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

• URL: http://arxiv.org/abs/2508.19742v1
• Date: Wed, 27 Aug 2025 10:13:46 GMT
• Title: POEv2: a flexible and robust framework for generic line segment detection and wireframe line segment detection
• Authors: Chenguang Liu, Chisheng Wang, Yuhua Cai, Chuanhua Zhu, Qingquan Li,
• Abstract summary: Line segment detectors can be divided into two categories: generic line segment detectors and wireframe line segment detectors.<n>Recent deep learning based approaches are mostly wireframe line segment detectors.<n>We propose a robust framework that can be used for both generic line segment detection and wireframe line segment detection.
• Score: 11.8193704397315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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