Adaptive Dual-Constrained Line Aggregation for Robust Generic and Wireframe Line Segment Detection

TL;DR

This paper introduces ADLA, a robust and efficient framework for line segment detection that works well for both generic and wireframe tasks by adaptively aggregating pixels based on dual geometric constraints.

Contribution

The paper proposes the Adaptive Dual-Constrained Line Aggregation (ADLA) algorithm, which dynamically refines line models and unifies generic and wireframe line detection tasks.

Findings

ADLA achieves superior performance on multiple datasets.

It demonstrates robustness across different line detection paradigms.

Requires minimal parameter tuning due to edge strength integration.

Abstract

Line segment detection in images has been studied for several decades. Existing methods can be roughly divided into two categories: generic line segment detectors and wireframe line segment detectors. Generic detectors aim to detect all meaningful line segments in images and traditional approaches usually fall into this category. Recent deep learning based approaches are mostly wireframe detectors. They detect only line segments that are geometrically meaningful and have large spatial support. Due to the difference in the aim of design, methods designed for one paradigm often perform poorly on the other, and few approaches demonstrate robust performance across both tasks. In this work, we propose a robust framework that is efficient for both tasks based on an Adaptive Dual-Constrained Line Aggregation (ADLA) algorithm. ADLA aggregates pixels into candidate line segments only if they satisfy dual geometric constraints: (1) orientation coherence and (2) bounded orthogonal distance to an adaptively estimated line model. Crucially, the parameters of the candidate line (its orientation and centroid) are dynamically updated as new pixels are incorporated. This progressive model refinement improves geometric accuracy. Moreover, by leveraging edge strength maps in orientation estimation and line segment validation, ADLA requires little parameter tuning. Extensive experiments on three publicly available datasets demonstrate that ADLA achieves competitive or superior performance than previous methods, highlighting its robustness, versatility, and practical usability.