SMA-MVS | | | 94.57 5 | 95.23 4 | 93.80 9 | 97.56 2 | 97.61 5 | 95.92 13 | 92.02 8 | 94.43 3 | 89.74 15 | 92.16 18 | 92.63 9 | 91.78 16 | 95.98 1 | 95.57 1 | 95.80 27 | 98.25 1 |
|
ESAPD | | | 95.35 1 | 95.97 1 | 94.63 2 | 97.35 6 | 97.95 1 | 97.09 2 | 93.48 1 | 93.91 9 | 90.13 11 | 96.41 2 | 95.14 1 | 92.88 4 | 95.64 3 | 94.53 8 | 96.86 2 | 98.21 2 |
|
APDe-MVS | | | 95.23 2 | 95.69 2 | 94.70 1 | 97.12 11 | 97.81 3 | 97.19 1 | 92.83 2 | 95.06 2 | 90.98 5 | 96.47 1 | 92.77 8 | 93.38 1 | 95.34 6 | 94.21 12 | 96.68 4 | 98.17 3 |
|
SD-MVS | | | 94.53 6 | 95.22 5 | 93.73 11 | 95.69 31 | 97.03 10 | 95.77 17 | 91.95 9 | 94.41 4 | 91.35 4 | 94.97 4 | 93.34 5 | 91.80 15 | 94.72 16 | 93.99 16 | 95.82 26 | 98.07 4 |
|
TSAR-MVS + MP. | | | 94.48 7 | 94.97 6 | 93.90 8 | 95.53 32 | 97.01 11 | 96.69 3 | 90.71 18 | 94.24 5 | 90.92 6 | 94.97 4 | 92.19 11 | 93.03 2 | 94.83 12 | 93.60 22 | 96.51 7 | 97.97 5 |
|
HSP-MVS | | | 94.83 3 | 95.37 3 | 94.21 5 | 96.82 20 | 97.94 2 | 96.69 3 | 92.37 7 | 93.97 8 | 90.29 9 | 96.16 3 | 93.71 3 | 92.70 5 | 94.80 13 | 93.13 32 | 96.37 8 | 97.90 6 |
|
CSCG | | | 92.76 22 | 93.16 24 | 92.29 25 | 96.30 23 | 97.74 4 | 94.67 28 | 88.98 30 | 92.46 19 | 89.73 16 | 86.67 33 | 92.15 12 | 88.69 36 | 92.26 46 | 92.92 36 | 95.40 46 | 97.89 7 |
|
SteuartSystems-ACMMP | | | 94.06 10 | 94.65 8 | 93.38 15 | 96.97 16 | 97.36 6 | 96.12 7 | 91.78 10 | 92.05 24 | 87.34 26 | 94.42 8 | 90.87 19 | 91.87 14 | 95.47 5 | 94.59 7 | 96.21 13 | 97.77 8 |
Skip Steuart: Steuart Systems R&D Blog. |
TSAR-MVS + ACMM | | | 92.97 20 | 94.51 10 | 91.16 32 | 95.88 29 | 96.59 26 | 95.09 24 | 90.45 24 | 93.42 13 | 83.01 48 | 94.68 6 | 90.74 21 | 88.74 35 | 94.75 14 | 93.78 20 | 93.82 140 | 97.63 9 |
|
DeepPCF-MVS | | 88.51 2 | 92.64 25 | 94.42 13 | 90.56 36 | 94.84 38 | 96.92 14 | 91.31 56 | 89.61 26 | 95.16 1 | 84.55 41 | 89.91 25 | 91.45 16 | 90.15 29 | 95.12 8 | 94.81 5 | 92.90 161 | 97.58 10 |
|
HPM-MVS++ | | | 94.60 4 | 94.91 7 | 94.24 4 | 97.86 1 | 96.53 28 | 96.14 6 | 92.51 4 | 93.87 11 | 90.76 7 | 93.45 13 | 93.84 2 | 92.62 6 | 95.11 9 | 94.08 15 | 95.58 40 | 97.48 11 |
|
ACMMP_Plus | | | 93.94 12 | 94.49 11 | 93.30 16 | 97.03 14 | 97.31 7 | 95.96 9 | 91.30 14 | 93.41 14 | 88.55 20 | 93.00 14 | 90.33 22 | 91.43 22 | 95.53 4 | 94.41 10 | 95.53 42 | 97.47 12 |
|
CNVR-MVS | | | 94.37 8 | 94.65 8 | 94.04 7 | 97.29 7 | 97.11 8 | 96.00 8 | 92.43 6 | 93.45 12 | 89.85 14 | 90.92 21 | 93.04 6 | 92.59 7 | 95.77 2 | 94.82 4 | 96.11 15 | 97.42 13 |
|
APD-MVS | | | 94.37 8 | 94.47 12 | 94.26 3 | 97.18 9 | 96.99 12 | 96.53 5 | 92.68 3 | 92.45 20 | 89.96 12 | 94.53 7 | 91.63 15 | 92.89 3 | 94.58 18 | 93.82 19 | 96.31 11 | 97.26 14 |
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023 |
DeepC-MVS | | 87.86 3 | 92.26 27 | 91.86 30 | 92.73 21 | 96.18 24 | 96.87 15 | 95.19 23 | 91.76 11 | 92.17 23 | 86.58 31 | 81.79 44 | 85.85 44 | 90.88 25 | 94.57 19 | 94.61 6 | 95.80 27 | 97.18 15 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
MP-MVS | | | 93.35 17 | 93.59 20 | 93.08 19 | 97.39 4 | 96.82 18 | 95.38 20 | 90.71 18 | 90.82 31 | 88.07 23 | 92.83 16 | 90.29 23 | 91.32 23 | 94.03 23 | 93.19 31 | 95.61 38 | 97.16 16 |
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo. |
TSAR-MVS + GP. | | | 92.71 24 | 93.91 17 | 91.30 30 | 91.96 66 | 96.00 37 | 93.43 36 | 87.94 35 | 92.53 18 | 86.27 36 | 93.57 11 | 91.94 13 | 91.44 21 | 93.29 33 | 92.89 37 | 96.78 3 | 97.15 17 |
|
MCST-MVS | | | 93.81 13 | 94.06 15 | 93.53 13 | 96.79 21 | 96.85 16 | 95.95 10 | 91.69 12 | 92.20 22 | 87.17 28 | 90.83 23 | 93.41 4 | 91.96 12 | 94.49 20 | 93.50 25 | 97.61 1 | 97.12 18 |
|
HFP-MVS | | | 94.02 11 | 94.22 14 | 93.78 10 | 97.25 8 | 96.85 16 | 95.81 15 | 90.94 17 | 94.12 6 | 90.29 9 | 94.09 10 | 89.98 25 | 92.52 8 | 93.94 26 | 93.49 27 | 95.87 21 | 97.10 19 |
|
CP-MVS | | | 93.25 18 | 93.26 23 | 93.24 17 | 96.84 19 | 96.51 29 | 95.52 19 | 90.61 21 | 92.37 21 | 88.88 18 | 90.91 22 | 89.52 28 | 91.91 13 | 93.64 30 | 92.78 38 | 95.69 33 | 97.09 20 |
|
canonicalmvs | | | 89.36 44 | 89.92 39 | 88.70 53 | 91.38 67 | 95.92 39 | 91.81 52 | 82.61 86 | 90.37 35 | 82.73 51 | 82.09 42 | 79.28 75 | 88.30 40 | 91.17 60 | 93.59 23 | 95.36 49 | 97.04 21 |
|
NCCC | | | 93.69 16 | 93.66 19 | 93.72 12 | 97.37 5 | 96.66 25 | 95.93 12 | 92.50 5 | 93.40 15 | 88.35 21 | 87.36 31 | 92.33 10 | 92.18 10 | 94.89 11 | 94.09 14 | 96.00 16 | 96.91 22 |
|
zzz-MVS | | | 93.80 14 | 93.45 22 | 94.20 6 | 97.53 3 | 96.43 32 | 95.88 14 | 91.12 16 | 94.09 7 | 92.74 3 | 87.68 29 | 90.77 20 | 92.04 11 | 94.74 15 | 93.56 24 | 95.91 19 | 96.85 23 |
|
3Dnovator+ | | 86.06 4 | 91.60 31 | 90.86 37 | 92.47 23 | 96.00 28 | 96.50 31 | 94.70 27 | 87.83 37 | 90.49 34 | 89.92 13 | 74.68 74 | 89.35 30 | 90.66 26 | 94.02 24 | 94.14 13 | 95.67 35 | 96.85 23 |
|
X-MVS | | | 92.36 26 | 92.75 27 | 91.90 28 | 96.89 17 | 96.70 21 | 95.25 22 | 90.48 23 | 91.50 29 | 83.95 43 | 88.20 27 | 88.82 34 | 89.11 32 | 93.75 29 | 93.43 28 | 95.75 32 | 96.83 25 |
|
MSLP-MVS++ | | | 92.02 30 | 91.40 32 | 92.75 20 | 96.01 27 | 95.88 40 | 93.73 35 | 89.00 28 | 89.89 39 | 90.31 8 | 81.28 49 | 88.85 33 | 91.45 19 | 92.88 40 | 94.24 11 | 96.00 16 | 96.76 26 |
|
train_agg | | | 92.87 21 | 93.53 21 | 92.09 26 | 96.88 18 | 95.38 44 | 95.94 11 | 90.59 22 | 90.65 33 | 83.65 46 | 94.31 9 | 91.87 14 | 90.30 27 | 93.38 32 | 92.42 39 | 95.17 58 | 96.73 27 |
|
ACMMPR | | | 93.72 15 | 93.94 16 | 93.48 14 | 97.07 12 | 96.93 13 | 95.78 16 | 90.66 20 | 93.88 10 | 89.24 17 | 93.53 12 | 89.08 32 | 92.24 9 | 93.89 28 | 93.50 25 | 95.88 20 | 96.73 27 |
|
PVSNet_Blended_VisFu | | | 87.40 60 | 87.80 56 | 86.92 64 | 92.86 58 | 95.40 43 | 88.56 87 | 83.45 72 | 79.55 88 | 82.26 52 | 74.49 75 | 84.03 51 | 79.24 129 | 92.97 39 | 91.53 46 | 95.15 60 | 96.65 29 |
|
DeepC-MVS_fast | | 88.76 1 | 93.10 19 | 93.02 26 | 93.19 18 | 97.13 10 | 96.51 29 | 95.35 21 | 91.19 15 | 93.14 17 | 88.14 22 | 85.26 37 | 89.49 29 | 91.45 19 | 95.17 7 | 95.07 2 | 95.85 24 | 96.48 30 |
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020 |
CPTT-MVS | | | 91.39 32 | 90.95 35 | 91.91 27 | 95.06 33 | 95.24 46 | 95.02 25 | 88.98 30 | 91.02 30 | 86.71 30 | 84.89 39 | 88.58 37 | 91.60 18 | 90.82 76 | 89.67 79 | 94.08 117 | 96.45 31 |
|
ACMMP | | | 92.03 29 | 92.16 28 | 91.87 29 | 95.88 29 | 96.55 27 | 94.47 30 | 89.49 27 | 91.71 27 | 85.26 37 | 91.52 20 | 84.48 49 | 90.21 28 | 92.82 41 | 91.63 45 | 95.92 18 | 96.42 32 |
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence |
PGM-MVS | | | 92.76 22 | 93.03 25 | 92.45 24 | 97.03 14 | 96.67 24 | 95.73 18 | 87.92 36 | 90.15 38 | 86.53 32 | 92.97 15 | 88.33 38 | 91.69 17 | 93.62 31 | 93.03 33 | 95.83 25 | 96.41 33 |
|
QAPM | | | 89.49 43 | 89.58 43 | 89.38 47 | 94.73 39 | 95.94 38 | 92.35 44 | 85.00 54 | 85.69 55 | 80.03 62 | 76.97 66 | 87.81 40 | 87.87 43 | 92.18 50 | 92.10 41 | 96.33 9 | 96.40 34 |
|
HQP-MVS | | | 89.13 45 | 89.58 43 | 88.60 55 | 93.53 50 | 93.67 64 | 93.29 38 | 87.58 39 | 88.53 44 | 75.50 77 | 87.60 30 | 80.32 66 | 87.07 53 | 90.66 82 | 89.95 72 | 94.62 93 | 96.35 35 |
|
PHI-MVS | | | 92.05 28 | 93.74 18 | 90.08 39 | 94.96 35 | 97.06 9 | 93.11 40 | 87.71 38 | 90.71 32 | 80.78 58 | 92.40 17 | 91.03 17 | 87.68 46 | 94.32 22 | 94.48 9 | 96.21 13 | 96.16 36 |
|
MVS_0304 | | | 90.88 35 | 91.35 33 | 90.34 37 | 93.91 46 | 96.79 19 | 94.49 29 | 86.54 44 | 86.57 50 | 82.85 49 | 81.68 47 | 89.70 27 | 87.57 48 | 94.64 17 | 93.93 17 | 96.67 5 | 96.15 37 |
|
UGNet | | | 85.90 68 | 88.23 51 | 83.18 99 | 88.96 104 | 94.10 59 | 87.52 97 | 83.60 65 | 81.66 69 | 77.90 72 | 80.76 51 | 83.19 55 | 66.70 196 | 91.13 71 | 90.71 58 | 94.39 108 | 96.06 38 |
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 |
CANet | | | 91.33 33 | 91.46 31 | 91.18 31 | 95.01 34 | 96.71 20 | 93.77 33 | 87.39 40 | 87.72 46 | 87.26 27 | 81.77 45 | 89.73 26 | 87.32 51 | 94.43 21 | 93.86 18 | 96.31 11 | 96.02 39 |
|
DELS-MVS | | | 89.71 41 | 89.68 42 | 89.74 42 | 93.75 48 | 96.22 34 | 93.76 34 | 85.84 47 | 82.53 63 | 85.05 39 | 78.96 57 | 84.24 50 | 84.25 65 | 94.91 10 | 94.91 3 | 95.78 31 | 96.02 39 |
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023 |
CDPH-MVS | | | 91.14 34 | 92.01 29 | 90.11 38 | 96.18 24 | 96.18 35 | 94.89 26 | 88.80 32 | 88.76 43 | 77.88 73 | 89.18 26 | 87.71 41 | 87.29 52 | 93.13 35 | 93.31 30 | 95.62 37 | 95.84 41 |
|
LGP-MVS_train | | | 88.25 54 | 88.55 47 | 87.89 59 | 92.84 60 | 93.66 65 | 93.35 37 | 85.22 53 | 85.77 53 | 74.03 84 | 86.60 34 | 76.29 84 | 86.62 56 | 91.20 58 | 90.58 61 | 95.29 54 | 95.75 42 |
|
3Dnovator | | 85.17 5 | 90.48 37 | 89.90 41 | 91.16 32 | 94.88 37 | 95.74 41 | 93.82 32 | 85.36 51 | 89.28 40 | 87.81 24 | 74.34 76 | 87.40 42 | 88.56 37 | 93.07 36 | 93.74 21 | 96.53 6 | 95.71 43 |
|
MVS_111021_HR | | | 90.56 36 | 91.29 34 | 89.70 44 | 94.71 40 | 95.63 42 | 91.81 52 | 86.38 45 | 87.53 47 | 81.29 55 | 87.96 28 | 85.43 46 | 87.69 45 | 93.90 27 | 92.93 35 | 96.33 9 | 95.69 44 |
|
anonymousdsp | | | 77.94 163 | 79.00 131 | 76.71 179 | 79.03 207 | 87.83 160 | 79.58 190 | 72.87 184 | 65.80 193 | 58.86 196 | 65.82 126 | 62.48 147 | 75.99 147 | 86.77 127 | 88.66 100 | 93.92 129 | 95.68 45 |
|
PCF-MVS | | 84.60 6 | 88.66 47 | 87.75 59 | 89.73 43 | 93.06 56 | 96.02 36 | 93.22 39 | 90.00 25 | 82.44 65 | 80.02 63 | 77.96 61 | 85.16 47 | 87.36 50 | 88.54 106 | 88.54 102 | 94.72 85 | 95.61 46 |
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019 |
EPNet | | | 89.60 42 | 89.91 40 | 89.24 49 | 96.45 22 | 93.61 66 | 92.95 42 | 88.03 34 | 85.74 54 | 83.36 47 | 87.29 32 | 83.05 56 | 80.98 79 | 92.22 47 | 91.85 43 | 93.69 147 | 95.58 47 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
EPP-MVSNet | | | 86.55 62 | 87.76 58 | 85.15 69 | 90.52 80 | 94.41 56 | 87.24 106 | 82.32 91 | 81.79 68 | 73.60 86 | 78.57 59 | 82.41 58 | 82.07 72 | 91.23 56 | 90.39 63 | 95.14 61 | 95.48 48 |
|
abl_6 | | | | | 90.66 35 | 94.65 41 | 96.27 33 | 92.21 45 | 86.94 42 | 90.23 36 | 86.38 33 | 85.50 36 | 92.96 7 | 88.37 39 | | | 95.40 46 | 95.46 49 |
|
ACMP | | 83.90 8 | 88.32 53 | 88.06 53 | 88.62 54 | 92.18 64 | 93.98 62 | 91.28 57 | 85.24 52 | 86.69 49 | 81.23 56 | 85.62 35 | 75.13 87 | 87.01 54 | 89.83 90 | 89.77 77 | 94.79 79 | 95.43 50 |
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020 |
CLD-MVS | | | 88.66 47 | 88.52 48 | 88.82 51 | 91.37 68 | 94.22 58 | 92.82 43 | 82.08 94 | 88.27 45 | 85.14 38 | 81.86 43 | 78.53 77 | 85.93 59 | 91.17 60 | 90.61 59 | 95.55 41 | 95.00 51 |
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020 |
IS_MVSNet | | | 86.18 64 | 88.18 52 | 83.85 91 | 91.02 71 | 94.72 55 | 87.48 98 | 82.46 88 | 81.05 75 | 70.28 96 | 76.98 65 | 82.20 60 | 76.65 144 | 93.97 25 | 93.38 29 | 95.18 57 | 94.97 52 |
|
UniMVSNet (Re) | | | 81.22 109 | 81.08 101 | 81.39 126 | 85.35 136 | 91.76 93 | 84.93 152 | 82.88 74 | 76.13 107 | 65.02 146 | 64.94 139 | 63.09 137 | 75.17 151 | 87.71 113 | 89.04 95 | 94.97 67 | 94.88 53 |
|
PVSNet_BlendedMVS | | | 88.19 55 | 88.00 54 | 88.42 56 | 92.71 62 | 94.82 53 | 89.08 75 | 83.81 62 | 84.91 57 | 86.38 33 | 79.14 55 | 78.11 78 | 82.66 68 | 93.05 37 | 91.10 48 | 95.86 22 | 94.86 54 |
|
PVSNet_Blended | | | 88.19 55 | 88.00 54 | 88.42 56 | 92.71 62 | 94.82 53 | 89.08 75 | 83.81 62 | 84.91 57 | 86.38 33 | 79.14 55 | 78.11 78 | 82.66 68 | 93.05 37 | 91.10 48 | 95.86 22 | 94.86 54 |
|
OpenMVS | | 82.53 11 | 87.71 57 | 86.84 63 | 88.73 52 | 94.42 42 | 95.06 49 | 91.02 58 | 83.49 69 | 82.50 64 | 82.24 53 | 67.62 119 | 85.48 45 | 85.56 60 | 91.19 59 | 91.30 47 | 95.67 35 | 94.75 56 |
|
IB-MVS | | 79.09 12 | 82.60 90 | 82.19 87 | 83.07 100 | 91.08 70 | 93.55 67 | 80.90 185 | 81.35 101 | 76.56 104 | 80.87 57 | 64.81 141 | 69.97 107 | 68.87 186 | 85.64 146 | 90.06 68 | 95.36 49 | 94.74 57 |
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 |
MVS_111021_LR | | | 90.14 40 | 90.89 36 | 89.26 48 | 93.23 53 | 94.05 61 | 90.43 60 | 84.65 56 | 90.16 37 | 84.52 42 | 90.14 24 | 83.80 53 | 87.99 42 | 92.50 44 | 90.92 53 | 94.74 83 | 94.70 58 |
|
FC-MVSNet-train | | | 85.18 72 | 85.31 72 | 85.03 70 | 90.67 75 | 91.62 95 | 87.66 95 | 83.61 64 | 79.75 85 | 74.37 83 | 78.69 58 | 71.21 104 | 78.91 131 | 91.23 56 | 89.96 71 | 94.96 68 | 94.69 59 |
|
Vis-MVSNet | | | 84.38 76 | 86.68 66 | 81.70 119 | 87.65 115 | 94.89 51 | 88.14 89 | 80.90 105 | 74.48 123 | 68.23 115 | 77.53 63 | 80.72 64 | 69.98 182 | 92.68 42 | 91.90 42 | 95.33 52 | 94.58 60 |
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020 |
UniMVSNet_NR-MVSNet | | | 81.87 99 | 81.33 97 | 82.50 104 | 85.31 137 | 91.30 98 | 85.70 139 | 84.25 58 | 75.89 108 | 64.21 150 | 66.95 122 | 64.65 126 | 80.22 99 | 87.07 119 | 89.18 92 | 95.27 56 | 94.29 61 |
|
DU-MVS | | | 81.20 110 | 80.30 116 | 82.25 106 | 84.98 144 | 90.94 104 | 85.70 139 | 83.58 67 | 75.74 110 | 64.21 150 | 65.30 133 | 59.60 180 | 80.22 99 | 86.89 123 | 89.31 87 | 94.77 81 | 94.29 61 |
|
Effi-MVS+ | | | 85.33 71 | 85.08 73 | 85.63 68 | 89.69 102 | 93.42 69 | 89.90 64 | 80.31 111 | 79.32 89 | 72.48 92 | 73.52 86 | 74.03 91 | 86.55 57 | 90.99 73 | 89.98 70 | 94.83 77 | 94.27 63 |
|
MAR-MVS | | | 88.39 52 | 88.44 49 | 88.33 58 | 94.90 36 | 95.06 49 | 90.51 59 | 83.59 66 | 85.27 56 | 79.07 66 | 77.13 64 | 82.89 57 | 87.70 44 | 92.19 49 | 92.32 40 | 94.23 112 | 94.20 64 |
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 |
DI_MVS_plusplus_trai | | | 86.41 63 | 85.54 71 | 87.42 62 | 89.24 103 | 93.13 74 | 92.16 47 | 82.65 84 | 82.30 66 | 80.75 59 | 68.30 115 | 80.41 65 | 85.01 62 | 90.56 83 | 90.07 67 | 94.70 87 | 94.01 65 |
|
OMC-MVS | | | 90.23 39 | 90.40 38 | 90.03 40 | 93.45 51 | 95.29 45 | 91.89 51 | 86.34 46 | 93.25 16 | 84.94 40 | 81.72 46 | 86.65 43 | 88.90 33 | 91.69 53 | 90.27 64 | 94.65 90 | 93.95 66 |
|
Anonymous20240521 | | | 77.55 167 | 78.71 134 | 76.20 185 | 82.88 185 | 89.76 131 | 77.36 202 | 79.11 132 | 68.90 176 | 60.75 183 | 61.50 152 | 62.59 143 | 63.06 202 | 86.52 133 | 87.81 114 | 94.06 119 | 93.89 67 |
|
NR-MVSNet | | | 80.25 123 | 79.98 121 | 80.56 145 | 85.20 139 | 90.94 104 | 85.65 141 | 83.58 67 | 75.74 110 | 61.36 178 | 65.30 133 | 56.75 196 | 72.38 170 | 88.46 107 | 88.80 99 | 95.16 59 | 93.87 68 |
|
CNLPA | | | 88.40 50 | 87.00 62 | 90.03 40 | 93.73 49 | 94.28 57 | 89.56 69 | 85.81 48 | 91.87 25 | 87.55 25 | 69.53 108 | 81.49 61 | 89.23 31 | 89.45 95 | 88.59 101 | 94.31 111 | 93.82 69 |
|
OPM-MVS | | | 87.56 59 | 85.80 70 | 89.62 45 | 93.90 47 | 94.09 60 | 94.12 31 | 88.18 33 | 75.40 113 | 77.30 76 | 76.41 67 | 77.93 80 | 88.79 34 | 92.20 48 | 90.82 54 | 95.40 46 | 93.72 70 |
|
AdaColmap | | | 90.29 38 | 88.38 50 | 92.53 22 | 96.10 26 | 95.19 47 | 92.98 41 | 91.40 13 | 89.08 42 | 88.65 19 | 78.35 60 | 81.44 62 | 91.30 24 | 90.81 77 | 90.21 65 | 94.72 85 | 93.59 71 |
|
MVS_Test | | | 86.93 61 | 87.24 61 | 86.56 65 | 90.10 95 | 93.47 68 | 90.31 61 | 80.12 113 | 83.55 61 | 78.12 69 | 79.58 54 | 79.80 70 | 85.45 61 | 90.17 86 | 90.59 60 | 95.29 54 | 93.53 72 |
|
TranMVSNet+NR-MVSNet | | | 80.52 119 | 79.84 123 | 81.33 129 | 84.92 146 | 90.39 111 | 85.53 144 | 84.22 60 | 74.27 126 | 60.68 184 | 64.93 140 | 59.96 175 | 77.48 140 | 86.75 128 | 89.28 88 | 95.12 63 | 93.29 73 |
|
conf0.05thres1000 | | | 81.00 113 | 79.12 130 | 83.20 98 | 90.14 94 | 92.15 90 | 87.05 118 | 82.09 93 | 68.11 182 | 66.19 129 | 59.67 182 | 61.10 167 | 79.05 130 | 90.47 85 | 89.11 93 | 94.68 88 | 93.22 74 |
|
CP-MVSNet | | | 76.36 184 | 76.41 177 | 76.32 183 | 82.73 189 | 88.64 150 | 79.39 191 | 79.62 124 | 67.21 183 | 53.70 201 | 60.72 168 | 55.22 204 | 67.91 191 | 83.52 181 | 86.34 138 | 94.55 99 | 93.19 75 |
|
Baseline_NR-MVSNet | | | 79.84 130 | 78.37 143 | 81.55 124 | 84.98 144 | 86.66 170 | 85.06 150 | 83.49 69 | 75.57 112 | 63.31 158 | 58.22 192 | 60.97 168 | 78.00 137 | 86.89 123 | 87.13 121 | 94.47 103 | 93.15 76 |
|
PS-CasMVS | | | 75.90 190 | 75.86 187 | 75.96 186 | 82.59 190 | 88.46 155 | 79.23 194 | 79.56 126 | 66.00 191 | 52.77 203 | 59.48 184 | 54.35 207 | 67.14 194 | 83.37 184 | 86.23 139 | 94.47 103 | 93.10 77 |
|
v52 | | | 76.55 177 | 75.89 185 | 77.31 173 | 79.94 206 | 88.49 154 | 81.07 183 | 73.62 181 | 65.49 196 | 61.66 174 | 56.29 198 | 58.90 187 | 74.30 160 | 83.47 183 | 85.62 165 | 93.28 153 | 92.99 78 |
|
V4 | | | 76.55 177 | 75.89 185 | 77.32 172 | 79.95 205 | 88.50 153 | 81.07 183 | 73.62 181 | 65.47 197 | 61.71 172 | 56.31 197 | 58.87 189 | 74.28 161 | 83.48 182 | 85.62 165 | 93.28 153 | 92.98 79 |
|
ACMM | | 83.27 10 | 87.68 58 | 86.09 69 | 89.54 46 | 93.26 52 | 92.19 89 | 91.43 55 | 86.74 43 | 86.02 52 | 82.85 49 | 75.63 70 | 75.14 86 | 88.41 38 | 90.68 81 | 89.99 69 | 94.59 94 | 92.97 80 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v7n | | | 77.22 170 | 76.23 179 | 78.38 166 | 81.89 195 | 89.10 147 | 82.24 174 | 76.36 156 | 65.96 192 | 61.21 180 | 56.56 195 | 55.79 201 | 75.07 153 | 86.55 131 | 86.68 128 | 93.52 150 | 92.95 81 |
|
Fast-Effi-MVS+ | | | 83.77 79 | 82.98 82 | 84.69 72 | 87.98 109 | 91.87 92 | 88.10 91 | 77.70 147 | 78.10 97 | 73.04 89 | 69.13 110 | 68.51 113 | 86.66 55 | 90.49 84 | 89.85 74 | 94.67 89 | 92.88 82 |
|
diffmvs | | | 85.70 69 | 86.35 67 | 84.95 71 | 87.75 111 | 90.96 102 | 89.09 74 | 78.56 137 | 86.50 51 | 80.44 60 | 77.86 62 | 83.93 52 | 81.64 74 | 85.52 153 | 86.79 126 | 92.21 168 | 92.87 83 |
|
WR-MVS_H | | | 75.84 191 | 76.93 174 | 74.57 197 | 82.86 186 | 89.50 138 | 78.34 198 | 79.36 129 | 66.90 186 | 52.51 204 | 60.20 179 | 59.71 177 | 59.73 206 | 83.61 180 | 85.77 161 | 94.65 90 | 92.84 84 |
|
v144192 | | | 78.81 154 | 77.22 170 | 80.67 143 | 82.95 182 | 89.79 130 | 86.40 132 | 77.42 148 | 68.26 181 | 63.13 159 | 59.50 183 | 58.13 190 | 80.08 104 | 85.93 140 | 86.08 156 | 94.06 119 | 92.83 85 |
|
MVSTER | | | 86.03 66 | 86.12 68 | 85.93 66 | 88.62 106 | 89.93 126 | 89.33 71 | 79.91 116 | 81.87 67 | 81.35 54 | 81.07 50 | 74.91 88 | 80.66 85 | 92.13 51 | 90.10 66 | 95.68 34 | 92.80 86 |
|
WR-MVS | | | 76.63 175 | 78.02 153 | 75.02 192 | 84.14 153 | 89.76 131 | 78.34 198 | 80.64 106 | 69.56 172 | 52.32 205 | 61.26 154 | 61.24 166 | 60.66 205 | 84.45 175 | 87.07 122 | 93.99 124 | 92.77 87 |
|
v1921920 | | | 78.57 159 | 76.99 173 | 80.41 151 | 82.93 183 | 89.63 136 | 86.38 133 | 77.14 151 | 68.31 180 | 61.80 171 | 58.89 189 | 56.79 195 | 80.19 101 | 86.50 135 | 86.05 158 | 94.02 122 | 92.76 88 |
|
v1192 | | | 78.94 153 | 77.33 165 | 80.82 141 | 83.25 177 | 89.90 127 | 86.91 122 | 77.72 146 | 68.63 179 | 62.61 163 | 59.17 185 | 57.53 192 | 80.62 88 | 86.89 123 | 86.47 135 | 93.79 145 | 92.75 89 |
|
IterMVS-LS | | | 83.28 84 | 82.95 83 | 83.65 93 | 88.39 108 | 88.63 151 | 86.80 125 | 78.64 136 | 76.56 104 | 73.43 87 | 72.52 93 | 75.35 85 | 80.81 82 | 86.43 136 | 88.51 103 | 93.84 139 | 92.66 90 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
LTVRE_ROB | | 74.41 16 | 75.78 192 | 74.72 197 | 77.02 177 | 85.88 129 | 89.22 142 | 82.44 170 | 77.17 150 | 50.57 224 | 45.45 217 | 65.44 131 | 52.29 212 | 81.25 76 | 85.50 154 | 87.42 118 | 89.94 191 | 92.62 91 |
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 | | | 79.59 140 | 78.43 141 | 80.94 140 | 82.79 188 | 89.71 133 | 86.66 127 | 76.73 155 | 71.38 153 | 67.42 122 | 61.01 162 | 62.30 151 | 78.39 134 | 85.56 150 | 86.48 134 | 93.65 149 | 92.60 92 |
|
tfpn | | | 81.79 102 | 80.06 118 | 83.82 92 | 90.61 76 | 92.91 83 | 87.62 96 | 82.34 90 | 73.66 136 | 67.46 119 | 64.99 138 | 55.50 202 | 79.77 113 | 91.12 72 | 89.62 80 | 95.14 61 | 92.59 93 |
|
thres600view7 | | | 82.53 93 | 81.02 102 | 84.28 81 | 90.61 76 | 93.05 78 | 88.57 85 | 82.67 82 | 74.12 129 | 68.56 113 | 65.09 136 | 62.13 154 | 80.40 91 | 91.15 65 | 89.02 96 | 94.88 74 | 92.59 93 |
|
Effi-MVS+-dtu | | | 82.05 97 | 81.76 89 | 82.38 105 | 87.72 113 | 90.56 107 | 86.90 123 | 78.05 143 | 73.85 133 | 66.85 124 | 71.29 96 | 71.90 102 | 82.00 73 | 86.64 130 | 85.48 167 | 92.76 163 | 92.58 95 |
|
TAPA-MVS | | 84.37 7 | 88.91 46 | 88.93 46 | 88.89 50 | 93.00 57 | 94.85 52 | 92.00 48 | 84.84 55 | 91.68 28 | 80.05 61 | 79.77 53 | 84.56 48 | 88.17 41 | 90.11 87 | 89.00 97 | 95.30 53 | 92.57 96 |
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019 |
view800 | | | 82.38 95 | 80.93 107 | 84.06 87 | 90.59 78 | 92.96 81 | 88.11 90 | 82.44 89 | 73.92 130 | 68.10 116 | 65.07 137 | 61.64 156 | 80.10 103 | 91.17 60 | 89.24 90 | 95.01 65 | 92.56 97 |
|
v7 | | | 79.79 133 | 78.28 144 | 81.54 125 | 83.73 171 | 90.34 116 | 87.27 104 | 78.27 140 | 70.50 159 | 65.59 139 | 60.59 171 | 60.47 170 | 80.46 89 | 86.90 122 | 86.63 130 | 93.92 129 | 92.56 97 |
|
v10 | | | 79.62 139 | 78.19 145 | 81.28 130 | 83.73 171 | 89.69 134 | 87.27 104 | 76.86 153 | 70.50 159 | 65.46 140 | 60.58 173 | 60.47 170 | 80.44 90 | 86.91 121 | 86.63 130 | 93.93 127 | 92.55 99 |
|
ACMH | | 78.52 14 | 81.86 100 | 80.45 115 | 83.51 96 | 90.51 82 | 91.22 99 | 85.62 142 | 84.23 59 | 70.29 162 | 62.21 165 | 69.04 112 | 64.05 131 | 84.48 64 | 87.57 114 | 88.45 104 | 94.01 123 | 92.54 100 |
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019 |
v1144 | | | 79.38 144 | 77.83 158 | 81.18 132 | 83.62 173 | 90.23 118 | 87.15 116 | 78.35 139 | 69.13 174 | 64.02 154 | 60.20 179 | 59.41 181 | 80.14 102 | 86.78 126 | 86.57 132 | 93.81 141 | 92.53 101 |
|
tfpn111 | | | 83.51 81 | 82.68 84 | 84.47 77 | 90.30 87 | 93.09 75 | 89.05 77 | 82.72 78 | 75.14 114 | 69.49 104 | 74.24 77 | 63.13 133 | 80.38 92 | 91.15 65 | 89.51 81 | 94.91 70 | 92.50 102 |
|
conf200view11 | | | 82.85 86 | 81.46 93 | 84.47 77 | 90.30 87 | 93.09 75 | 89.05 77 | 82.72 78 | 75.14 114 | 69.49 104 | 65.72 127 | 63.13 133 | 80.38 92 | 91.15 65 | 89.51 81 | 94.91 70 | 92.50 102 |
|
tfpn200view9 | | | 82.86 85 | 81.46 93 | 84.48 76 | 90.30 87 | 93.09 75 | 89.05 77 | 82.71 80 | 75.14 114 | 69.56 101 | 65.72 127 | 63.13 133 | 80.38 92 | 91.15 65 | 89.51 81 | 94.91 70 | 92.50 102 |
|
view600 | | | 82.51 94 | 81.00 106 | 84.27 82 | 90.56 79 | 92.95 82 | 88.57 85 | 82.57 87 | 74.16 128 | 68.70 112 | 65.13 135 | 62.15 153 | 80.36 97 | 91.15 65 | 88.98 98 | 94.87 76 | 92.48 105 |
|
v1240 | | | 78.15 161 | 76.53 176 | 80.04 153 | 82.85 187 | 89.48 139 | 85.61 143 | 76.77 154 | 67.05 184 | 61.18 181 | 58.37 191 | 56.16 200 | 79.89 108 | 86.11 139 | 86.08 156 | 93.92 129 | 92.47 106 |
|
thres400 | | | 82.68 88 | 81.15 99 | 84.47 77 | 90.52 80 | 92.89 84 | 88.95 83 | 82.71 80 | 74.33 125 | 69.22 108 | 65.31 132 | 62.61 142 | 80.63 86 | 90.96 74 | 89.50 85 | 94.79 79 | 92.45 107 |
|
CHOSEN 1792x2688 | | | 82.16 96 | 80.91 109 | 83.61 94 | 91.14 69 | 92.01 91 | 89.55 70 | 79.15 131 | 79.87 84 | 70.29 95 | 52.51 208 | 72.56 99 | 81.39 75 | 88.87 101 | 88.17 107 | 90.15 189 | 92.37 108 |
|
v748 | | | 76.17 186 | 75.10 195 | 77.43 171 | 81.60 197 | 88.01 158 | 79.02 195 | 76.28 161 | 64.47 200 | 64.14 152 | 56.55 196 | 56.26 199 | 70.40 181 | 82.50 190 | 85.77 161 | 93.11 158 | 92.15 109 |
|
tfpnnormal | | | 77.46 169 | 74.86 196 | 80.49 147 | 86.34 127 | 88.92 149 | 84.33 158 | 81.26 102 | 61.39 208 | 61.70 173 | 51.99 209 | 53.66 209 | 74.84 154 | 88.63 105 | 87.38 119 | 94.50 101 | 92.08 110 |
|
CANet_DTU | | | 85.43 70 | 87.72 60 | 82.76 103 | 90.95 74 | 93.01 80 | 89.99 63 | 75.46 173 | 82.67 62 | 64.91 147 | 83.14 40 | 80.09 67 | 80.68 84 | 92.03 52 | 91.03 50 | 94.57 96 | 92.08 110 |
|
thres200 | | | 82.77 87 | 81.25 98 | 84.54 73 | 90.38 84 | 93.05 78 | 89.13 73 | 82.67 82 | 74.40 124 | 69.53 103 | 65.69 130 | 63.03 138 | 80.63 86 | 91.15 65 | 89.42 86 | 94.88 74 | 92.04 112 |
|
PEN-MVS | | | 76.02 188 | 76.07 180 | 75.95 187 | 83.17 179 | 87.97 159 | 79.65 189 | 80.07 115 | 66.57 188 | 51.45 207 | 60.94 163 | 55.47 203 | 66.81 195 | 82.72 187 | 86.80 125 | 94.59 94 | 92.03 113 |
|
v8 | | | 79.90 129 | 78.39 142 | 81.66 121 | 83.97 157 | 89.81 128 | 87.16 115 | 77.40 149 | 71.49 150 | 67.71 117 | 61.24 156 | 62.49 146 | 79.83 109 | 85.48 159 | 86.17 151 | 93.89 133 | 92.02 114 |
|
v1neww | | | 80.09 125 | 78.45 139 | 82.00 110 | 83.97 157 | 90.49 108 | 87.18 112 | 79.67 121 | 71.49 150 | 67.44 120 | 61.24 156 | 62.41 149 | 79.83 109 | 85.49 155 | 86.19 147 | 93.88 135 | 91.86 115 |
|
v7new | | | 80.09 125 | 78.45 139 | 82.00 110 | 83.97 157 | 90.49 108 | 87.18 112 | 79.67 121 | 71.49 150 | 67.44 120 | 61.24 156 | 62.41 149 | 79.83 109 | 85.49 155 | 86.19 147 | 93.88 135 | 91.86 115 |
|
v6 | | | 80.11 124 | 78.47 137 | 82.01 109 | 83.97 157 | 90.49 108 | 87.19 111 | 79.67 121 | 71.59 149 | 67.51 118 | 61.26 154 | 62.46 148 | 79.81 112 | 85.49 155 | 86.18 150 | 93.89 133 | 91.86 115 |
|
TSAR-MVS + COLMAP | | | 88.40 50 | 89.09 45 | 87.60 61 | 92.72 61 | 93.92 63 | 92.21 45 | 85.57 50 | 91.73 26 | 73.72 85 | 91.75 19 | 73.22 98 | 87.64 47 | 91.49 54 | 89.71 78 | 93.73 146 | 91.82 118 |
|
divwei89l23v2f112 | | | 79.75 135 | 78.04 151 | 81.75 115 | 83.90 161 | 90.37 113 | 87.21 107 | 79.90 117 | 70.20 165 | 66.18 131 | 60.92 164 | 61.48 161 | 79.52 124 | 85.36 161 | 86.17 151 | 93.81 141 | 91.77 119 |
|
v1 | | | 79.76 134 | 78.06 149 | 81.74 117 | 83.89 164 | 90.38 112 | 87.20 108 | 79.88 119 | 70.23 163 | 66.17 134 | 60.92 164 | 61.56 157 | 79.50 125 | 85.37 160 | 86.17 151 | 93.81 141 | 91.77 119 |
|
conf0.01 | | | 82.64 89 | 81.02 102 | 84.53 75 | 90.30 87 | 93.22 73 | 89.05 77 | 82.75 76 | 75.14 114 | 69.69 100 | 67.15 121 | 59.19 183 | 80.38 92 | 91.16 63 | 89.51 81 | 95.00 66 | 91.76 121 |
|
v1141 | | | 79.75 135 | 78.04 151 | 81.75 115 | 83.89 164 | 90.37 113 | 87.20 108 | 79.89 118 | 70.23 163 | 66.18 131 | 60.92 164 | 61.48 161 | 79.54 121 | 85.36 161 | 86.17 151 | 93.81 141 | 91.76 121 |
|
GBi-Net | | | 84.51 73 | 84.80 74 | 84.17 84 | 84.20 150 | 89.95 123 | 89.70 66 | 80.37 107 | 81.17 71 | 75.50 77 | 69.63 103 | 79.69 72 | 79.75 114 | 90.73 78 | 90.72 55 | 95.52 43 | 91.71 123 |
|
test1 | | | 84.51 73 | 84.80 74 | 84.17 84 | 84.20 150 | 89.95 123 | 89.70 66 | 80.37 107 | 81.17 71 | 75.50 77 | 69.63 103 | 79.69 72 | 79.75 114 | 90.73 78 | 90.72 55 | 95.52 43 | 91.71 123 |
|
FMVSNet2 | | | 83.87 77 | 83.73 80 | 84.05 89 | 84.20 150 | 89.95 123 | 89.70 66 | 80.21 112 | 79.17 91 | 74.89 81 | 65.91 125 | 77.49 81 | 79.75 114 | 90.87 75 | 91.00 52 | 95.52 43 | 91.71 123 |
|
v2v482 | | | 79.84 130 | 78.07 147 | 81.90 113 | 83.75 170 | 90.21 120 | 87.17 114 | 79.85 120 | 70.65 157 | 65.93 137 | 61.93 150 | 60.07 174 | 80.82 80 | 85.25 164 | 86.71 127 | 93.88 135 | 91.70 126 |
|
FMVSNet1 | | | 81.64 106 | 80.61 114 | 82.84 102 | 82.36 192 | 89.20 143 | 88.67 84 | 79.58 125 | 70.79 156 | 72.63 91 | 58.95 188 | 72.26 101 | 79.34 128 | 90.73 78 | 90.72 55 | 94.47 103 | 91.62 127 |
|
thres100view900 | | | 82.55 91 | 81.01 105 | 84.34 80 | 90.30 87 | 92.27 87 | 89.04 82 | 82.77 75 | 75.14 114 | 69.56 101 | 65.72 127 | 63.13 133 | 79.62 117 | 89.97 89 | 89.26 89 | 94.73 84 | 91.61 128 |
|
FMVSNet3 | | | 84.44 75 | 84.64 76 | 84.21 83 | 84.32 149 | 90.13 121 | 89.85 65 | 80.37 107 | 81.17 71 | 75.50 77 | 69.63 103 | 79.69 72 | 79.62 117 | 89.72 92 | 90.52 62 | 95.59 39 | 91.58 129 |
|
Vis-MVSNet (Re-imp) | | | 83.65 80 | 86.81 65 | 79.96 154 | 90.46 83 | 92.71 85 | 84.84 153 | 82.00 95 | 80.93 77 | 62.44 164 | 76.29 68 | 82.32 59 | 65.54 199 | 92.29 45 | 91.66 44 | 94.49 102 | 91.47 130 |
|
Fast-Effi-MVS+-dtu | | | 79.95 128 | 80.69 113 | 79.08 159 | 86.36 126 | 89.14 145 | 85.85 137 | 72.28 185 | 72.85 144 | 59.32 189 | 70.43 101 | 68.42 114 | 77.57 139 | 86.14 138 | 86.44 136 | 93.11 158 | 91.39 131 |
|
conf0.002 | | | 82.54 92 | 80.83 110 | 84.54 73 | 90.28 92 | 93.24 72 | 89.05 77 | 82.75 76 | 75.14 114 | 69.75 99 | 67.99 116 | 57.12 194 | 80.38 92 | 91.16 63 | 89.79 75 | 95.02 64 | 91.36 132 |
|
pm-mvs1 | | | 78.51 160 | 77.75 160 | 79.40 157 | 84.83 147 | 89.30 140 | 83.55 164 | 79.38 128 | 62.64 204 | 63.68 156 | 58.73 190 | 64.68 125 | 70.78 179 | 89.79 91 | 87.84 112 | 94.17 114 | 91.28 133 |
|
ACMH+ | | 79.08 13 | 81.84 101 | 80.06 118 | 83.91 90 | 89.92 100 | 90.62 106 | 86.21 134 | 83.48 71 | 73.88 132 | 65.75 138 | 66.38 124 | 65.30 123 | 84.63 63 | 85.90 141 | 87.25 120 | 93.45 151 | 91.13 134 |
|
v11 | | | 79.02 151 | 77.36 163 | 80.95 139 | 83.89 164 | 86.48 178 | 86.53 130 | 75.77 172 | 69.69 171 | 65.21 145 | 60.36 176 | 60.24 173 | 80.32 98 | 87.20 117 | 86.54 133 | 93.96 126 | 91.02 135 |
|
v17 | | | 79.59 140 | 77.88 157 | 81.60 123 | 84.03 155 | 86.66 170 | 87.13 117 | 76.31 160 | 72.09 147 | 68.29 114 | 61.15 160 | 62.57 144 | 79.90 107 | 85.55 151 | 86.20 145 | 93.93 127 | 90.93 136 |
|
v18 | | | 79.71 137 | 77.98 154 | 81.73 118 | 84.02 156 | 86.67 169 | 87.37 101 | 76.35 157 | 72.61 145 | 68.86 110 | 61.35 153 | 62.65 141 | 79.94 105 | 85.49 155 | 86.21 142 | 93.85 138 | 90.92 137 |
|
v16 | | | 79.65 138 | 77.91 156 | 81.69 120 | 84.04 154 | 86.65 172 | 87.20 108 | 76.32 159 | 72.41 146 | 68.71 111 | 61.13 161 | 62.52 145 | 79.93 106 | 85.55 151 | 86.22 140 | 93.92 129 | 90.91 138 |
|
v13 | | | 78.99 152 | 77.25 169 | 81.02 138 | 83.87 169 | 86.47 179 | 86.60 129 | 75.96 170 | 69.87 170 | 66.07 135 | 60.25 178 | 61.41 164 | 79.49 126 | 85.72 143 | 86.22 140 | 94.14 115 | 90.84 139 |
|
V9 | | | 79.08 148 | 77.32 166 | 81.14 135 | 83.89 164 | 86.52 176 | 86.85 124 | 76.06 167 | 70.02 168 | 66.42 127 | 60.44 174 | 61.52 160 | 79.54 121 | 85.68 145 | 86.21 142 | 94.08 117 | 90.83 140 |
|
v12 | | | 79.03 150 | 77.28 167 | 81.06 137 | 83.88 168 | 86.49 177 | 86.62 128 | 76.02 168 | 69.99 169 | 66.18 131 | 60.34 177 | 61.44 163 | 79.54 121 | 85.70 144 | 86.21 142 | 94.11 116 | 90.82 141 |
|
V14 | | | 79.11 147 | 77.35 164 | 81.16 133 | 83.90 161 | 86.54 175 | 86.94 120 | 76.10 166 | 70.14 167 | 66.41 128 | 60.59 171 | 61.54 159 | 79.59 120 | 85.64 146 | 86.20 145 | 94.04 121 | 90.82 141 |
|
v15 | | | 79.13 146 | 77.37 162 | 81.19 131 | 83.90 161 | 86.56 174 | 87.01 119 | 76.15 164 | 70.20 165 | 66.48 126 | 60.71 169 | 61.55 158 | 79.60 119 | 85.59 149 | 86.19 147 | 93.98 125 | 90.80 143 |
|
UA-Net | | | 86.07 65 | 87.78 57 | 84.06 87 | 92.85 59 | 95.11 48 | 87.73 94 | 84.38 57 | 73.22 139 | 73.18 88 | 79.99 52 | 89.22 31 | 71.47 177 | 93.22 34 | 93.03 33 | 94.76 82 | 90.69 144 |
|
CDS-MVSNet | | | 81.63 107 | 82.09 88 | 81.09 136 | 87.21 120 | 90.28 117 | 87.46 100 | 80.33 110 | 69.06 175 | 70.66 93 | 71.30 95 | 73.87 92 | 67.99 189 | 89.58 93 | 89.87 73 | 92.87 162 | 90.69 144 |
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022 |
v148 | | | 78.59 158 | 76.84 175 | 80.62 144 | 83.61 174 | 89.16 144 | 83.65 163 | 79.24 130 | 69.38 173 | 69.34 107 | 59.88 181 | 60.41 172 | 75.19 150 | 83.81 179 | 84.63 176 | 92.70 164 | 90.63 146 |
|
pmmvs6 | | | 74.83 196 | 72.89 202 | 77.09 175 | 82.11 193 | 87.50 162 | 80.88 186 | 76.97 152 | 52.79 222 | 61.91 170 | 46.66 215 | 60.49 169 | 69.28 184 | 86.74 129 | 85.46 168 | 91.39 176 | 90.56 147 |
|
DTE-MVSNet | | | 75.14 195 | 75.44 192 | 74.80 194 | 83.18 178 | 87.19 165 | 78.25 200 | 80.11 114 | 66.05 190 | 48.31 213 | 60.88 167 | 54.67 205 | 64.54 201 | 82.57 189 | 86.17 151 | 94.43 106 | 90.53 148 |
|
PLC | | 83.76 9 | 88.61 49 | 86.83 64 | 90.70 34 | 94.22 43 | 92.63 86 | 91.50 54 | 87.19 41 | 89.16 41 | 86.87 29 | 75.51 71 | 80.87 63 | 89.98 30 | 90.01 88 | 89.20 91 | 94.41 107 | 90.45 149 |
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019 |
GA-MVS | | | 79.52 142 | 79.71 126 | 79.30 158 | 85.68 132 | 90.36 115 | 84.55 155 | 78.44 138 | 70.47 161 | 57.87 197 | 68.52 114 | 61.38 165 | 76.21 146 | 89.40 96 | 87.89 110 | 93.04 160 | 89.96 150 |
|
LS3D | | | 85.96 67 | 84.37 77 | 87.81 60 | 94.13 44 | 93.27 71 | 90.26 62 | 89.00 28 | 84.91 57 | 72.84 90 | 71.74 94 | 72.47 100 | 87.45 49 | 89.53 94 | 89.09 94 | 93.20 156 | 89.60 151 |
|
gg-mvs-nofinetune | | | 75.64 193 | 77.26 168 | 73.76 198 | 87.92 110 | 92.20 88 | 87.32 102 | 64.67 217 | 51.92 223 | 35.35 230 | 46.44 216 | 77.05 83 | 71.97 171 | 92.64 43 | 91.02 51 | 95.34 51 | 89.53 152 |
|
SixPastTwentyTwo | | | 76.02 188 | 75.72 188 | 76.36 182 | 83.38 175 | 87.54 161 | 75.50 206 | 76.22 162 | 65.50 195 | 57.05 198 | 70.64 98 | 53.97 208 | 74.54 156 | 80.96 196 | 82.12 191 | 91.44 175 | 89.35 153 |
|
HyFIR lowres test | | | 81.62 108 | 79.45 129 | 84.14 86 | 91.00 72 | 93.38 70 | 88.27 88 | 78.19 141 | 76.28 106 | 70.18 97 | 48.78 212 | 73.69 94 | 83.52 66 | 87.05 120 | 87.83 113 | 93.68 148 | 89.15 154 |
|
CostFormer | | | 80.94 114 | 80.21 117 | 81.79 114 | 87.69 114 | 88.58 152 | 87.47 99 | 70.66 191 | 80.02 82 | 77.88 73 | 73.03 89 | 71.40 103 | 78.24 135 | 79.96 201 | 79.63 197 | 88.82 196 | 88.84 155 |
|
IterMVS | | | 78.79 155 | 79.71 126 | 77.71 168 | 85.26 138 | 85.91 182 | 84.54 156 | 69.84 197 | 73.38 138 | 61.25 179 | 70.53 100 | 70.35 105 | 74.43 158 | 85.21 167 | 83.80 181 | 90.95 183 | 88.77 156 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo. |
pmmvs5 | | | 76.93 172 | 76.33 178 | 77.62 169 | 81.97 194 | 88.40 156 | 81.32 179 | 74.35 178 | 65.42 198 | 61.42 177 | 63.07 147 | 57.95 191 | 73.23 167 | 85.60 148 | 85.35 169 | 93.41 152 | 88.55 157 |
|
RPMNet | | | 77.07 171 | 77.63 161 | 76.42 181 | 85.56 134 | 85.15 190 | 81.37 177 | 65.27 214 | 74.71 121 | 60.29 185 | 63.71 146 | 66.59 119 | 73.64 163 | 82.71 188 | 82.12 191 | 92.38 166 | 88.39 158 |
|
test-mter | | | 77.79 164 | 80.02 120 | 75.18 191 | 81.18 201 | 82.85 201 | 80.52 188 | 62.03 222 | 73.62 137 | 62.16 166 | 73.55 82 | 73.83 93 | 73.81 162 | 84.67 172 | 83.34 183 | 91.37 177 | 88.31 159 |
|
CR-MVSNet | | | 78.71 156 | 78.86 132 | 78.55 164 | 85.85 131 | 85.15 190 | 82.30 172 | 68.23 200 | 74.71 121 | 65.37 142 | 64.39 143 | 69.59 109 | 77.18 141 | 85.10 169 | 84.87 172 | 92.34 167 | 88.21 160 |
|
PatchT | | | 76.42 181 | 77.81 159 | 74.80 194 | 78.46 210 | 84.30 195 | 71.82 213 | 65.03 216 | 73.89 131 | 65.37 142 | 61.58 151 | 66.70 118 | 77.18 141 | 85.10 169 | 84.87 172 | 90.94 184 | 88.21 160 |
|
pmmvs4 | | | 79.99 127 | 78.08 146 | 82.22 107 | 83.04 181 | 87.16 166 | 84.95 151 | 78.80 135 | 78.64 94 | 74.53 82 | 64.61 142 | 59.41 181 | 79.45 127 | 84.13 177 | 84.54 177 | 92.53 165 | 88.08 162 |
|
PM-MVS | | | 74.17 200 | 73.10 200 | 75.41 189 | 76.07 216 | 82.53 204 | 77.56 201 | 71.69 187 | 71.04 154 | 61.92 169 | 61.23 159 | 47.30 219 | 74.82 155 | 81.78 194 | 79.80 196 | 90.42 186 | 88.05 163 |
|
CVMVSNet | | | 76.70 174 | 78.46 138 | 74.64 196 | 83.34 176 | 84.48 194 | 81.83 176 | 74.58 175 | 68.88 177 | 51.23 209 | 69.77 102 | 70.05 106 | 67.49 192 | 84.27 176 | 83.81 180 | 89.38 194 | 87.96 164 |
|
TransMVSNet (Re) | | | 76.57 176 | 75.16 194 | 78.22 167 | 85.60 133 | 87.24 164 | 82.46 168 | 81.23 103 | 59.80 212 | 59.05 195 | 57.07 194 | 59.14 185 | 66.60 197 | 88.09 110 | 86.82 124 | 94.37 109 | 87.95 165 |
|
EG-PatchMatch MVS | | | 76.40 183 | 75.47 191 | 77.48 170 | 85.86 130 | 90.22 119 | 82.45 169 | 73.96 180 | 59.64 213 | 59.60 188 | 52.75 207 | 62.20 152 | 68.44 188 | 88.23 109 | 87.50 115 | 94.55 99 | 87.78 166 |
|
EPNet_dtu | | | 81.98 98 | 83.82 79 | 79.83 156 | 94.10 45 | 85.97 181 | 87.29 103 | 84.08 61 | 80.61 80 | 59.96 186 | 81.62 48 | 77.19 82 | 62.91 204 | 87.21 116 | 86.38 137 | 90.66 185 | 87.77 167 |
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023 |
TDRefinement | | | 79.05 149 | 77.05 172 | 81.39 126 | 88.45 107 | 89.00 148 | 86.92 121 | 82.65 84 | 74.21 127 | 64.41 149 | 59.17 185 | 59.16 184 | 74.52 157 | 85.23 165 | 85.09 170 | 91.37 177 | 87.51 168 |
|
tfpn_ndepth | | | 81.77 104 | 82.29 86 | 81.15 134 | 89.79 101 | 91.71 94 | 85.49 145 | 81.63 100 | 79.17 91 | 64.76 148 | 73.04 88 | 68.14 117 | 70.62 180 | 88.72 102 | 87.88 111 | 94.63 92 | 87.38 169 |
|
DWT-MVSNet_training | | | 80.51 120 | 78.05 150 | 83.39 97 | 88.64 105 | 88.33 157 | 86.11 136 | 76.33 158 | 79.65 86 | 78.64 68 | 69.62 106 | 58.89 188 | 80.82 80 | 80.50 198 | 82.03 193 | 89.77 192 | 87.36 170 |
|
tfpn1000 | | | 81.03 112 | 81.70 90 | 80.25 152 | 90.18 93 | 91.35 96 | 83.96 160 | 81.15 104 | 78.00 98 | 62.11 167 | 73.37 87 | 65.75 120 | 69.17 185 | 88.68 104 | 87.44 116 | 94.93 69 | 87.29 171 |
|
tpmp4_e23 | | | 79.82 132 | 77.96 155 | 82.00 110 | 87.59 116 | 86.93 167 | 87.81 93 | 72.21 186 | 79.99 83 | 78.02 71 | 67.83 118 | 64.77 124 | 78.74 132 | 79.99 200 | 78.90 200 | 87.65 202 | 87.29 171 |
|
CHOSEN 280x420 | | | 80.28 122 | 81.66 91 | 78.67 163 | 82.92 184 | 79.24 215 | 85.36 147 | 66.79 207 | 78.11 96 | 70.32 94 | 75.03 73 | 79.87 68 | 81.09 78 | 89.07 98 | 83.16 184 | 85.54 213 | 87.17 173 |
|
test-LLR | | | 79.47 143 | 79.84 123 | 79.03 160 | 87.47 117 | 82.40 206 | 81.24 180 | 78.05 143 | 73.72 134 | 62.69 161 | 73.76 80 | 74.42 89 | 73.49 164 | 84.61 173 | 82.99 186 | 91.25 179 | 87.01 174 |
|
TESTMET0.1,1 | | | 77.78 165 | 79.84 123 | 75.38 190 | 80.86 202 | 82.40 206 | 81.24 180 | 62.72 221 | 73.72 134 | 62.69 161 | 73.76 80 | 74.42 89 | 73.49 164 | 84.61 173 | 82.99 186 | 91.25 179 | 87.01 174 |
|
PatchMatch-RL | | | 83.34 83 | 81.36 96 | 85.65 67 | 90.33 86 | 89.52 137 | 84.36 157 | 81.82 97 | 80.87 79 | 79.29 64 | 74.04 79 | 62.85 140 | 86.05 58 | 88.40 108 | 87.04 123 | 92.04 170 | 86.77 176 |
|
FC-MVSNet-test | | | 76.53 180 | 81.62 92 | 70.58 205 | 84.99 143 | 85.73 184 | 74.81 207 | 78.85 134 | 77.00 102 | 39.13 229 | 75.90 69 | 73.50 95 | 54.08 212 | 86.54 132 | 85.99 159 | 91.65 173 | 86.68 177 |
|
tpm | | | 76.30 185 | 76.05 182 | 76.59 180 | 86.97 122 | 83.01 200 | 83.83 161 | 67.06 206 | 71.83 148 | 63.87 155 | 69.56 107 | 62.88 139 | 73.41 166 | 79.79 202 | 78.59 201 | 84.41 217 | 86.68 177 |
|
PMMVS | | | 81.65 105 | 84.05 78 | 78.86 161 | 78.56 209 | 82.63 203 | 83.10 165 | 67.22 205 | 81.39 70 | 70.11 98 | 84.91 38 | 79.74 71 | 82.12 71 | 87.31 115 | 85.70 163 | 92.03 171 | 86.67 179 |
|
tfpn_n400 | | | 80.63 117 | 80.79 111 | 80.43 149 | 90.02 97 | 91.08 100 | 85.34 148 | 81.79 98 | 72.93 142 | 59.27 190 | 73.54 83 | 64.40 127 | 71.61 175 | 89.05 99 | 88.21 105 | 94.56 97 | 86.32 180 |
|
tfpnconf | | | 80.63 117 | 80.79 111 | 80.43 149 | 90.02 97 | 91.08 100 | 85.34 148 | 81.79 98 | 72.93 142 | 59.27 190 | 73.54 83 | 64.40 127 | 71.61 175 | 89.05 99 | 88.21 105 | 94.56 97 | 86.32 180 |
|
MS-PatchMatch | | | 81.79 102 | 81.44 95 | 82.19 108 | 90.35 85 | 89.29 141 | 88.08 92 | 75.36 174 | 77.60 99 | 69.00 109 | 64.37 144 | 78.87 76 | 77.14 143 | 88.03 111 | 85.70 163 | 93.19 157 | 86.24 182 |
|
RPSCF | | | 83.46 82 | 83.36 81 | 83.59 95 | 87.75 111 | 87.35 163 | 84.82 154 | 79.46 127 | 83.84 60 | 78.12 69 | 82.69 41 | 79.87 68 | 82.60 70 | 82.47 191 | 81.13 195 | 88.78 197 | 86.13 183 |
|
pmmvs-eth3d | | | 74.32 199 | 71.96 204 | 77.08 176 | 77.33 213 | 82.71 202 | 78.41 197 | 76.02 168 | 66.65 187 | 65.98 136 | 54.23 203 | 49.02 218 | 73.14 168 | 82.37 192 | 82.69 188 | 91.61 174 | 86.05 184 |
|
tfpnview11 | | | 80.84 115 | 81.10 100 | 80.54 146 | 90.10 95 | 90.96 102 | 85.44 146 | 81.84 96 | 75.77 109 | 59.27 190 | 73.54 83 | 64.40 127 | 71.69 174 | 89.16 97 | 87.97 108 | 94.91 70 | 85.92 185 |
|
MSDG | | | 83.87 77 | 81.02 102 | 87.19 63 | 92.17 65 | 89.80 129 | 89.15 72 | 85.72 49 | 80.61 80 | 79.24 65 | 66.66 123 | 68.75 112 | 82.69 67 | 87.95 112 | 87.44 116 | 94.19 113 | 85.92 185 |
|
EU-MVSNet | | | 69.98 206 | 72.30 203 | 67.28 211 | 75.67 218 | 79.39 213 | 73.12 210 | 69.94 196 | 63.59 203 | 42.80 220 | 62.93 148 | 56.71 197 | 55.07 210 | 79.13 206 | 78.55 202 | 87.06 207 | 85.82 187 |
|
thresconf0.02 | | | 81.14 111 | 80.93 107 | 81.39 126 | 90.01 99 | 91.31 97 | 86.79 126 | 82.28 92 | 76.97 103 | 61.46 176 | 74.24 77 | 62.08 155 | 72.98 169 | 88.70 103 | 87.90 109 | 94.81 78 | 85.28 188 |
|
Anonymous20231211 | | | 62.95 218 | 60.42 221 | 65.89 214 | 74.22 220 | 78.37 217 | 67.66 217 | 74.47 176 | 40.37 232 | 39.59 227 | 27.51 231 | 38.26 230 | 52.13 213 | 75.39 216 | 77.89 206 | 87.28 205 | 85.16 189 |
|
tpm cat1 | | | 77.78 165 | 75.28 193 | 80.70 142 | 87.14 121 | 85.84 183 | 85.81 138 | 70.40 192 | 77.44 100 | 78.80 67 | 63.72 145 | 64.01 132 | 76.55 145 | 75.60 215 | 75.21 215 | 85.51 214 | 85.12 190 |
|
ambc | | | | 61.92 218 | | 70.98 227 | 73.54 221 | 63.64 223 | | 60.06 211 | 52.23 206 | 38.44 223 | 19.17 238 | 57.12 207 | 82.33 193 | 75.03 217 | 83.21 221 | 84.89 191 |
|
COLMAP_ROB | | 76.78 15 | 80.50 121 | 78.49 136 | 82.85 101 | 90.96 73 | 89.65 135 | 86.20 135 | 83.40 73 | 77.15 101 | 66.54 125 | 62.27 149 | 65.62 122 | 77.89 138 | 85.23 165 | 84.70 175 | 92.11 169 | 84.83 192 |
Johannes L. Schönberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016 |
USDC | | | 80.69 116 | 79.89 122 | 81.62 122 | 86.48 125 | 89.11 146 | 86.53 130 | 78.86 133 | 81.15 74 | 63.48 157 | 72.98 90 | 59.12 186 | 81.16 77 | 87.10 118 | 85.01 171 | 93.23 155 | 84.77 193 |
|
gm-plane-assit | | | 70.29 205 | 70.65 206 | 69.88 206 | 85.03 142 | 78.50 216 | 58.41 227 | 65.47 213 | 50.39 225 | 40.88 222 | 49.60 211 | 50.11 215 | 75.14 152 | 91.43 55 | 89.78 76 | 94.32 110 | 84.73 194 |
|
GG-mvs-BLEND | | | 57.56 221 | 82.61 85 | 28.34 234 | 0.22 240 | 90.10 122 | 79.37 192 | 0.14 238 | 79.56 87 | 0.40 242 | 71.25 97 | 83.40 54 | 0.30 239 | 86.27 137 | 83.87 179 | 89.59 193 | 83.83 195 |
|
Anonymous20231206 | | | 70.80 204 | 70.59 207 | 71.04 204 | 81.60 197 | 82.49 205 | 74.64 208 | 75.87 171 | 64.17 201 | 49.27 211 | 44.85 219 | 53.59 210 | 54.68 211 | 83.07 185 | 82.34 190 | 90.17 188 | 83.65 196 |
|
dps | | | 78.02 162 | 75.94 184 | 80.44 148 | 86.06 128 | 86.62 173 | 82.58 167 | 69.98 195 | 75.14 114 | 77.76 75 | 69.08 111 | 59.93 176 | 78.47 133 | 79.47 203 | 77.96 204 | 87.78 200 | 83.40 197 |
|
tpmrst | | | 76.55 177 | 75.99 183 | 77.20 174 | 87.32 119 | 83.05 199 | 82.86 166 | 65.62 212 | 78.61 95 | 67.22 123 | 69.19 109 | 65.71 121 | 75.87 148 | 76.75 212 | 75.33 214 | 84.31 218 | 83.28 198 |
|
TAMVS | | | 76.42 181 | 77.16 171 | 75.56 188 | 83.05 180 | 85.55 187 | 80.58 187 | 71.43 188 | 65.40 199 | 61.04 182 | 67.27 120 | 69.22 111 | 67.99 189 | 84.88 171 | 84.78 174 | 89.28 195 | 83.01 199 |
|
TinyColmap | | | 76.73 173 | 73.95 199 | 79.96 154 | 85.16 141 | 85.64 186 | 82.34 171 | 78.19 141 | 70.63 158 | 62.06 168 | 60.69 170 | 49.61 216 | 80.81 82 | 85.12 168 | 83.69 182 | 91.22 181 | 82.27 200 |
|
MDTV_nov1_ep13 | | | 79.14 145 | 79.49 128 | 78.74 162 | 85.40 135 | 86.89 168 | 84.32 159 | 70.29 193 | 78.85 93 | 69.42 106 | 75.37 72 | 73.29 97 | 75.64 149 | 80.61 197 | 79.48 199 | 87.36 203 | 81.91 201 |
|
PatchmatchNet | | | 78.67 157 | 78.85 133 | 78.46 165 | 86.85 124 | 86.03 180 | 83.77 162 | 68.11 202 | 80.88 78 | 66.19 129 | 72.90 91 | 73.40 96 | 78.06 136 | 79.25 205 | 77.71 207 | 87.75 201 | 81.75 202 |
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. |
test0.0.03 1 | | | 76.03 187 | 78.51 135 | 73.12 202 | 87.47 117 | 85.13 192 | 76.32 204 | 78.05 143 | 73.19 141 | 50.98 210 | 70.64 98 | 69.28 110 | 55.53 208 | 85.33 163 | 84.38 178 | 90.39 187 | 81.63 203 |
|
CMPMVS | | 56.49 17 | 73.84 201 | 71.73 205 | 76.31 184 | 85.20 139 | 85.67 185 | 75.80 205 | 73.23 183 | 62.26 205 | 65.40 141 | 53.40 206 | 59.70 178 | 71.77 173 | 80.25 199 | 79.56 198 | 86.45 209 | 81.28 204 |
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011 |
MIMVSNet | | | 74.69 197 | 75.60 190 | 73.62 199 | 76.02 217 | 85.31 189 | 81.21 182 | 67.43 203 | 71.02 155 | 59.07 194 | 54.48 200 | 64.07 130 | 66.14 198 | 86.52 133 | 86.64 129 | 91.83 172 | 81.17 205 |
|
MDTV_nov1_ep13_2view | | | 73.21 202 | 72.91 201 | 73.56 200 | 80.01 203 | 84.28 196 | 78.62 196 | 66.43 209 | 68.64 178 | 59.12 193 | 60.39 175 | 59.69 179 | 69.81 183 | 78.82 207 | 77.43 209 | 87.36 203 | 81.11 206 |
|
EPMVS | | | 77.53 168 | 78.07 147 | 76.90 178 | 86.89 123 | 84.91 193 | 82.18 175 | 66.64 208 | 81.00 76 | 64.11 153 | 72.75 92 | 69.68 108 | 74.42 159 | 79.36 204 | 78.13 203 | 87.14 206 | 80.68 207 |
|
ADS-MVSNet | | | 74.53 198 | 75.69 189 | 73.17 201 | 81.57 199 | 80.71 211 | 79.27 193 | 63.03 220 | 79.27 90 | 59.94 187 | 67.86 117 | 68.32 116 | 71.08 178 | 77.33 209 | 76.83 210 | 84.12 220 | 79.53 208 |
|
MDA-MVSNet-bldmvs | | | 66.22 211 | 64.49 214 | 68.24 209 | 61.67 230 | 82.11 208 | 70.07 215 | 76.16 163 | 59.14 214 | 47.94 214 | 54.35 202 | 35.82 231 | 67.33 193 | 64.94 229 | 75.68 213 | 86.30 210 | 79.36 209 |
|
testpf | | | 63.91 214 | 65.23 213 | 62.38 218 | 81.32 200 | 69.95 226 | 62.71 225 | 54.16 230 | 61.29 209 | 48.73 212 | 57.31 193 | 52.50 211 | 50.97 215 | 67.50 225 | 68.86 226 | 76.36 228 | 79.21 210 |
|
FMVSNet5 | | | 75.50 194 | 76.07 180 | 74.83 193 | 76.16 215 | 81.19 209 | 81.34 178 | 70.21 194 | 73.20 140 | 61.59 175 | 58.97 187 | 68.33 115 | 68.50 187 | 85.87 142 | 85.85 160 | 91.18 182 | 79.11 211 |
|
testgi | | | 71.92 203 | 74.20 198 | 69.27 208 | 84.58 148 | 83.06 198 | 73.40 209 | 74.39 177 | 64.04 202 | 46.17 216 | 68.90 113 | 57.15 193 | 48.89 219 | 84.07 178 | 83.08 185 | 88.18 199 | 79.09 212 |
|
MIMVSNet1 | | | 65.00 212 | 66.24 212 | 63.55 217 | 58.41 234 | 80.01 212 | 69.00 216 | 74.03 179 | 55.81 220 | 41.88 221 | 36.81 228 | 49.48 217 | 47.89 220 | 81.32 195 | 82.40 189 | 90.08 190 | 77.88 213 |
|
test20.03 | | | 68.31 209 | 70.05 208 | 66.28 213 | 82.41 191 | 80.84 210 | 67.35 218 | 76.11 165 | 58.44 215 | 40.80 223 | 53.77 204 | 54.54 206 | 42.28 225 | 83.07 185 | 81.96 194 | 88.73 198 | 77.76 214 |
|
pmmvs3 | | | 61.89 219 | 61.74 219 | 62.06 219 | 64.30 228 | 70.83 225 | 64.22 221 | 52.14 232 | 48.78 226 | 44.47 218 | 41.67 222 | 41.70 224 | 63.03 203 | 76.06 213 | 76.02 212 | 84.18 219 | 77.14 215 |
|
new-patchmatchnet | | | 63.80 215 | 63.31 217 | 64.37 215 | 76.49 214 | 75.99 218 | 63.73 222 | 70.99 190 | 57.27 216 | 43.08 219 | 45.86 217 | 43.80 220 | 45.13 224 | 73.20 220 | 70.68 225 | 86.80 208 | 76.34 216 |
|
LP | | | 68.35 208 | 67.23 209 | 69.67 207 | 77.49 212 | 79.38 214 | 72.84 212 | 61.37 223 | 66.94 185 | 55.08 199 | 47.00 214 | 50.35 214 | 65.16 200 | 75.61 214 | 76.03 211 | 86.08 212 | 75.28 217 |
|
N_pmnet | | | 66.85 210 | 66.63 210 | 67.11 212 | 78.73 208 | 74.66 220 | 70.53 214 | 71.07 189 | 66.46 189 | 46.54 215 | 51.68 210 | 51.91 213 | 55.48 209 | 74.68 217 | 72.38 221 | 80.29 225 | 74.65 218 |
|
testus | | | 63.31 217 | 64.48 215 | 61.94 220 | 73.99 221 | 71.99 222 | 63.56 224 | 63.25 219 | 57.01 218 | 39.41 228 | 54.38 201 | 38.73 229 | 46.24 223 | 77.01 210 | 77.93 205 | 85.20 215 | 74.29 219 |
|
MVS-HIRNet | | | 68.83 207 | 66.39 211 | 71.68 203 | 77.58 211 | 75.52 219 | 66.45 219 | 65.05 215 | 62.16 206 | 62.84 160 | 44.76 220 | 56.60 198 | 71.96 172 | 78.04 208 | 75.06 216 | 86.18 211 | 72.56 220 |
|
test2356 | | | 63.96 213 | 64.10 216 | 63.78 216 | 74.71 219 | 71.55 223 | 65.83 220 | 67.38 204 | 57.11 217 | 40.41 224 | 53.58 205 | 41.13 225 | 49.35 218 | 77.00 211 | 77.57 208 | 85.01 216 | 70.79 221 |
|
testmv | | | 56.62 223 | 56.41 224 | 56.86 223 | 71.92 223 | 67.58 228 | 52.17 230 | 65.69 210 | 40.60 230 | 28.53 233 | 37.90 224 | 31.52 232 | 40.10 227 | 72.64 221 | 74.73 218 | 82.78 222 | 69.91 222 |
|
test1235678 | | | 56.61 224 | 56.40 225 | 56.86 223 | 71.92 223 | 67.58 228 | 52.17 230 | 65.69 210 | 40.58 231 | 28.52 234 | 37.89 225 | 31.49 233 | 40.10 227 | 72.64 221 | 74.72 219 | 82.78 222 | 69.90 223 |
|
new_pmnet | | | 59.28 220 | 61.47 220 | 56.73 225 | 61.66 231 | 68.29 227 | 59.57 226 | 54.91 228 | 60.83 210 | 34.38 231 | 44.66 221 | 43.65 221 | 49.90 217 | 71.66 223 | 71.56 224 | 79.94 226 | 69.67 224 |
|
FPMVS | | | 63.63 216 | 60.08 222 | 67.78 210 | 80.01 203 | 71.50 224 | 72.88 211 | 69.41 199 | 61.82 207 | 53.11 202 | 45.12 218 | 42.11 223 | 50.86 216 | 66.69 226 | 63.84 228 | 80.41 224 | 69.46 225 |
|
PMVS | | 50.48 18 | 55.81 225 | 51.93 226 | 60.33 221 | 72.90 222 | 49.34 235 | 48.78 232 | 69.51 198 | 43.49 229 | 54.25 200 | 36.26 229 | 41.04 226 | 39.71 229 | 65.07 228 | 60.70 229 | 76.85 227 | 67.58 226 |
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010) |
no-one | | | 44.14 228 | 43.91 230 | 44.40 229 | 59.91 232 | 61.10 232 | 34.07 237 | 60.09 224 | 27.71 235 | 14.44 238 | 19.11 233 | 19.28 237 | 23.90 234 | 47.36 233 | 66.69 227 | 73.98 231 | 66.11 227 |
|
1111 | | | 57.32 222 | 57.20 223 | 57.46 222 | 71.89 225 | 67.50 230 | 52.34 228 | 58.78 225 | 46.57 227 | 39.69 225 | 37.38 226 | 38.78 227 | 46.37 221 | 74.15 218 | 74.36 220 | 75.70 229 | 61.66 228 |
|
DeepMVS_CX | | | | | | | 48.31 237 | 48.03 233 | 26.08 235 | 56.42 219 | 25.77 235 | 47.51 213 | 31.31 234 | 51.30 214 | 48.49 232 | | 53.61 234 | 61.52 229 |
|
test12356 | | | 50.02 226 | 51.22 227 | 48.61 227 | 63.00 229 | 60.15 233 | 47.60 234 | 56.49 227 | 38.02 233 | 24.74 236 | 36.14 230 | 25.93 235 | 24.79 232 | 66.19 227 | 71.68 223 | 75.07 230 | 60.44 230 |
|
PMMVS2 | | | 41.68 229 | 44.74 229 | 38.10 230 | 46.97 237 | 52.32 234 | 40.63 236 | 48.08 233 | 35.51 234 | 7.36 241 | 26.86 232 | 24.64 236 | 16.72 235 | 55.24 231 | 59.03 230 | 68.85 233 | 59.59 231 |
|
Gipuma | | | 49.17 227 | 47.05 228 | 51.65 226 | 59.67 233 | 48.39 236 | 41.98 235 | 63.47 218 | 55.64 221 | 33.33 232 | 14.90 234 | 13.78 239 | 41.34 226 | 69.31 224 | 72.30 222 | 70.11 232 | 55.00 232 |
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015 |
MVE | | 30.17 19 | 30.88 232 | 33.52 232 | 27.80 235 | 23.78 239 | 39.16 238 | 18.69 241 | 46.90 234 | 21.88 238 | 15.39 237 | 14.37 236 | 7.31 242 | 24.41 233 | 41.63 234 | 56.22 231 | 37.64 238 | 54.07 233 |
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014) |
E-PMN | | | 31.40 231 | 26.80 233 | 36.78 231 | 51.39 236 | 29.96 239 | 20.20 239 | 54.17 229 | 25.93 237 | 12.75 239 | 14.73 235 | 8.58 241 | 34.10 231 | 27.36 235 | 37.83 233 | 48.07 236 | 43.18 234 |
|
EMVS | | | 30.49 233 | 25.44 234 | 36.39 232 | 51.47 235 | 29.89 240 | 20.17 240 | 54.00 231 | 26.49 236 | 12.02 240 | 13.94 237 | 8.84 240 | 34.37 230 | 25.04 236 | 34.37 234 | 46.29 237 | 39.53 235 |
|
test123 | | | 0.87 235 | 1.40 236 | 0.25 237 | 0.03 242 | 0.25 243 | 0.35 244 | 0.08 239 | 1.21 240 | 0.05 244 | 2.84 239 | 0.03 244 | 0.89 237 | 0.43 238 | 1.16 237 | 0.13 241 | 3.87 236 |
|
.test1245 | | | 41.43 230 | 38.48 231 | 44.88 228 | 71.89 225 | 67.50 230 | 52.34 228 | 58.78 225 | 46.57 227 | 39.69 225 | 37.38 226 | 38.78 227 | 46.37 221 | 74.15 218 | 1.18 235 | 0.20 239 | 3.76 237 |
|
testmvs | | | 1.03 234 | 1.63 235 | 0.34 236 | 0.09 241 | 0.35 242 | 0.61 243 | 0.16 237 | 1.49 239 | 0.10 243 | 3.15 238 | 0.15 243 | 0.86 238 | 1.32 237 | 1.18 235 | 0.20 239 | 3.76 237 |
|
sosnet-low-res | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
sosnet | | | 0.00 236 | 0.00 237 | 0.00 238 | 0.00 243 | 0.00 244 | 0.00 245 | 0.00 240 | 0.00 241 | 0.00 245 | 0.00 240 | 0.00 245 | 0.00 240 | 0.00 239 | 0.00 238 | 0.00 242 | 0.00 239 |
|
our_test_3 | | | | | | 81.81 196 | 83.96 197 | 76.61 203 | | | | | | | | | | |
|
MTAPA | | | | | | | | | | | 92.97 2 | | 91.03 17 | | | | | |
|
MTMP | | | | | | | | | | | 93.14 1 | | 90.21 24 | | | | | |
|
Patchmatch-RL test | | | | | | | | 8.55 242 | | | | | | | | | | |
|
tmp_tt | | | | | 32.73 233 | 43.96 238 | 21.15 241 | 26.71 238 | 8.99 236 | 65.67 194 | 51.39 208 | 56.01 199 | 42.64 222 | 11.76 236 | 56.60 230 | 50.81 232 | 53.55 235 | |
|
XVS | | | | | | 93.11 54 | 96.70 21 | 91.91 49 | | | 83.95 43 | | 88.82 34 | | | | 95.79 29 | |
|
X-MVStestdata | | | | | | 93.11 54 | 96.70 21 | 91.91 49 | | | 83.95 43 | | 88.82 34 | | | | 95.79 29 | |
|
mPP-MVS | | | | | | 97.06 13 | | | | | | | 88.08 39 | | | | | |
|
NP-MVS | | | | | | | | | | 87.47 48 | | | | | | | | |
|
Patchmtry | | | | | | | 85.54 188 | 82.30 172 | 68.23 200 | | 65.37 142 | | | | | | | |
|