This article presents Holistically-Attracted Wireframe Parsing (HAWP), a method for geometric analysis of 2D images containing wireframes formed by line segments and junctions. HAWP utilizes a parsimonious Holistic Attraction (HAT) field representation that encodes line segments using a closed-form 4D geometric vector field. The proposed HAWP consists of three sequential components empowered by end-to-end and HAT-driven designs: (1) generating a dense set of line segments from HAT fields and endpoint proposals from heatmaps, (2) binding the dense line segments to sparse endpoint proposals to produce initial wireframes, and (3) filtering false positive proposals through a novel endpoint-decoupled line-of-interest aligning (EPD LOIAlign) module that captures the co-occurrence between endpoint proposals and HAT fields for better verification. Thanks to our novel designs, HAWPv2 shows strong performance in fully supervised learning, while HAWPv3 excels in self-supervised learning, achieving superior repeatability scores and efficient training (24 GPU hours on a single GPU). Furthermore, HAWPv3 exhibits a promising potential for wireframe parsing in out-of-distribution images without providing ground truth labels of wireframes.

翻译：本文提出了整体吸引式线框解析（HAWP），一种用于包含线段和交点构成的线框的二维图像几何分析方法。HAWP采用简洁的整体吸引（HAT）场表示，通过闭合形式的四维几何向量场编码线段。所提出的HAWP由三个依次连接的组件构成，这些组件基于端到端和HAT驱动设计：（1）从HAT场生成密集线段集合，并从热力图生成端点提议；（2）将密集线段绑定到稀疏端点提议以生成初始线框；（3）通过新颖的端点解耦感兴趣线段对齐（EPD LOIAlign）模块过滤假阳性提议，该模块捕捉端点提议与HAT场之间的共现性以提升验证效果。得益于新颖设计，HAWPv2在全监督学习中表现卓越，而HAWPv3在自监督学习中表现优异，实现了高重复性得分和高效训练（单GPU 24 GPU小时）。此外，HAWPv3在无需提供线框真实标签的情况下，展现出对分布外图像进行线框解析的潜力。