Feature Point Matching in Cross-Spectral Images with Cycle Consistency Learning

Feature point matching is an important problem because its applications cover a wide range of tasks in computer vision. Deep learning-based methods for learning local features have recently shown superior performance. However, it is not easy to collect the training data in these methods, especially in cross-spectral settings such as the correspondence between RGB and near-infrared images. In this paper, we propose an unsupervised learning method for general feature point matching. Because we train a convolutional neural network as a feature extractor in order to satisfy the cycle consistency of the correspondences between an input image pair, the proposed method does not require supervision and works even in cross-spectral settings. In our experiments, we apply the proposed method to stereo matching, which is a dense feature point matching problem. The experimental results, which simulate cross-spectral settings with three different settings, i.e., RGB stereo, RGB vs gray-scale, and anaglyph (red vs cyan), show that our proposed method outperforms the compared methods, which employ handcrafted features for stereo matching, by a significant margin.