BlurPoint: Efficient Motion Blur Aware Student-Teacher Local Feature Learning

Abstract

Local feature detection and description serve as the foundation for many 3D vision tasks. However, most existing algorithms rely on sharp images, resulting in degraded performance when motion blur occurs due to long exposure. To tackle this challenge, we propose an effective end-to-end model that jointly learns feature detection and description from blurred images in a self-supervised manner, without requiring any additional labeled data. Rather than simply mixing sharp and blurred samples during training, we design a student–teacher framework to explicitly transfer knowledge from sharp to blurred domains. The teacher model extracts local features from sharp images and enforces photometric consistency in feature space, which is then distilled to the student model trained on blurred inputs. To facilitate this knowledge transfer, we introduce two tailored loss functions, feature divergence loss and triplet knowledge distillation loss, both aimed at aligning feature representations under motion blur. Extensive experiments on homography estimation, relative pose estimation, and visual localization demonstrate that our method achieves state-of-the-art performance on blurred images, while maintaining competitive accuracy on sharp images.