$\mathbf{F}^{2} \mathbf{R}^{2}$: Frequency Filtering-Based Rectification Robustness Method for Stereo Matching

Most stereo matching networks assume that the stereo images are perfectly rectified, ignoring the perturbation of extrinsic parameters due to collisions, mechanical vibrations, and thermal expansion. This leads to poor rectification robustness in real-world stereo systems. That is, even minor rectification errors can lead to failure, making stereo systems unreliable for long-term autonomous operation in complex environments. In this paper, we are the first to propose a frequency filtering-based rectification robustness ($\mathbf{F}^{2} \mathbf{R}^{2}$) method for stereo matching, which aims to enhance the robustness of existing stereo networks to rectification errors. Specifically, we propose a sensitive frequency filter (SFF) to remove components susceptible to rectification errors within the frequency domain. SFF achieves the filtering through the learning-based adaptive filtering mask (AFM) guided by the spatial-frequency mapping modulation mask (SFM). Moreover, we build the matching feature reconstruction module (MFRM) to recover the features lost during filtering to benefit cost aggregation. Comprehensive experiments on simulated datasets and self-collected data validate that our method can significantly enhance the rectification robustness of stereo matching networks.