# Adaptive Dual-Constrained Line Aggregation for Cross-Paradigm Line Segment Detection

arXiv: https://arxiv.org/abs/2508.19742 · HTML: https://mlsanity.com/paper/2508.19742/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 44 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 25 of 25)

- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [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)
- [Fast Edge Detection Using Structured Forests](https://mlsanity.com/paper/1406.5549/) (2014)
- [Holistically-Attracted Wireframe Parsing](https://mlsanity.com/paper/2003.01663/) (2020)
- [Dilated Neighborhood Attention Transformer](https://mlsanity.com/paper/2209.15001/) (2022)
- [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)
- [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)
- [Dense Extreme Inception Network for Edge Detection](https://mlsanity.com/paper/2112.02250/) (2021)
- [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)
- [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)
- [Learning Regional Attraction for Line Segment Detection](https://mlsanity.com/paper/1912.09344/) (2019)
- [LSDNet: Trainable Modification of LSD Algorithm for Real-Time Line Segment Detection](https://mlsanity.com/paper/2209.04642/) (2022)
- [EdgeNAT: Transformer for Efficient Edge Detection](https://mlsanity.com/paper/2408.10527/) (2024)
- [DT-LSD: Deformable Transformer-based Line Segment Detection](https://mlsanity.com/paper/2411.13005/) (2024)
- [ScaleLSD: Scalable Deep Line Segment Detection Streamlined](https://mlsanity.com/paper/2506.09369/) (2025)
- [LINEA: Fast and Accurate Line Detection Using Scalable Transformers](https://mlsanity.com/paper/2505.16264/) (2025)
- [Improving Transformer Based Line Segment Detection with Matched Predicting and Re-ranking](https://mlsanity.com/paper/2502.17766/) (2025)

## Abstract (arXiv)

Line segment detection has been studied for decades, yet existing methods are typically designed for different detection paradigms. Generic line segment detectors aim to recover all meaningful line segments in an image, whereas recent deep-learning-based approaches mainly target wireframe line segments that describe salient geometric structures. Because these paradigms follow different detection objectives, methods optimized for one often perform poorly on the other. In this work, we propose Adaptive Dual-Constrained Line Aggregation (ADLA), a line extraction framework designed to operate across different line segment detection paradigms. Starting from an edge strength map, ADLA progressively aggregates pixels into candidate line segments under two complementary geometric constraints: orientation coherence and bounded orthogonal distance to an adaptively estimated line model. During aggregation, the line centroid and orientation are dynamically updated using the accumulated supporting pixels, progressively improving the geometric consistency of the estimated line. Edge strength information is further incorporated into orientation estimation, seed selection, model refinement, and segment validation, reducing the need for extensive parameter tuning. Experiments on three publicly available datasets covering generic, wireframe, and Manhattan line segment detection demonstrate consistently strong performance across substantially different annotation settings. ADLA achieves (F^H) scores of 0.8665 on YorkUrban-LineSegment dataset, 0.8720 on ShanghaiTech dataset, and 0.7297 on YorkUrban dataset. These results demonstrate the effectiveness and flexibility of ADLA across different line segment detection paradigms. The source code for this work is publicly available at https://github.com/ChenguangTelecom/adla .