# A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges arXiv: https://arxiv.org/abs/2305.00264 ยท HTML: https://mlsanity.com/paper/2305.00264/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Builds on 42 of its 213 references that are in the map. - First posted on arXiv 29 Apr 2023. ## Builds on (most cited first, 30 of 42) - [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017) - [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016) - [HPatches: A benchmark and evaluation of handcrafted and learned local descriptors](https://mlsanity.com/paper/1704.05939/) (2017) - [Working hard to know your neighbor's margins: Local descriptor learning loss](https://mlsanity.com/paper/1705.10872/) (2017) - [R2D2: Repeatable and Reliable Detector and Descriptor](https://mlsanity.com/paper/1906.06195/) (2019) - [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) - [GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://mlsanity.com/paper/2304.02008/) (2023) - [Fast Edge Detection Using Structured Forests](https://mlsanity.com/paper/1406.5549/) (2014) - [Holistically-Attracted Wireframe Parsing](https://mlsanity.com/paper/2003.01663/) (2020) - [DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients](https://mlsanity.com/paper/2212.07766/) (2022) - [Learning Attraction Field Representation for Robust Line Segment Detection](https://mlsanity.com/paper/1812.02122/) (2018) - [SOLD2: Self-supervised Occlusion-aware Line Description and Detection](https://mlsanity.com/paper/2104.03362/) (2021) - [Deep Hough-Transform Line Priors](https://mlsanity.com/paper/2007.09493/) (2020) - [Online Invariance Selection for Local Feature Descriptors](https://mlsanity.com/paper/2007.08988/) (2020) - [PPGNet: Learning Point-Pair Graph for Line Segment Detection](https://mlsanity.com/paper/1905.03415/) (2019) - [Learning to Reconstruct 3D Manhattan Wireframes from a Single Image](https://mlsanity.com/paper/1905.07482/) (2019) - [Towards Light-weight and Real-time Line Segment Detection](https://mlsanity.com/paper/2106.00186/) (2021) - [TP-LSD: Tri-Points Based Line Segment Detector](https://mlsanity.com/paper/2009.05505/) (2020) - [Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning](https://mlsanity.com/paper/2210.12971/) (2022) - [Fully Convolutional Line Parsing](https://mlsanity.com/paper/2104.11207/) (2021) - [Learning to Calibrate Straight Lines for Fisheye Image Rectification](https://mlsanity.com/paper/1904.09856/) (2019) - [ELSED: Enhanced Line SEgment Drawing](https://mlsanity.com/paper/2108.03144/) (2021) - [ELSD: Efficient Line Segment Detector and Descriptor](https://mlsanity.com/paper/2104.14205/) (2021) - [LGNN: A Context-aware Line Segment Detector](https://mlsanity.com/paper/2008.05892/) (2020) - [Line as a Visual Sentence: Context-aware Line Descriptor for Visual Localization](https://mlsanity.com/paper/2109.04753/) (2021) - [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) - [LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection](https://mlsanity.com/paper/2209.04642/) (2022) ## Built on by (most cited first, 6 of 6) - [MeasureNet: Measurement Based Celiac Disease Identification](https://mlsanity.com/paper/2412.01182/) (2024) - [GraphEnet: Event-driven Human Pose Estimation with a Graph Neural Network](https://mlsanity.com/paper/2510.07990/) (2025) - [Linear Anchored Gaussian Mixture Model for Location and Width Computations of Objects in Thick Line Shape](https://mlsanity.com/paper/2404.03043/) (2024) - [Tackling Snow-Induced Challenges: Safe Autonomous Lane-Keeping with Robust Reinforcement Learning](https://mlsanity.com/paper/2512.12987/) (2025) - [Event-driven dynamic trajectories reconstruction and measurement of mechanical parameters for fragments](https://mlsanity.com/paper/2606.09208/) (2026) - [SweepLSD: A One-Pass, O(width)-Memory Line Segment Detector with an Integer-Only Streaming Core and a Real-Time FPGA Realization](https://mlsanity.com/paper/2608.22086/) (2026) ## Abstract (arXiv) An image line segment is a fundamental low-level visual feature that delineates straight, slender, and uninterrupted portions of objects and scenarios within images. Detection and description of line segments lay the basis for numerous vision tasks. Although many studies have aimed to detect and describe line segments, a comprehensive review is lacking, obstructing their progress. This study fills the gap by comprehensively reviewing related studies on detecting and describing two-dimensional image line segments to provide researchers with an overall picture and deep understanding. Based on their mechanisms, two taxonomies for line segment detection and description are presented to introduce, analyze, and summarize these studies, facilitating researchers to learn about them quickly and extensively. The key issues, core ideas, advantages and disadvantages of existing methods, and their potential applications for each category are analyzed and summarized, including previously unknown findings. The challenges in existing methods and corresponding insights for potentially solving them are also provided to inspire researchers. In addition, some state-of-the-art line segment detection and description algorithms are evaluated without bias, and the evaluation code will be publicly available. The theoretical analysis, coupled with the experimental results, can guide researchers in selecting the best method for their intended vision applications. Finally, this study provides insights for potentially interesting future research directions to attract more attention from researchers to this field.