# Detecting Line Segments in Motion-blurred Images with Events

arXiv: https://arxiv.org/abs/2211.07365 · HTML: https://mlsanity.com/paper/2211.07365/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 58 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation](https://mlsanity.com/paper/1712.00080/) (2017)
- [CMT: Convolutional Neural Networks Meet Vision Transformers](https://mlsanity.com/paper/2107.06263/) (2021)
- [Unsupervised Event-based Learning of Optical Flow, Depth, and Egomotion](https://mlsanity.com/paper/1812.08156/) (2018)
- [End-to-End Learning of Representations for Asynchronous Event-Based Data](https://mlsanity.com/paper/1904.08245/) (2019)
- [Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Cars](https://mlsanity.com/paper/1804.01310/) (2018)
- [TimeLens: Event-based Video Frame Interpolation](https://mlsanity.com/paper/2106.07286/) (2021)
- [Line Segment Detection Using Transformers without Edges](https://mlsanity.com/paper/2101.01909/) (2021)
- [End-to-End Wireframe Parsing](https://mlsanity.com/paper/1905.03246/) (2019)
- [Learning to Parse Wireframes in Images of Man-Made Environments](https://mlsanity.com/paper/2007.07527/) (2020)
- [Holistically-Attracted Wireframe Parsing](https://mlsanity.com/paper/2003.01663/) (2020)
- [PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments](https://mlsanity.com/paper/1705.09479/) (2017)
- [Learning to Extract a Video Sequence from a Single Motion-Blurred Image](https://mlsanity.com/paper/1804.04065/) (2018)
- [Motion Deblurring with Real Events](https://mlsanity.com/paper/2109.13695/) (2021)
- [SOLD2: Self-supervised Occlusion-aware Line Description and Detection](https://mlsanity.com/paper/2104.03362/) (2021)
- [Bringing Alive Blurred Moments](https://mlsanity.com/paper/1804.02913/) (2018)
- [Deep Hough-Transform Line Priors](https://mlsanity.com/paper/2007.09493/) (2020)
- [PPGNet: Learning Point-Pair Graph for Line Segment Detection](https://mlsanity.com/paper/1905.03415/) (2019)
- [LGNN: A Context-aware Line Segment Detector](https://mlsanity.com/paper/2008.05892/) (2020)
- [Learning Regional Attraction for Line Segment Detection](https://mlsanity.com/paper/1912.09344/) (2019)
- [ULSD: Unified Line Segment Detection across Pinhole, Fisheye, and Spherical Cameras](https://mlsanity.com/paper/2011.03174/) (2020)
- [Powerline Tracking with Event Cameras](https://mlsanity.com/paper/2108.00515/) (2021)
- [Hough2Map -- Iterative Event-based Hough Transform for High-Speed Railway Mapping](https://mlsanity.com/paper/2102.08145/) (2021)
- [LDNet: End-to-End Lane Marking Detection Approach Using a Dynamic Vision Sensor](https://mlsanity.com/paper/2009.08020/) (2020)

## Built on by (most cited first, 6 of 6)

- [Frequency-Adaptive Low-Latency Object Detection Using Events and Frames](https://mlsanity.com/paper/2412.04149/) (2024)
- [A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges](https://mlsanity.com/paper/2305.00264/) (2023)
- [Tracking Any Point with Frame-Event Fusion Network at High Frame Rate](https://mlsanity.com/paper/2409.11953/) (2024)
- [Lattice-allocated Real-time Line Segment Feature Detection and Tracking Using Only an Event-based Camera](https://mlsanity.com/paper/2510.06829/) (2025)
- [TAPFormer: Robust Arbitrary Point Tracking via Transient Asynchronous Fusion of Frames and Events](https://mlsanity.com/paper/2603.04989/) (2026)
- [Ev-Layout: A Large-scale Event-based Multi-modal Dataset for Indoor Layout Estimation and Tracking](https://mlsanity.com/paper/2503.08370/) (2025)

## Abstract (arXiv)

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