Acta Aeronautica et Astronautica Sinica ›› 2024, Vol. 45 ›› Issue (3): 28796-028796.doi: 10.7527/S1000-6893.2023.28796
• Reviews • Previous Articles Next Articles
Haiqiao LIU1, Meng LIU1, Zichao GONG1, Jing DONG2()
Received:
2023-04-03
Revised:
2023-04-24
Accepted:
2023-07-06
Online:
2024-02-15
Published:
2023-07-21
Contact:
Jing DONG
E-mail:dongjing@csu.edu.cn
Supported by:
CLC Number:
Haiqiao LIU, Meng LIU, Zichao GONG, Jing DONG. A review of image matching methods based on deep learning[J]. Acta Aeronautica et Astronautica Sinica, 2024, 45(3): 28796-028796.
Table 1
Comparison of MMA of single⁃link network models on Hpatches dataset
方法 | MMA/% | ||||||||
---|---|---|---|---|---|---|---|---|---|
Viewpoints | Illumination | Overall | |||||||
1@px | 3@px | 5@px | 1@px | 3@px | 5@px | 1@px | 3@px | 5@px | |
SIFT[ | 29 | 54 | 59 | 33 | 54 | 57 | 32 | 53 | 58 |
ORB[ | 13 | 38 | 42 | 29 | 42 | 44 | 21 | 40 | 43 |
SobelNet[ | 68.95 | 79.68 | 74.22 | ||||||
D2D[ | 14 | 62 | 75 | 79 | 80 | 89 | 45 | 71 | 83 |
文献[ | 38 | 65 | 66 | 46 | 73 | 80 | 42 | 70 | 74 |
LIFT[ | 28.4 | 59.8 | 71.7 | ||||||
R2D2[ | 26 | 68 | 78 | 32 | 70 | 83 | 30 | 69 | 80 |
Table 2
Comparison of three error exclusion network model methods
方法 | Saint Peter’s Square | Brown_3 | Reichstag | ||||||
---|---|---|---|---|---|---|---|---|---|
P | R | F1 | P | R | F1 | P | R | F1 | |
RANSAC[ | 0.534 6 | 0.326 3 | 0.394 5 | 0.531 5 | 0.279 7 | 0.351 8 | 0.737 3 | 0.515 9 | 0.599 9 |
LGC[ | 0.806 9 | 0.565 2 | 0.656 7 | 0.689 8 | 0.394 5 | 0.478 7 | 0.913 6 | 0.615 8 | 0.729 6 |
文献[ | 0.828 9 | 0.567 | 0.665 1 | 0.689 8 | 0.392 2 | 0.482 4 | 0.922 7 | 0.613 6 | 0.730 7 |
Table 3
Advantages and limitations of end⁃to⁃end model of a single network fabric
方法 | 优点 | 局限性 |
---|---|---|
SuperPoint[ | ①采用了轻量级的卷积神经网络结构,能够在较短的时间内提取出大量的图像特征 ②采用了模块化的设计,能够很容易地将它应用到不同的图像处理任务中 | 对图像的旋转变化、尺度变化、 遮挡不够鲁棒 |
D2-Net[ | ①鲁棒性强 ②适用性广,不受图像大小、形状、旋转和光照等因素的影响 | 对遮挡和变形敏感 |
BSS-2chDCNN[ | ①鲁棒性强 ②适用性广,不受图像大小、形状、旋转和光照等因素的影响 | 多个超参数需要调整,包括神经网络结构 |
LF-Net[ | ①适用于复杂场景 ②不需要使用手工制作的检测器来生成训练数据 | 仅限于角和边缘的特征 |
RF-Net[ | ①不依赖数据集 ②能生成更有效的比例空间和响应 | 对于复杂场景效果不理想 |
文献[ | ①不依赖数据集 | 对存在显著旋转和尺度变化的多模态复杂数据匹配效果一般 |
文献[ | ①适合于处理遥感图像的复杂特征 ②保留了大尺度遥感场景中每个斑块的纹理完整性,为特征提取提供了适当的邻域信息,提高了特征表示的可靠性 | 样本在训练阶段邻域信息的丢失以及检测的关键点不均匀 |
KCG-GAN[ | ①通过约束空间信息合成来提高合成的图像质量 ②引入了知识约束,将先验知识应用于GAN模型中,从而提高了模型的稳定性和性能 | 需要调整的超参数较多 |
LoFTR[ | ①较好地解决了同名特征点对数不足的情况下对图像的匹配 ②鲁棒性强,用自监督学习方法来训练,可以对输入数据进行自适应的学习 | 训练数据要求高 |
UnsuperPoint[ | ①模型非常轻量级,能够实时运行 ②无监督学习,可以在没有标注数据的情况下使用 | 只能处理二维图像,无法处理三维点云数据等其他形式的数据 |
Table 4
Advantages and limitations of an end⁃to⁃end model for multi⁃network fabric composition
方法 | 优点 | 局限性 |
---|---|---|
MatchNet[ | ①降低了描述符的存储需求 ②可以处理不同尺度的图像 | 可能存在过拟合问题 |
文献[ | ①能够有效地提取SAR和光学图像的特征,实现了更精准的匹配 ②考虑了SAR和光学图像之间的差异性,通过联合训练网络来解决跨域匹配问题,提高了匹配的准确性 | 忽略了图像几何结构信息 |
文献[ | ①匹配精度高、计算速度快 ②对于复杂环境下的图像匹配具有很好的鲁棒性 | 卷积与图神经网络的模型具有很强的黑盒性,难以解释其内部的运行机制和决策过程 |
Table 5
Performance comparison of some classic end⁃to⁃end model image matching methods
方法 | 单应性估计 (HPatches @3px) | 相对姿态估计 (ScanNet @5°) | 视觉定位 (ScanNet Night) | 三维重建 (Dataset:Madrid Metropolis) | 方法复杂度 |
---|---|---|---|---|---|
LoFTR-DS[ | 65.9(AUC) | 22.06(AUC) | 99.0(1.0 m,10°) | 较高 | |
SuperPoint+SuperGlue[ | 53.9(AUC) | 16.16(AUC) | 98.4(1.0 m,10°) | 较低 | |
SuperPoint+NN+OANet[ | 44.55(AUC) | 11.76(AUC) | 较高 | ||
SIFT+SuperGlue[ | 6.71(AUC) | 6.71(AUC) | 较高 | ||
SIFT+NN+OANet[ | 6.00(AUC) | 较高 | |||
SuperPoint[ | 0.684 | 57.1(1.0 m,5°) | 低 | ||
R2D2+NN[ | 98.9(1.0 m,10°) | 高 | |||
D2 MS trained[ | 64.3(1.0 m,5°) | 144K(Sparse.Points) | 高 | ||
RootSIFT[ | 54.1(1.0 m,5°) | 116K(Sparse.Points) | 最高 | ||
SIFT[ | 0.676 | 低 | |||
ORB[ | 0.395 | 低 | |||
ORB+GMS[ | 5.21(AUC) | 较低 |
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Total visits: 6658907 Today visits: 1341