DRKF: Distilled Rotated Kernel Fusion for Efficiently Boosting Rotation Invariance in Image Matching

Most existing learning-based image matching pipelines are designed for better feature detectors and descriptors which are robust to repeated textures, viewpoint changes, etc., while little attention has been paid to rotation invariance. As a consequence, these approaches usually demonstrate inferior performance compared to the handcrafted algorithms in circumstances where a significant level of rotation exists in data, due to the lack of keypoint orientation prediction. To address the issue efficiently, an approach based on knowledge distillation is proposed for improving rotation robustness without extra computational costs. Specifically, based on the base model, we propose Multi-Oriented Feature Aggregation (MOFA), which is subsequently adopted as the teacher in the distillation pipeline. Moreover, Rotated Kernel Fusion (RKF) is applied to each convolution kernel of the student model to facilitate learning rotation-invariant features. Eventually, experiments show that our proposals can generalize successfully under various rotations without additional costs in the inference stage.