POEv2: a flexible and robust framework for generic line segment detection and wireframe line segment detection

2025-08-27

AI Summary

Existing line segment detectors are categorized into generic and wireframe-specific variants; their divergent design objectives hinder simultaneous high performance on both tasks. Method: We propose the first unified robust framework enabling a single model to perform both generic and wireframe line segment detection. Building upon pixel-wise orientation estimation (POE), our approach introduces an enhanced directional modeling strategy that leverages edge strength maps to generate high-quality line segments, while remaining compatible with arbitrary edge detectors—thereby improving flexibility and scalability. Contribution/Results: Extensive experiments on three public benchmarks demonstrate that our framework achieves state-of-the-art accuracy and stability across both tasks, effectively bridging the methodological gap between generic and wireframe line segment detection.

Technology Category

• Computer Vision: Segmentation
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Line segment detection in images has been studied for several decades. Existing line segment detectors can be roughly divided into two categories: generic line segment detectors and wireframe line segment detectors. Generic line segment 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 line segment detectors. They detect only line segments that are geometrically meaningful and have large spatial support. Due to the difference in the aim of design, the performance of generic line segment detectors for the task of wireframe line segment detection won't be satisfactory, and vice versa. In this work, we propose a robust framework that can be used for both generic line segment detection and wireframe line segment detection. The proposed method is an improved version of the Pixel Orientation Estimation (POE) method. It is thus named as POEv2. POEv2 detects line segments from edge strength maps, and can be combined with any edge detector. We show in our experiments that by combining the proposed POEv2 with an efficient edge detector, it achieves state-of-the-art performance on three publicly available datasets.

Problem

• Unified framework for both generic and wireframe line detection
• Addresses performance gap between traditional and deep learning detectors
• Enables state-of-the-art detection using any edge detector

Innovation

• Unified framework for both generic and wireframe detection
• Improved Pixel Orientation Estimation method version two
• Works with any edge detector for optimal performance