  Making line segment detectors more reliable under motion blurs is one of the
most important challenges for practical applications, such as visual SLAM and
3D reconstruction. Existing line segment detection methods face severe
performance degradation for accurately detecting and locating line segments
when motion blur occurs. While event data shows strong complementary
characteristics to images for minimal blur and edge awareness at high-temporal
resolution, potentially beneficial for reliable line segment recognition. To
robustly detect line segments over motion blurs, we propose to leverage the
complementary information of images and events. To achieve this, we first
design a general frame-event feature fusion network to extract and fuse the
detailed image textures and low-latency event edges, which consists of a
channel-attention-based shallow fusion module and a self-attention-based dual
hourglass module. We then utilize two state-of-the-art wireframe parsing
networks to detect line segments on the fused feature map. Besides, we
contribute a synthetic and a realistic dataset for line segment detection,
i.e., FE-Wireframe and FE-Blurframe, with pairwise motion-blurred images and
events. Extensive experiments on both datasets demonstrate the effectiveness of
the proposed method. When tested on the real dataset, our method achieves 63.3%
mean structural average precision (msAP) with the model pre-trained on the
FE-Wireframe and fine-tuned on the FE-Blurframe, improved by 32.6 and 11.3
points compared with models trained on synthetic only and real only,
respectively. The codes, datasets, and trained models are released at:
https://levenberg.github.io/FE-LSD
