Motion-Feat: Motion Blur-Aware Local Feature Description for Image Matching

Abstract

Local feature description is crucial for robotic tasks, yet existing methods struggle with motion blur, a prevalent challenge in high-dynamic and low-light environments. While effective on sharp images, they suffer significant degradation under blur. To address this issue, we propose Motion-Feat, an end-to-end motion blur-aware feature description method. Our approach introduces a Motion Deformable Block (MDB) that adaptively adjusts the receptive field based on pixel-wise motion information at different stages of the network, enhancing multi-scale feature descriptor robustness in blurred conditions. Additionally, we construct synthetic blurred datasets to systematically benchmark feature matching performance across varying blur intensities. Extensive experiments demonstrate that Motion-Feat outperforms state-of-the-art methods on blurred images while maintaining competitive performance on sharp images for relative camera pose estimation and homography estimation tasks. Both code and datasets are available at https://github.com/AndreGao08/Motion-Feat.