# Level-line Guided Edge Drawing for Robust Line Segment Detection

arXiv: https://arxiv.org/abs/2305.05883 · HTML: https://mlsanity.com/paper/2305.05883/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 8 of 8)

- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [HPatches: A benchmark and evaluation of handcrafted and learned local descriptors](https://mlsanity.com/paper/1704.05939/) (2017)
- [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)
- [Deep Hough-Transform Line Priors](https://mlsanity.com/paper/2007.09493/) (2020)
- [Towards Light-weight and Real-time Line Segment Detection](https://mlsanity.com/paper/2106.00186/) (2021)
- [Fully Convolutional Line Parsing](https://mlsanity.com/paper/2104.11207/) (2021)
- [ELSED: Enhanced Line SEgment Drawing](https://mlsanity.com/paper/2108.03144/) (2021)

## Built on by (most cited first, 1 of 1)

- [Aligned Anchor Groups Guided Line Segment Detector](https://mlsanity.com/paper/2509.00786/) (2025)

## Abstract (arXiv)

Line segment detection plays a cornerstone role in computer vision tasks. Among numerous detection methods that have been recently proposed, the ones based on edge drawing attract increasing attention owing to their excellent detection efficiency. However, the existing methods are not robust enough due to the inadequate usage of image gradients for edge drawing and line segment fitting. Based on the observation that the line segments should locate on the edge points with both consistent coordinates and level-line information, i.e., the unit vector perpendicular to the gradient orientation, this paper proposes a level-line guided edge drawing for robust line segment detection (GEDRLSD). The level-line information provides potential directions for edge tracking, which could be served as a guideline for accurate edge drawing. Additionally, the level-line information is fused in line segment fitting to improve the robustness. Numerical experiments show the superiority of the proposed GEDRLSD algorithm compared with state-of-the-art methods.