | | 96.62 5 | 97.60 42 | 98.46 57 | 96.60 40 | 98.73 43 | 99.90 44 | 99.30 33 | 94.96 36 | 99.46 87 | 87.57 93 | 96.05 103 | 98.53 67 | 99.26 47 | 98.04 80 | 97.33 89 | 99.77 68 | 99.88 147 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
AdaColmap | | | 99.21 10 | 99.45 35 | 98.92 4 | 99.67 4 | 99.95 12 | 99.65 24 | 96.77 18 | 99.97 7 | 97.67 3 | 100.00 1 | 99.69 49 | 99.93 1 | 99.26 28 | 97.25 90 | 99.85 29 | 100.00 1 |
|
conf0.002 | | | 96.51 57 | 97.75 81 | 95.07 58 | 93.11 86 | 99.83 53 | 97.67 64 | 89.10 67 | 98.62 125 | 91.47 48 | 99.39 34 | 91.68 114 | 99.28 42 | 97.49 97 | 97.24 91 | 99.76 75 | 100.00 1 |
|
tfpnview11 | | | 95.78 78 | 98.17 70 | 93.01 100 | 92.58 105 | 99.04 116 | 96.64 97 | 88.72 79 | 99.63 73 | 83.08 117 | 98.90 54 | 94.24 102 | 97.25 108 | 98.35 63 | 97.21 92 | 99.77 68 | 99.80 165 |
|
GBi-Net | | | 95.19 95 | 96.99 102 | 93.09 96 | 89.11 150 | 96.47 169 | 96.90 89 | 84.17 118 | 99.48 83 | 89.76 75 | 95.09 111 | 94.35 99 | 98.87 63 | 96.50 122 | 97.21 92 | 99.74 93 | 99.81 161 |
|
test1 | | | 95.19 95 | 96.99 102 | 93.09 96 | 89.11 150 | 96.47 169 | 96.90 89 | 84.17 118 | 99.48 83 | 89.76 75 | 95.09 111 | 94.35 99 | 98.87 63 | 96.50 122 | 97.21 92 | 99.74 93 | 99.81 161 |
|
FMVSNet1 | | | 92.55 136 | 93.66 151 | 91.26 113 | 87.91 160 | 96.12 176 | 94.75 131 | 81.69 149 | 97.67 154 | 85.63 106 | 80.56 189 | 87.88 135 | 98.15 87 | 96.50 122 | 97.21 92 | 99.41 192 | 99.71 173 |
|
CR-MVSNet | | | 92.32 140 | 97.97 75 | 85.74 170 | 90.63 142 | 98.95 121 | 95.46 119 | 65.50 226 | 99.09 109 | 67.51 177 | 94.20 121 | 98.18 77 | 95.59 131 | 98.16 71 | 97.20 96 | 99.74 93 | 100.00 1 |
|
PatchT | | | 91.06 149 | 97.66 82 | 83.36 207 | 90.32 144 | 98.96 120 | 82.30 224 | 64.72 231 | 98.45 137 | 67.51 177 | 93.28 132 | 97.60 81 | 95.59 131 | 98.16 71 | 97.20 96 | 99.70 113 | 100.00 1 |
|
CANet_DTU | | | 94.90 102 | 98.98 41 | 90.13 123 | 94.74 74 | 99.81 65 | 98.53 54 | 82.23 141 | 99.97 7 | 66.76 186 | 100.00 1 | 98.50 68 | 98.74 72 | 97.52 96 | 97.19 98 | 99.76 75 | 99.88 147 |
|
tfpn1000 | | | 96.58 56 | 98.37 59 | 94.50 76 | 93.04 93 | 99.59 85 | 96.53 99 | 88.54 82 | 99.73 63 | 91.59 45 | 98.28 71 | 95.76 94 | 97.46 105 | 98.19 70 | 97.10 99 | 99.82 41 | 99.96 101 |
|
UGNet | | | 96.05 65 | 98.55 54 | 93.13 94 | 94.64 76 | 99.65 81 | 94.70 132 | 87.78 90 | 99.40 94 | 89.69 79 | 98.25 74 | 99.25 62 | 92.12 165 | 96.50 122 | 97.08 100 | 99.84 31 | 99.72 172 |
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 |
IterMVS | | | 91.65 144 | 96.62 108 | 85.85 167 | 90.27 145 | 95.80 189 | 95.32 122 | 74.15 194 | 98.91 118 | 60.95 215 | 88.79 154 | 97.76 79 | 94.69 141 | 98.04 80 | 97.07 101 | 99.73 105 | 100.00 1 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
MIMVSNet | | | 91.01 150 | 96.22 124 | 84.93 182 | 85.24 181 | 98.09 142 | 90.40 175 | 64.96 230 | 97.55 156 | 72.65 148 | 96.23 99 | 90.81 120 | 96.79 116 | 96.69 118 | 97.06 102 | 99.52 148 | 97.09 220 |
|
Effi-MVS+ | | | 93.06 127 | 95.94 129 | 89.70 126 | 90.82 138 | 99.45 95 | 95.71 111 | 78.94 169 | 98.72 121 | 74.71 147 | 97.92 80 | 80.73 159 | 98.35 81 | 97.72 90 | 97.05 103 | 99.70 113 | 100.00 1 |
|
Anonymous202405211 | | | | 95.78 131 | | 93.26 83 | 99.52 87 | 96.70 96 | 88.55 81 | 97.93 152 | | 88.99 151 | 90.68 121 | 98.99 59 | 96.46 126 | 97.02 104 | 99.64 128 | 99.89 142 |
|
MVSTER | | | 97.00 51 | 98.85 46 | 94.83 68 | 92.71 103 | 97.43 154 | 99.03 41 | 85.52 100 | 99.82 50 | 92.74 38 | 99.15 43 | 99.94 37 | 99.19 50 | 98.66 40 | 96.99 105 | 99.79 56 | 99.98 68 |
|
PMMVS | | | 96.45 59 | 98.24 65 | 94.36 79 | 92.58 105 | 99.01 117 | 97.08 86 | 87.42 95 | 99.88 37 | 90.06 72 | 99.39 34 | 94.63 98 | 99.33 39 | 97.85 88 | 96.99 105 | 99.70 113 | 99.96 101 |
|
Anonymous20231211 | | | 94.96 100 | 94.99 139 | 94.91 62 | 93.01 95 | 99.44 99 | 96.85 92 | 88.49 85 | 98.78 120 | 92.61 39 | 83.94 169 | 90.25 125 | 98.94 60 | 95.87 142 | 96.77 107 | 99.58 137 | 99.89 142 |
|
tfpn | | | 95.93 69 | 97.06 98 | 94.62 73 | 92.94 101 | 99.81 65 | 97.25 81 | 88.71 80 | 98.32 145 | 89.98 73 | 98.79 62 | 88.55 132 | 99.11 56 | 97.26 112 | 96.71 108 | 99.75 88 | 99.98 68 |
|
Effi-MVS+-dtu | | | 93.13 124 | 97.13 96 | 88.47 142 | 88.86 156 | 99.19 108 | 96.79 94 | 79.08 167 | 99.64 71 | 70.01 166 | 97.51 87 | 89.38 126 | 96.53 122 | 97.60 92 | 96.55 109 | 99.57 139 | 100.00 1 |
|
thres100view900 | | | 95.86 72 | 96.62 108 | 94.97 61 | 93.10 88 | 99.83 53 | 97.76 63 | 89.15 66 | 98.62 125 | 90.69 59 | 99.00 48 | 84.86 142 | 99.30 40 | 97.57 94 | 96.48 110 | 99.81 49 | 100.00 1 |
|
testgi | | | 92.47 138 | 95.68 134 | 88.73 139 | 90.68 140 | 98.35 136 | 91.67 160 | 79.50 163 | 98.96 114 | 77.12 138 | 95.17 110 | 85.84 140 | 93.95 149 | 95.75 144 | 96.47 111 | 99.45 168 | 99.21 199 |
|
RPMNet | | | 92.64 135 | 97.88 77 | 86.53 158 | 90.79 139 | 98.95 121 | 95.13 124 | 64.44 232 | 99.09 109 | 72.36 152 | 93.58 129 | 99.01 64 | 96.74 119 | 98.05 78 | 96.45 112 | 99.71 111 | 100.00 1 |
|
tpmp4_e23 | | | 92.95 128 | 96.28 122 | 89.06 133 | 91.80 126 | 98.81 128 | 94.95 126 | 67.56 217 | 99.21 102 | 82.97 120 | 96.54 96 | 88.52 133 | 97.47 104 | 94.47 163 | 96.42 113 | 99.61 133 | 100.00 1 |
|
MVS-HIRNet | | | 88.27 168 | 94.05 150 | 81.51 214 | 88.90 155 | 98.93 124 | 83.38 222 | 60.52 240 | 98.06 150 | 63.78 205 | 80.67 188 | 90.36 124 | 92.94 157 | 97.29 110 | 96.41 114 | 99.56 141 | 96.66 222 |
|
TSAR-MVS + COLMAP | | | 95.20 94 | 95.03 138 | 95.41 54 | 96.17 62 | 98.69 130 | 99.11 38 | 93.40 39 | 99.97 7 | 84.89 111 | 98.23 76 | 75.01 174 | 99.34 38 | 97.27 111 | 96.37 115 | 99.58 137 | 99.64 178 |
|
thres200 | | | 95.77 81 | 96.55 110 | 94.86 66 | 93.09 92 | 99.82 57 | 97.63 70 | 88.85 71 | 98.49 134 | 90.66 63 | 98.99 51 | 84.86 142 | 99.20 48 | 97.41 102 | 96.28 116 | 99.76 75 | 100.00 1 |
|
HQP-MVS | | | 94.48 106 | 95.39 136 | 93.42 90 | 95.10 69 | 98.35 136 | 98.19 58 | 91.41 44 | 99.77 57 | 79.79 127 | 99.30 40 | 77.08 164 | 96.25 123 | 96.93 115 | 96.28 116 | 99.76 75 | 99.99 49 |
|
PatchMatch-RL | | | 96.84 53 | 98.03 73 | 95.47 50 | 98.84 41 | 99.81 65 | 95.61 116 | 89.20 65 | 99.65 69 | 91.28 52 | 99.39 34 | 93.46 109 | 98.18 86 | 98.05 78 | 96.28 116 | 99.69 120 | 99.55 188 |
|
COLMAP_ROB | | 93.56 12 | 96.03 66 | 96.83 106 | 95.11 56 | 97.87 51 | 99.52 87 | 98.81 47 | 91.40 45 | 99.42 91 | 84.97 109 | 90.46 145 | 96.82 85 | 98.05 88 | 96.46 126 | 96.19 119 | 99.54 144 | 98.92 205 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
tfpn111 | | | 95.79 77 | 96.55 110 | 94.89 63 | 93.10 88 | 99.82 57 | 97.67 64 | 88.85 71 | 98.62 125 | 90.69 59 | 99.07 45 | 84.86 142 | 99.28 42 | 97.41 102 | 96.10 120 | 99.76 75 | 99.99 49 |
|
conf0.01 | | | 96.20 64 | 97.19 95 | 95.05 60 | 93.11 86 | 99.83 53 | 97.67 64 | 89.06 68 | 98.62 125 | 91.38 50 | 99.19 42 | 89.09 130 | 99.28 42 | 97.48 98 | 96.10 120 | 99.76 75 | 100.00 1 |
|
conf200view11 | | | 95.78 78 | 96.54 112 | 94.89 63 | 93.10 88 | 99.82 57 | 97.67 64 | 88.85 71 | 98.62 125 | 90.69 59 | 99.00 48 | 84.86 142 | 99.28 42 | 97.41 102 | 96.10 120 | 99.76 75 | 99.99 49 |
|
tfpn200view9 | | | 95.78 78 | 96.54 112 | 94.89 63 | 93.10 88 | 99.82 57 | 97.67 64 | 88.85 71 | 98.62 125 | 90.69 59 | 99.00 48 | 84.86 142 | 99.28 42 | 97.41 102 | 96.10 120 | 99.76 75 | 99.99 49 |
|
thres400 | | | 95.72 85 | 96.48 116 | 94.84 67 | 93.00 96 | 99.83 53 | 97.55 71 | 88.93 69 | 98.49 134 | 90.61 64 | 98.86 57 | 84.63 147 | 99.20 48 | 97.45 99 | 96.10 120 | 99.77 68 | 99.99 49 |
|
CostFormer | | | 93.50 118 | 96.50 115 | 90.00 124 | 91.69 132 | 98.65 132 | 93.88 142 | 67.64 215 | 98.97 113 | 89.16 83 | 97.79 84 | 88.92 131 | 97.97 92 | 95.14 155 | 96.06 125 | 99.63 130 | 100.00 1 |
|
tpmrst | | | 92.52 137 | 97.45 88 | 86.77 156 | 92.15 119 | 99.36 103 | 92.53 153 | 65.95 224 | 99.53 77 | 72.50 150 | 92.22 138 | 99.83 42 | 97.81 97 | 95.18 154 | 96.05 126 | 99.69 120 | 100.00 1 |
|
FC-MVSNet-train | | | 94.61 103 | 96.27 123 | 92.68 107 | 92.35 116 | 97.14 158 | 93.45 147 | 87.73 91 | 98.93 116 | 87.31 95 | 96.42 97 | 89.35 127 | 95.67 128 | 96.06 140 | 96.01 127 | 99.56 141 | 99.98 68 |
|
Fast-Effi-MVS+-dtu | | | 92.73 132 | 97.62 83 | 87.02 152 | 88.91 154 | 98.83 126 | 95.79 109 | 73.98 198 | 99.89 34 | 68.62 171 | 97.73 85 | 93.30 110 | 95.21 134 | 97.67 91 | 95.96 128 | 99.59 135 | 100.00 1 |
|
TAMVS | | | 92.43 139 | 94.21 149 | 90.35 121 | 88.68 157 | 98.85 125 | 94.15 140 | 81.53 151 | 95.58 171 | 83.61 115 | 87.05 164 | 86.45 138 | 94.71 139 | 96.27 133 | 95.91 129 | 99.42 180 | 99.38 195 |
|
ACMMP | | | 98.16 33 | 99.01 40 | 97.18 31 | 98.86 40 | 99.92 36 | 98.77 48 | 95.73 34 | 99.31 100 | 91.15 55 | 100.00 1 | 99.81 44 | 98.82 68 | 98.11 74 | 95.91 129 | 99.77 68 | 99.97 82 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
LGP-MVS_train | | | 93.60 116 | 95.05 137 | 91.90 111 | 94.90 72 | 98.29 139 | 97.93 60 | 88.06 88 | 99.14 106 | 74.83 145 | 99.26 41 | 76.50 168 | 96.07 125 | 96.31 132 | 95.90 131 | 99.59 135 | 99.97 82 |
|
view800 | | | 95.62 89 | 96.38 119 | 94.73 72 | 92.96 97 | 99.81 65 | 97.38 77 | 88.75 77 | 98.35 144 | 90.43 69 | 98.81 61 | 84.54 150 | 99.13 55 | 97.35 108 | 95.82 132 | 99.76 75 | 99.98 68 |
|
testpf | | | 91.26 148 | 97.28 92 | 84.23 194 | 89.52 148 | 97.45 153 | 88.08 207 | 56.08 241 | 99.76 59 | 78.71 133 | 95.06 115 | 98.26 73 | 93.44 154 | 94.72 160 | 95.69 133 | 99.57 139 | 99.99 49 |
|
CLD-MVS | | | 94.53 104 | 94.45 146 | 94.61 74 | 93.85 81 | 98.36 135 | 98.12 59 | 89.68 59 | 99.35 98 | 89.62 81 | 95.19 109 | 77.08 164 | 96.66 120 | 95.51 146 | 95.67 134 | 99.74 93 | 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 | | | 94.50 105 | 95.70 132 | 93.11 95 | 92.68 104 | 99.67 80 | 96.04 106 | 87.81 89 | 97.52 157 | 83.71 113 | 96.20 101 | 84.52 151 | 98.73 73 | 96.39 129 | 95.66 135 | 99.71 111 | 99.92 124 |
|
tpm | | | 89.60 158 | 94.93 140 | 83.39 205 | 89.94 146 | 97.11 159 | 90.09 189 | 65.28 229 | 98.67 123 | 60.03 219 | 96.79 92 | 84.38 152 | 95.66 130 | 91.90 200 | 95.65 136 | 99.32 203 | 99.98 68 |
|
view600 | | | 95.64 87 | 96.38 119 | 94.79 70 | 92.96 97 | 99.82 57 | 97.48 75 | 88.85 71 | 98.38 139 | 90.52 66 | 98.84 59 | 84.61 148 | 99.15 52 | 97.41 102 | 95.60 137 | 99.76 75 | 99.99 49 |
|
thres600view7 | | | 95.64 87 | 96.38 119 | 94.79 70 | 92.96 97 | 99.82 57 | 97.48 75 | 88.85 71 | 98.38 139 | 90.52 66 | 98.84 59 | 84.61 148 | 99.15 52 | 97.41 102 | 95.60 137 | 99.76 75 | 99.99 49 |
|
FMVSNet5 | | | 93.53 117 | 96.09 127 | 90.56 119 | 86.74 163 | 92.84 217 | 92.64 151 | 77.50 177 | 99.41 93 | 88.97 84 | 98.02 79 | 97.81 78 | 98.00 91 | 94.85 158 | 95.43 139 | 99.50 154 | 94.25 229 |
|
tpm cat1 | | | 93.29 123 | 96.53 114 | 89.50 129 | 91.84 125 | 99.18 109 | 94.70 132 | 67.70 214 | 98.38 139 | 86.67 99 | 89.16 149 | 99.38 61 | 96.66 120 | 94.33 165 | 95.30 140 | 99.43 174 | 100.00 1 |
|
FC-MVSNet-test | | | 92.78 131 | 96.19 126 | 88.80 137 | 88.00 159 | 97.54 151 | 93.60 144 | 82.36 140 | 98.16 146 | 79.71 128 | 91.55 142 | 95.41 96 | 89.65 201 | 96.09 137 | 95.23 141 | 99.49 155 | 99.31 196 |
|
DU-MVS | | | 89.49 160 | 90.60 171 | 88.19 146 | 84.71 201 | 96.20 173 | 90.94 162 | 84.58 110 | 95.54 172 | 75.69 140 | 87.52 161 | 68.74 219 | 93.78 151 | 91.10 218 | 95.13 142 | 99.47 162 | 99.97 82 |
|
LTVRE_ROB | | 88.65 16 | 87.87 175 | 91.11 168 | 84.10 197 | 86.64 166 | 97.47 152 | 94.40 136 | 78.41 173 | 96.13 168 | 52.02 229 | 87.95 156 | 65.92 227 | 93.59 153 | 95.29 151 | 95.09 143 | 99.52 148 | 99.95 110 |
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 |
Baseline_NR-MVSNet | | | 89.13 161 | 89.53 185 | 88.66 140 | 84.71 201 | 94.43 210 | 91.79 158 | 84.49 114 | 95.54 172 | 78.28 135 | 78.52 211 | 72.46 188 | 93.29 156 | 91.10 218 | 94.82 144 | 99.42 180 | 99.86 155 |
|
UniMVSNet_NR-MVSNet | | | 90.50 151 | 92.31 159 | 88.38 143 | 85.04 187 | 96.34 172 | 90.94 162 | 85.32 101 | 95.87 170 | 75.69 140 | 87.68 159 | 78.49 160 | 93.78 151 | 93.21 188 | 94.60 145 | 99.53 147 | 99.97 82 |
|
OPM-MVS | | | 93.50 118 | 93.00 157 | 94.07 81 | 95.82 65 | 98.26 140 | 98.49 56 | 91.62 43 | 94.69 184 | 81.93 124 | 92.82 135 | 76.18 172 | 96.82 115 | 96.12 136 | 94.57 146 | 99.74 93 | 98.39 208 |
|
TranMVSNet+NR-MVSNet | | | 88.88 163 | 89.90 176 | 87.69 149 | 84.06 213 | 95.68 191 | 91.88 156 | 85.23 103 | 95.16 178 | 72.54 149 | 83.06 173 | 70.14 211 | 92.93 158 | 90.81 221 | 94.53 147 | 99.48 159 | 99.89 142 |
|
pm-mvs1 | | | 89.68 157 | 92.00 160 | 86.96 154 | 86.23 168 | 96.62 166 | 90.36 177 | 83.05 132 | 93.97 196 | 72.15 155 | 81.77 184 | 82.10 157 | 90.69 196 | 95.38 149 | 94.50 148 | 99.29 207 | 99.65 175 |
|
TransMVSNet (Re) | | | 88.33 166 | 89.55 184 | 86.91 155 | 86.65 165 | 95.56 196 | 90.48 171 | 84.44 115 | 92.02 225 | 71.07 164 | 80.13 191 | 72.48 187 | 89.41 203 | 95.05 157 | 94.44 149 | 99.39 194 | 97.14 219 |
|
USDC | | | 90.36 154 | 91.68 163 | 88.82 136 | 92.58 105 | 98.02 143 | 96.27 104 | 79.83 159 | 98.37 142 | 70.61 165 | 89.05 150 | 67.50 221 | 94.17 146 | 95.77 143 | 94.43 150 | 99.46 165 | 98.62 206 |
|
Anonymous20231206 | | | 84.28 216 | 89.53 185 | 78.17 220 | 82.31 225 | 94.16 213 | 82.57 223 | 76.51 186 | 93.38 214 | 52.98 228 | 79.47 201 | 73.74 181 | 75.45 230 | 95.07 156 | 94.41 151 | 99.18 216 | 96.46 225 |
|
test-LLR | | | 93.71 114 | 97.23 93 | 89.60 127 | 91.69 132 | 99.10 111 | 94.68 134 | 83.60 124 | 99.36 96 | 71.94 156 | 93.82 126 | 96.51 87 | 95.96 126 | 97.42 100 | 94.37 152 | 99.74 93 | 99.99 49 |
|
TESTMET0.1,1 | | | 92.87 130 | 97.23 93 | 87.79 148 | 86.96 162 | 99.10 111 | 94.68 134 | 77.46 178 | 99.36 96 | 71.94 156 | 93.82 126 | 96.51 87 | 95.96 126 | 97.42 100 | 94.37 152 | 99.74 93 | 99.99 49 |
|
test-mter | | | 92.67 134 | 97.13 96 | 87.47 150 | 86.72 164 | 99.07 113 | 94.28 138 | 76.90 182 | 99.21 102 | 71.53 160 | 93.63 128 | 96.32 91 | 95.67 128 | 97.32 109 | 94.36 154 | 99.74 93 | 99.99 49 |
|
ACMP | | 94.49 9 | 94.19 112 | 94.74 142 | 93.56 88 | 94.25 79 | 98.32 138 | 96.02 108 | 89.35 64 | 98.90 119 | 87.28 97 | 99.14 44 | 76.41 170 | 94.94 136 | 96.07 139 | 94.35 155 | 99.49 155 | 99.99 49 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
TinyColmap | | | 89.94 156 | 90.88 169 | 88.84 135 | 92.43 113 | 97.91 147 | 95.59 117 | 80.10 157 | 98.12 148 | 71.33 162 | 84.56 165 | 67.46 222 | 94.15 147 | 95.57 145 | 94.27 156 | 99.43 174 | 98.26 211 |
|
ACMH | | 92.34 14 | 91.59 145 | 93.02 156 | 89.92 125 | 93.97 80 | 97.98 145 | 90.10 188 | 84.70 109 | 98.46 136 | 76.80 139 | 93.38 131 | 71.94 189 | 94.39 143 | 95.34 150 | 94.04 157 | 99.54 144 | 100.00 1 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
dps | | | 94.29 110 | 97.33 90 | 90.75 116 | 92.02 123 | 99.21 107 | 94.31 137 | 66.97 218 | 99.50 79 | 95.61 21 | 96.22 100 | 98.64 66 | 96.08 124 | 93.71 179 | 94.03 158 | 99.52 148 | 99.98 68 |
|
test2356 | | | 83.84 219 | 91.77 162 | 74.59 227 | 78.71 228 | 89.10 233 | 78.24 232 | 72.07 211 | 96.78 163 | 45.18 238 | 96.19 102 | 76.77 166 | 74.87 232 | 93.17 190 | 94.01 159 | 98.44 224 | 96.38 226 |
|
ACMH+ | | 92.61 13 | 91.80 143 | 93.03 155 | 90.37 120 | 93.03 94 | 98.17 141 | 94.00 141 | 84.13 121 | 98.12 148 | 77.39 137 | 91.95 139 | 74.62 175 | 94.36 145 | 94.62 162 | 93.82 160 | 99.32 203 | 99.87 152 |
|
ACMM | | 94.44 10 | 94.26 111 | 94.62 143 | 93.84 85 | 94.86 73 | 97.73 149 | 93.48 146 | 90.76 49 | 99.27 101 | 87.46 94 | 99.04 47 | 76.60 167 | 96.76 118 | 96.37 130 | 93.76 161 | 99.74 93 | 99.55 188 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
CP-MVSNet | | | 88.09 170 | 89.57 182 | 86.36 159 | 84.63 204 | 95.46 201 | 89.48 199 | 80.53 155 | 93.42 211 | 71.26 163 | 81.25 186 | 69.90 212 | 92.78 160 | 93.30 187 | 93.69 162 | 99.47 162 | 99.96 101 |
|
testus | | | 82.22 222 | 88.82 202 | 74.52 228 | 79.14 227 | 89.37 232 | 78.38 230 | 72.99 206 | 97.57 155 | 44.54 239 | 93.44 130 | 58.13 235 | 74.20 233 | 92.96 193 | 93.67 163 | 97.89 226 | 96.58 223 |
|
PS-CasMVS | | | 87.24 189 | 88.52 206 | 85.73 171 | 84.58 205 | 95.35 203 | 89.03 202 | 80.17 156 | 93.11 217 | 68.86 170 | 77.71 213 | 66.89 223 | 92.30 163 | 93.13 191 | 93.50 164 | 99.46 165 | 99.96 101 |
|
NR-MVSNet | | | 89.52 159 | 90.71 170 | 88.14 147 | 86.19 169 | 96.20 173 | 92.07 155 | 84.58 110 | 95.54 172 | 75.27 144 | 87.52 161 | 67.96 220 | 91.24 192 | 94.33 165 | 93.45 165 | 99.49 155 | 99.97 82 |
|
LP | | | 88.31 167 | 93.18 153 | 82.63 210 | 90.66 141 | 97.98 145 | 87.32 210 | 63.49 235 | 97.17 160 | 63.02 209 | 82.08 176 | 90.47 123 | 91.92 167 | 92.75 196 | 93.42 166 | 99.38 196 | 98.37 209 |
|
DTE-MVSNet | | | 86.70 200 | 87.66 218 | 85.58 174 | 83.30 218 | 94.29 211 | 89.74 198 | 81.53 151 | 92.77 219 | 68.93 169 | 80.13 191 | 64.00 231 | 90.62 197 | 89.45 225 | 93.34 167 | 99.32 203 | 99.67 174 |
|
thisisatest0530 | | | 95.89 70 | 98.32 62 | 93.06 98 | 91.76 128 | 99.75 74 | 94.94 127 | 87.60 93 | 99.91 32 | 86.66 100 | 98.28 71 | 99.98 29 | 97.72 98 | 97.10 113 | 93.24 168 | 99.65 124 | 99.95 110 |
|
tttt0517 | | | 95.88 71 | 98.31 63 | 93.04 99 | 91.75 130 | 99.75 74 | 94.90 128 | 87.60 93 | 99.91 32 | 86.63 102 | 98.28 71 | 99.98 29 | 97.72 98 | 97.10 113 | 93.24 168 | 99.65 124 | 99.95 110 |
|
IB-MVS | | 90.59 15 | 92.70 133 | 95.70 132 | 89.21 131 | 94.62 77 | 99.45 95 | 83.77 219 | 88.92 70 | 99.53 77 | 92.82 37 | 98.86 57 | 86.08 139 | 75.24 231 | 92.81 195 | 93.17 170 | 99.89 25 | 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 |
PEN-MVS | | | 87.20 190 | 88.22 210 | 86.01 162 | 84.01 215 | 94.93 209 | 90.00 191 | 81.52 153 | 93.46 210 | 69.29 168 | 79.69 197 | 65.51 228 | 91.72 171 | 91.01 220 | 93.12 171 | 99.49 155 | 99.84 157 |
|
MS-PatchMatch | | | 93.46 122 | 95.91 130 | 90.61 118 | 95.48 66 | 99.31 105 | 95.62 115 | 77.23 179 | 99.42 91 | 81.88 125 | 88.92 152 | 96.06 92 | 93.80 150 | 96.45 128 | 93.11 172 | 99.65 124 | 98.10 214 |
|
UniMVSNet (Re) | | | 90.41 152 | 91.96 161 | 88.59 141 | 85.71 172 | 96.73 164 | 90.82 165 | 84.11 122 | 95.23 177 | 78.54 134 | 88.91 153 | 76.41 170 | 92.84 159 | 93.40 186 | 93.05 173 | 99.55 143 | 100.00 1 |
|
EU-MVSNet | | | 87.20 190 | 90.47 173 | 83.38 206 | 85.11 186 | 93.85 215 | 86.10 213 | 79.76 161 | 93.30 215 | 65.39 201 | 84.41 166 | 78.43 161 | 85.04 221 | 92.20 199 | 93.03 174 | 98.86 220 | 98.05 215 |
|
tfpnnormal | | | 89.09 162 | 89.71 178 | 88.38 143 | 87.37 161 | 96.78 163 | 91.46 161 | 85.20 104 | 90.33 226 | 72.35 153 | 83.45 170 | 69.30 217 | 94.45 142 | 95.29 151 | 92.86 175 | 99.44 173 | 99.93 119 |
|
EG-PatchMatch MVS | | | 86.96 195 | 89.56 183 | 83.93 201 | 86.29 167 | 97.61 150 | 90.75 167 | 73.31 203 | 95.43 175 | 66.08 194 | 75.88 222 | 71.31 195 | 87.55 216 | 94.79 159 | 92.74 176 | 99.61 133 | 99.13 202 |
|
MDTV_nov1_ep13_2view | | | 87.75 178 | 93.32 152 | 81.26 216 | 83.74 216 | 96.64 165 | 85.66 214 | 66.20 222 | 98.36 143 | 61.61 213 | 84.34 167 | 87.95 134 | 91.12 195 | 94.01 170 | 92.66 177 | 99.22 213 | 99.27 198 |
|
pmmvs4 | | | 91.41 147 | 93.05 154 | 89.49 130 | 85.85 171 | 96.52 168 | 91.70 159 | 82.49 135 | 98.14 147 | 83.17 116 | 87.57 160 | 81.76 158 | 94.39 143 | 95.47 147 | 92.62 178 | 99.33 201 | 99.29 197 |
|
GA-MVS | | | 90.38 153 | 94.59 144 | 85.46 176 | 88.30 158 | 98.44 134 | 92.18 154 | 83.30 129 | 97.89 153 | 58.05 222 | 92.86 134 | 84.25 153 | 91.27 190 | 96.65 119 | 92.61 179 | 99.66 123 | 99.43 193 |
|
WR-MVS_H | | | 88.47 164 | 90.55 172 | 86.04 161 | 85.13 184 | 96.07 182 | 89.86 197 | 79.80 160 | 94.37 193 | 72.32 154 | 83.12 172 | 74.44 178 | 89.60 202 | 93.52 183 | 92.40 180 | 99.51 152 | 99.96 101 |
|
anonymousdsp | | | 87.98 171 | 92.38 158 | 82.85 208 | 83.68 217 | 96.79 162 | 90.78 166 | 74.06 197 | 95.29 176 | 57.91 223 | 83.33 171 | 83.12 154 | 91.15 194 | 95.96 141 | 92.37 181 | 99.52 148 | 99.76 168 |
|
WR-MVS | | | 88.23 169 | 90.15 174 | 86.00 163 | 84.39 208 | 95.64 192 | 89.96 193 | 81.80 146 | 94.46 191 | 71.60 159 | 82.10 175 | 74.36 179 | 88.76 210 | 92.48 197 | 92.20 182 | 99.46 165 | 99.83 159 |
|
N_pmnet | | | 87.31 188 | 91.51 165 | 82.41 213 | 85.13 184 | 95.57 195 | 80.59 227 | 81.79 147 | 96.20 167 | 58.52 221 | 78.62 209 | 85.66 141 | 89.36 204 | 94.64 161 | 92.14 183 | 99.08 218 | 97.72 218 |
|
RPSCF | | | 95.86 72 | 96.94 105 | 94.61 74 | 96.52 58 | 98.67 131 | 98.54 53 | 88.43 86 | 99.56 76 | 90.51 68 | 99.39 34 | 98.70 65 | 97.72 98 | 93.77 177 | 92.00 184 | 95.93 231 | 96.50 224 |
|
v1192 | | | 86.93 196 | 89.01 192 | 84.50 187 | 84.46 207 | 95.51 198 | 89.93 195 | 78.65 171 | 93.75 201 | 62.29 211 | 77.19 215 | 70.88 203 | 92.28 164 | 93.84 174 | 91.96 185 | 99.38 196 | 99.90 137 |
|
pmmvs6 | | | 85.75 211 | 86.97 219 | 84.34 191 | 84.88 193 | 95.59 194 | 87.41 209 | 79.19 166 | 87.81 232 | 67.56 176 | 63.05 236 | 77.76 162 | 89.15 205 | 93.45 185 | 91.90 186 | 97.83 227 | 99.21 199 |
|
v10 | | | 87.40 186 | 89.62 180 | 84.80 184 | 84.93 191 | 95.07 207 | 90.44 172 | 75.63 188 | 94.51 186 | 66.52 187 | 78.87 205 | 73.47 183 | 91.86 169 | 93.69 180 | 91.87 187 | 99.45 168 | 99.86 155 |
|
v7 | | | 87.72 179 | 89.75 177 | 85.35 178 | 85.01 188 | 95.79 190 | 90.43 174 | 78.98 168 | 94.50 189 | 66.39 189 | 78.87 205 | 73.65 182 | 91.85 170 | 93.69 180 | 91.86 188 | 99.45 168 | 99.92 124 |
|
v1144 | | | 87.49 181 | 89.64 179 | 84.97 181 | 84.73 200 | 95.84 188 | 90.17 187 | 79.30 164 | 93.96 197 | 64.65 203 | 78.83 207 | 73.38 184 | 91.51 180 | 93.77 177 | 91.77 189 | 99.45 168 | 99.93 119 |
|
v1240 | | | 86.24 208 | 88.56 205 | 83.54 202 | 84.05 214 | 95.21 206 | 89.27 201 | 76.76 183 | 93.42 211 | 60.68 218 | 75.99 221 | 69.80 214 | 91.21 193 | 93.83 176 | 91.76 190 | 99.29 207 | 99.91 136 |
|
v144192 | | | 86.80 198 | 88.90 199 | 84.35 189 | 84.33 209 | 95.56 196 | 89.34 200 | 77.74 176 | 93.60 207 | 64.03 204 | 77.82 212 | 70.76 204 | 91.28 189 | 92.91 194 | 91.74 191 | 99.37 198 | 99.90 137 |
|
v1921920 | | | 86.81 197 | 88.93 197 | 84.33 192 | 84.23 211 | 95.41 202 | 90.09 189 | 78.10 174 | 93.74 203 | 62.17 212 | 76.98 217 | 71.14 200 | 92.05 166 | 93.69 180 | 91.69 192 | 99.32 203 | 99.88 147 |
|
v11 | | | 86.74 199 | 89.01 192 | 84.09 199 | 84.79 198 | 91.79 226 | 90.39 176 | 72.53 209 | 94.47 190 | 65.75 197 | 78.64 208 | 72.96 185 | 91.66 172 | 93.92 172 | 91.69 192 | 99.42 180 | 99.61 179 |
|
MIMVSNet1 | | | 80.64 224 | 83.97 225 | 76.76 224 | 68.91 240 | 91.15 231 | 78.32 231 | 75.47 189 | 89.58 231 | 56.64 226 | 65.10 234 | 65.17 229 | 82.14 223 | 93.51 184 | 91.64 194 | 99.10 217 | 91.66 231 |
|
v2v482 | | | 87.46 183 | 88.90 199 | 85.78 169 | 84.58 205 | 95.95 187 | 89.90 196 | 82.43 139 | 94.19 195 | 65.65 198 | 79.80 195 | 69.12 218 | 92.67 161 | 91.88 201 | 91.46 195 | 99.45 168 | 99.93 119 |
|
v18 | | | 87.14 193 | 88.96 196 | 85.01 180 | 85.57 173 | 92.03 219 | 90.89 164 | 74.62 192 | 94.80 183 | 67.90 172 | 82.02 177 | 71.28 196 | 91.63 176 | 91.53 206 | 91.44 196 | 99.47 162 | 99.60 180 |
|
v1neww | | | 87.88 173 | 89.51 187 | 85.97 165 | 85.32 179 | 96.12 176 | 90.33 179 | 82.17 143 | 94.51 186 | 66.96 183 | 81.84 181 | 71.21 197 | 91.64 174 | 91.52 207 | 91.43 197 | 99.42 180 | 99.92 124 |
|
v7new | | | 87.88 173 | 89.51 187 | 85.97 165 | 85.32 179 | 96.12 176 | 90.33 179 | 82.17 143 | 94.51 186 | 66.96 183 | 81.84 181 | 71.21 197 | 91.64 174 | 91.52 207 | 91.43 197 | 99.42 180 | 99.92 124 |
|
v6 | | | 87.96 172 | 89.58 181 | 86.08 160 | 85.34 178 | 96.14 175 | 90.44 172 | 82.19 142 | 94.56 185 | 67.43 181 | 81.90 179 | 71.57 194 | 91.62 177 | 91.54 205 | 91.43 197 | 99.43 174 | 99.92 124 |
|
v16 | | | 87.15 192 | 89.13 191 | 84.83 183 | 85.55 174 | 91.94 221 | 90.50 169 | 74.13 196 | 95.06 179 | 67.72 174 | 81.84 181 | 72.55 186 | 91.65 173 | 91.50 209 | 91.42 200 | 99.42 180 | 99.60 180 |
|
v1141 | | | 87.45 185 | 88.98 194 | 85.67 173 | 84.86 195 | 96.08 180 | 90.23 185 | 82.46 136 | 93.75 201 | 65.64 200 | 79.57 199 | 70.52 206 | 91.41 185 | 91.63 202 | 91.39 201 | 99.42 180 | 99.92 124 |
|
divwei89l23v2f112 | | | 87.46 183 | 88.97 195 | 85.70 172 | 84.85 196 | 96.08 180 | 90.23 185 | 82.46 136 | 93.69 205 | 65.83 196 | 79.57 199 | 70.54 205 | 91.39 186 | 91.60 203 | 91.39 201 | 99.43 174 | 99.92 124 |
|
v1 | | | 87.48 182 | 88.91 198 | 85.81 168 | 84.93 191 | 96.07 182 | 90.33 179 | 82.45 138 | 93.65 206 | 66.39 189 | 79.38 202 | 70.40 208 | 91.33 187 | 91.58 204 | 91.38 203 | 99.42 180 | 99.93 119 |
|
pmmvs5 | | | 87.33 187 | 90.01 175 | 84.20 195 | 84.31 210 | 96.04 184 | 87.63 208 | 76.59 185 | 93.17 216 | 65.35 202 | 84.30 168 | 71.68 191 | 91.91 168 | 95.41 148 | 91.37 204 | 99.39 194 | 98.13 212 |
|
v15 | | | 86.50 204 | 88.32 208 | 84.37 188 | 85.00 189 | 91.86 222 | 90.30 183 | 73.76 199 | 93.90 199 | 66.28 192 | 79.78 196 | 70.37 209 | 91.45 183 | 91.48 212 | 91.27 205 | 99.43 174 | 99.58 183 |
|
V14 | | | 86.54 203 | 88.41 207 | 84.35 189 | 84.94 190 | 91.83 223 | 90.28 184 | 73.48 201 | 93.73 204 | 66.50 188 | 79.89 194 | 71.12 201 | 91.46 182 | 91.48 212 | 91.25 206 | 99.42 180 | 99.58 183 |
|
V9 | | | 86.42 205 | 88.26 209 | 84.27 193 | 84.88 193 | 91.80 224 | 90.34 178 | 73.18 205 | 93.92 198 | 66.37 191 | 79.68 198 | 70.25 210 | 91.42 184 | 91.43 214 | 91.23 207 | 99.42 180 | 99.55 188 |
|
v8 | | | 87.54 180 | 89.33 190 | 85.45 177 | 85.41 176 | 95.50 199 | 90.32 182 | 78.94 169 | 94.35 194 | 66.93 185 | 81.90 179 | 70.99 202 | 91.62 177 | 91.49 210 | 91.22 208 | 99.48 159 | 99.87 152 |
|
v17 | | | 86.99 194 | 88.90 199 | 84.76 185 | 85.52 175 | 91.96 220 | 90.50 169 | 74.17 193 | 94.88 181 | 67.33 182 | 81.94 178 | 71.21 197 | 91.57 179 | 91.49 210 | 91.20 209 | 99.48 159 | 99.60 180 |
|
v12 | | | 86.32 206 | 88.22 210 | 84.10 197 | 84.76 199 | 91.80 224 | 89.94 194 | 72.97 207 | 93.85 200 | 66.18 193 | 79.98 193 | 69.72 216 | 91.33 187 | 91.40 215 | 91.20 209 | 99.42 180 | 99.56 187 |
|
V42 | | | 87.84 176 | 89.42 189 | 85.99 164 | 85.16 183 | 96.01 185 | 90.52 168 | 81.78 148 | 94.43 192 | 67.59 175 | 81.32 185 | 71.87 190 | 91.48 181 | 91.25 217 | 91.16 211 | 99.43 174 | 99.92 124 |
|
v13 | | | 86.27 207 | 88.16 212 | 84.06 200 | 84.85 196 | 91.77 227 | 90.00 191 | 72.77 208 | 93.56 208 | 66.06 195 | 79.25 203 | 70.50 207 | 91.25 191 | 91.35 216 | 91.15 212 | 99.42 180 | 99.55 188 |
|
test20.03 | | | 83.86 218 | 88.73 204 | 78.16 221 | 82.60 224 | 93.00 216 | 81.61 226 | 74.68 191 | 92.36 220 | 57.50 224 | 83.01 174 | 74.48 177 | 73.30 234 | 92.40 198 | 91.14 213 | 99.29 207 | 94.75 228 |
|
v148 | | | 86.63 202 | 87.79 214 | 85.28 179 | 84.65 203 | 95.97 186 | 86.46 212 | 82.84 134 | 92.91 218 | 71.52 161 | 78.99 204 | 66.74 225 | 86.83 218 | 89.28 226 | 90.69 214 | 99.41 192 | 99.94 116 |
|
v7n | | | 85.39 212 | 87.70 217 | 82.70 209 | 82.77 223 | 95.64 192 | 88.27 206 | 74.83 190 | 92.30 221 | 62.58 210 | 76.37 220 | 64.80 230 | 88.38 212 | 94.29 167 | 90.61 215 | 99.34 199 | 99.87 152 |
|
thisisatest0515 | | | 90.28 155 | 94.32 148 | 85.57 175 | 85.23 182 | 97.23 157 | 85.44 215 | 83.09 131 | 96.80 162 | 72.41 151 | 89.82 147 | 90.87 119 | 87.93 214 | 95.27 153 | 90.39 216 | 99.33 201 | 99.88 147 |
|
SixPastTwentyTwo | | | 88.35 165 | 91.51 165 | 84.66 186 | 85.39 177 | 96.96 161 | 86.57 211 | 79.62 162 | 96.57 165 | 63.73 206 | 87.86 157 | 75.18 173 | 93.43 155 | 94.03 169 | 90.37 217 | 99.24 212 | 99.58 183 |
|
v748 | | | 84.47 215 | 86.06 220 | 82.62 211 | 82.85 222 | 95.02 208 | 83.73 220 | 78.48 172 | 90.20 228 | 67.45 180 | 75.86 223 | 61.27 233 | 83.84 222 | 89.87 224 | 90.28 218 | 99.34 199 | 99.90 137 |
|
v52 | | | 85.80 209 | 87.74 215 | 83.53 203 | 82.87 221 | 95.31 205 | 88.71 203 | 77.04 181 | 92.23 222 | 63.53 207 | 76.91 218 | 69.80 214 | 89.78 199 | 90.05 223 | 90.07 219 | 99.26 211 | 99.82 160 |
|
V4 | | | 85.78 210 | 87.74 215 | 83.50 204 | 82.90 220 | 95.33 204 | 88.62 204 | 77.05 180 | 92.14 224 | 63.45 208 | 76.91 218 | 69.85 213 | 89.72 200 | 90.07 222 | 90.05 220 | 99.27 210 | 99.81 161 |
|
TDRefinement | | | 87.79 177 | 88.76 203 | 86.66 157 | 93.54 82 | 98.02 143 | 95.76 110 | 85.18 105 | 96.57 165 | 67.90 172 | 80.51 190 | 66.51 226 | 78.37 228 | 93.20 189 | 89.73 221 | 99.22 213 | 96.75 221 |
|
new_pmnet | | | 84.12 217 | 87.89 213 | 79.72 218 | 80.43 226 | 94.14 214 | 80.26 228 | 74.14 195 | 96.01 169 | 56.30 227 | 74.94 224 | 76.45 169 | 88.59 211 | 93.11 192 | 89.31 222 | 98.59 223 | 91.27 232 |
|
pmmvs-eth3d | | | 82.92 220 | 83.31 226 | 82.47 212 | 76.97 230 | 91.76 228 | 83.79 218 | 76.10 187 | 90.33 226 | 69.95 167 | 71.04 229 | 48.09 237 | 89.02 207 | 93.85 173 | 89.14 223 | 99.02 219 | 98.96 204 |
|
pmmvs3 | | | 80.91 223 | 85.62 221 | 75.42 225 | 75.01 232 | 89.09 234 | 75.31 233 | 68.70 213 | 86.99 233 | 46.74 237 | 81.18 187 | 62.91 232 | 87.95 213 | 93.84 174 | 89.06 224 | 98.80 222 | 96.23 227 |
|
PM-MVS | | | 82.79 221 | 84.51 224 | 80.77 217 | 77.22 229 | 92.13 218 | 83.61 221 | 73.31 203 | 93.50 209 | 61.06 214 | 77.15 216 | 46.52 240 | 90.55 198 | 94.14 168 | 89.05 225 | 98.85 221 | 99.12 203 |
|
1111 | | | 73.79 228 | 78.62 230 | 68.16 231 | 69.34 237 | 81.48 238 | 59.42 243 | 52.46 243 | 78.55 240 | 50.42 230 | 62.43 237 | 71.67 192 | 80.43 226 | 86.79 228 | 88.22 226 | 96.87 228 | 81.17 242 |
|
test12356 | | | 69.94 233 | 75.85 233 | 63.04 234 | 70.04 236 | 79.32 243 | 61.62 241 | 65.84 225 | 80.56 238 | 36.30 243 | 71.45 228 | 39.38 243 | 48.79 245 | 83.64 234 | 88.02 227 | 95.64 234 | 88.56 238 |
|
testmv | | | 71.50 229 | 77.62 231 | 64.36 232 | 72.64 233 | 81.28 240 | 59.32 245 | 66.24 220 | 83.91 236 | 35.02 244 | 69.74 230 | 46.18 241 | 57.12 239 | 85.60 232 | 87.48 228 | 95.84 232 | 89.16 235 |
|
test1235678 | | | 71.50 229 | 77.61 232 | 64.36 232 | 72.64 233 | 81.26 241 | 59.31 246 | 66.22 221 | 83.90 237 | 35.02 244 | 69.74 230 | 46.18 241 | 57.12 239 | 85.60 232 | 87.47 229 | 95.84 232 | 89.15 236 |
|
new-patchmatchnet | | | 78.17 226 | 80.82 228 | 75.07 226 | 76.93 231 | 91.20 230 | 71.90 235 | 73.32 202 | 86.59 234 | 48.91 232 | 67.11 232 | 47.85 239 | 81.19 224 | 88.18 227 | 87.02 230 | 98.19 225 | 97.79 217 |
|
MDA-MVSNet-bldmvs | | | 80.30 225 | 82.83 227 | 77.34 222 | 69.16 239 | 94.29 211 | 72.16 234 | 81.97 145 | 90.14 229 | 57.32 225 | 94.01 125 | 47.97 238 | 86.81 219 | 68.74 240 | 86.82 231 | 96.63 229 | 97.86 216 |
|
ambc | | | | 74.33 235 | | 66.84 242 | 84.26 237 | 84.17 216 | | 93.39 213 | 58.99 220 | 45.93 242 | 18.06 251 | 70.61 235 | 93.94 171 | 86.62 232 | 92.61 239 | 98.13 212 |
|
Gipuma | | | 71.02 231 | 72.60 237 | 69.19 230 | 71.31 235 | 75.11 244 | 66.36 238 | 61.65 238 | 94.93 180 | 47.29 236 | 38.74 243 | 38.52 244 | 75.52 229 | 86.09 231 | 85.92 233 | 93.01 237 | 88.87 237 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
CMPMVS | | 65.66 17 | 84.62 214 | 85.02 223 | 84.15 196 | 95.40 67 | 97.79 148 | 88.35 205 | 79.22 165 | 89.66 230 | 60.71 217 | 72.20 226 | 73.94 180 | 87.32 217 | 86.73 230 | 84.55 234 | 93.90 236 | 90.31 233 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
PMMVS2 | | | 65.18 235 | 68.25 238 | 61.59 235 | 61.37 243 | 79.72 242 | 59.18 247 | 61.80 236 | 64.72 244 | 37.33 241 | 53.82 240 | 35.59 245 | 54.46 243 | 73.94 238 | 80.52 235 | 95.40 235 | 89.43 234 |
|
.test1245 | | | 70.78 232 | 79.90 229 | 60.13 237 | 69.34 237 | 81.48 238 | 59.42 243 | 52.46 243 | 78.55 240 | 50.42 230 | 62.43 237 | 71.67 192 | 80.43 226 | 86.79 228 | 78.71 236 | 48.74 247 | 99.65 175 |
|
testmvs | | | 61.76 237 | 72.90 236 | 48.76 241 | 21.21 248 | 68.61 246 | 66.11 240 | 37.38 245 | 94.83 182 | 33.06 246 | 64.31 235 | 29.72 246 | 86.08 220 | 74.44 237 | 78.71 236 | 48.74 247 | 99.65 175 |
|
MVE | | 58.81 19 | 52.07 241 | 55.15 243 | 48.48 242 | 42.45 247 | 62.35 247 | 36.41 251 | 54.70 242 | 49.88 247 | 27.65 247 | 29.98 247 | 18.08 250 | 54.87 242 | 65.93 242 | 77.26 238 | 74.79 246 | 82.59 239 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
tmp_tt | | | | | 78.81 219 | 98.80 42 | 85.73 235 | 70.08 236 | 77.87 175 | 98.68 122 | 83.71 113 | 99.53 28 | 74.55 176 | 54.97 241 | 78.28 236 | 72.43 239 | 87.45 240 | |
|
no-one | | | 52.34 240 | 53.36 244 | 51.14 240 | 57.63 246 | 69.39 245 | 35.07 252 | 61.58 239 | 44.14 248 | 37.06 242 | 34.80 246 | 26.36 249 | 32.65 246 | 50.68 245 | 70.83 240 | 82.88 243 | 77.30 244 |
|
E-PMN | | | 55.33 238 | 55.79 241 | 54.81 239 | 59.81 245 | 57.23 249 | 38.83 249 | 63.59 233 | 64.06 246 | 24.66 248 | 35.33 245 | 26.40 248 | 58.69 238 | 55.41 243 | 70.54 241 | 83.26 242 | 81.56 241 |
|
test123 | | | 48.14 242 | 58.11 240 | 36.51 243 | 8.71 249 | 56.81 250 | 59.55 242 | 24.08 246 | 77.50 242 | 14.41 250 | 49.20 241 | 11.94 252 | 80.98 225 | 41.62 246 | 69.81 242 | 31.32 249 | 99.90 137 |
|
EMVS | | | 55.14 239 | 55.29 242 | 54.97 238 | 60.87 244 | 57.52 248 | 38.58 250 | 63.57 234 | 64.54 245 | 23.36 249 | 36.96 244 | 27.99 247 | 60.69 237 | 51.17 244 | 66.61 243 | 82.73 244 | 82.25 240 |
|
FPMVS | | | 73.80 227 | 74.62 234 | 72.84 229 | 83.09 219 | 84.44 236 | 83.89 217 | 73.64 200 | 92.20 223 | 48.50 233 | 72.19 227 | 59.51 234 | 63.16 236 | 69.13 239 | 66.26 244 | 84.74 241 | 78.59 243 |
|
PMVS | | 60.14 18 | 62.67 236 | 64.05 239 | 61.06 236 | 68.32 241 | 53.27 251 | 52.23 248 | 67.63 216 | 75.07 243 | 48.30 234 | 58.27 239 | 57.43 236 | 49.99 244 | 67.20 241 | 62.42 245 | 79.87 245 | 74.68 245 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
sosnet-low-res | | | 0.00 243 | 0.00 245 | 0.00 245 | 0.00 251 | 0.00 252 | 0.00 253 | 0.00 248 | 0.00 249 | 0.00 252 | 0.00 248 | 0.00 253 | 0.00 248 | 0.00 247 | 0.00 246 | 0.00 250 | 0.00 246 |
|
sosnet | | | 0.00 243 | 0.00 245 | 0.00 245 | 0.00 251 | 0.00 252 | 0.00 253 | 0.00 248 | 0.00 249 | 0.00 252 | 0.00 248 | 0.00 253 | 0.00 248 | 0.00 247 | 0.00 246 | 0.00 250 | 0.00 246 |
|
our_test_3 | | | | | | 85.89 170 | 96.09 179 | 82.15 225 | | | | | | | | | | |
|
MTAPA | | | | | | | | | | | 96.61 12 | | 100.00 1 | | | | | |
|
MTMP | | | | | | | | | | | 97.42 7 | | 100.00 1 | | | | | |
|
Patchmatch-RL test | | | | | | | | 68.01 237 | | | | | | | | | | |
|
XVS | | | | | | 95.09 70 | 99.94 17 | 97.49 72 | | | 88.58 87 | | 99.98 29 | | | | 99.78 63 | |
|
X-MVStestdata | | | | | | 95.09 70 | 99.94 17 | 97.49 72 | | | 88.58 87 | | 99.98 29 | | | | 99.78 63 | |
|
abl_6 | | | | | 97.06 33 | 99.17 36 | 99.82 57 | 98.68 50 | 90.86 48 | 100.00 1 | 94.53 29 | 97.40 89 | 100.00 1 | 99.17 51 | | | 99.93 18 | 99.99 49 |
|
mPP-MVS | | | | | | 99.23 34 | | | | | | | 99.87 40 | | | | | |
|
NP-MVS | | | | | | | | | | 99.79 54 | | | | | | | | |
|
Patchmtry | | | | | | | 99.00 118 | 95.46 119 | 65.50 226 | | 67.51 177 | | | | | | | |
|
DeepMVS_CX | | | | | | | 97.31 155 | 79.48 229 | 89.65 60 | 98.66 124 | 60.89 216 | 94.40 120 | 66.89 223 | 87.65 215 | 81.69 235 | | 92.76 238 | 94.24 230 |
|