基于重复性和特异性约束的图像特征匹配

Image feature matching based on repeatability and specificity constraints

图像特征匹配通过比较一对像素在特征空间的距离确定其是否可匹配,如何学习鲁棒的像素特征是基于深度学习的图像特征匹配要解决的关键问题之一,另外,像素特征表示的学习也受到源图像质量的影响。针对学习更鲁棒的像素特征表示的问题,对图像特征匹配网络LoFTR进行改进。针对粗粒度特征重构分支,定义特异性约束使得同一幅图像内像素的特征距离尽可能远,使不同像素间具有强区分性;定义重复性约束使得不同图像的匹配点对的特征距离尽可能近,使不同图像间的匹配像素点具有强相似性,以增强匹配的准确性。在Backbone的解码阶段增加图像重建层,定义图像重建损失约束编码器学习更鲁棒的特征表示。在室内数据集ScanNet与室外数据集MegaDepth上的实验结果证明了本文方法的有效性,构建了不同质量图像数据并验证了方法能够更好地适应不同质量图像的特征匹配。

Image feature matching ascertains whether a pair of pixels can be matched by comparing their distance in the feature space.Therefore,how to learn robust pixel features constitutes one of the primary concerns in the field of image feature matching based on deep learning.In addition,the learning of pixel feature representation is also affected by the quality of the source image.As a solution to the challenge of learning more robust pixel feature representations,the proposed method improved the image feature matching network LoFTR.For the coarse granularity feature reconstruction branch,the specificity constraint was defined to maximize the feature distance between pixels within the same image,enabling strong distinguishability between different pixels.The repeatability constraint was defined to minimize the feature distance between the matched pixels from different images,enabling strong similarity between the matched pixels across different images and thus enhancing the accuracy of matching.Additionally,an image reconstruction layer was incorporated into the decoding phase of the Backbone,and image reconstruction loss was defined to constrain the encoder to learn more robust feature representation.The experimental results on indoor dataset ScanNet and outdoor dataset MegeDepth show the effectiveness of the proposed method.Furthermore,based on images with different qualities,it is verified that the proposed method can better adapt to image feature matching when the source images have different quality.