Learning Attraction Field Representation for Robust Line Segment Detection
Proposes attraction field-based region segmentation for line detection, achieving 4.5% performance gain with end-to-end deep learning.
Key Findings
Methodology
This paper introduces a dual representation combining region-partition maps and attraction field maps for line segment detection. The approach encodes each pixel by its projection vector onto associated line segments, transforming the detection task into a region coloring problem. Using encoder-decoder architectures like U-Net and DeepLab V3+, the network predicts attraction fields directly from raw images. A squeeze module then reconstructs line segments from these fields, enabling end-to-end training. The method addresses local ambiguity and class imbalance by leveraging the duality between regions and boundaries, inspired by semantic segmentation techniques.
Key Results
- On WireFrame dataset, the method improves F-measure by 4.5%, reaching 0.773, outperforming previous state-of-the-art. On YorkUrban dataset, it achieves superior precision and recall, with detection speeds of 6.6-10.4 FPS, suitable for real-time applications.
- The attraction field representation demonstrates scale invariance, with precision above 0.99 and recall around 0.93 across multiple scales, validating robustness.
- End-to-end training with deep networks significantly reduces local ambiguities and class imbalance issues, leading to higher detection accuracy and efficiency.
Significance
This work advances line segment detection by reformulating it as a region coloring problem via attraction fields, enabling the use of semantic segmentation architectures. It effectively handles multi-scale, ambiguous, and cluttered scenes, providing a robust foundation for applications like autonomous driving, scene understanding, and 3D reconstruction. The approach bridges classical geometric methods and modern deep learning, setting a new standard in structural scene analysis.
Technical Contribution
The paper pioneers the dual representation of line segments and attraction fields, integrating it into an end-to-end deep learning framework. It introduces a novel squeeze module for accurate line recovery and validates scale invariance. This combination offers a principled solution to local ambiguity and class imbalance, surpassing existing two-stage detectors in both accuracy and speed.
Novelty
This is the first work to formalize line detection as a region coloring problem using attraction fields, leveraging the duality with region partitioning. The integration of deep neural networks for direct attraction field prediction and the scale-invariant validation constitute key innovations, differentiating it from prior methods that rely on handcrafted features or multi-stage pipelines.
Limitations
- Despite robustness, the method may struggle in scenes with extreme occlusion or highly dense line intersections. The reliance on annotated datasets limits generalization to unseen environments. The compression step may introduce errors in geometrically complex shapes. Further research is needed to enhance adaptability to diverse scene complexities.
Future Work
Future directions include integrating multi-scale features and graph neural networks to improve connectivity in cluttered scenes, exploring unsupervised learning to reduce annotation dependency, and extending the framework to 3D line and boundary detection for volumetric scene understanding.
AI Executive Summary
Line segment detection is a fundamental task in computer vision, underpinning applications from scene reconstruction to autonomous navigation. Traditional approaches, relying on edge detection and geometric heuristics, often falter in complex, noisy environments. Recent deep learning methods have improved robustness but still face challenges like local ambiguity and class imbalance, especially in cluttered scenes. This paper introduces a novel attraction field-based dual representation that transforms line detection into a region coloring problem, inspired by semantic segmentation techniques.
The core idea involves constructing a region-partition map where each pixel belongs to a unique line segment, and an attraction field map encoding each pixel’s projection vector onto its associated line. A lightweight squeeze module then reconstructs line segments from the predicted attraction fields. The entire pipeline is trained end-to-end using encoder-decoder architectures like U-Net and DeepLab V3+. This formulation effectively addresses local ambiguity and class imbalance, leveraging the duality between regions and boundaries.
Experimental results on WireFrame and YorkUrban datasets demonstrate significant performance gains: a 4.5% increase in F-measure on WireFrame, with detection speeds suitable for real-time applications. The method also exhibits strong scale invariance, with precision above 0.99 and recall around 0.93 across multiple scales, confirming robustness.
This approach marks a substantial step forward in line detection, combining geometric insights with deep learning to deliver accurate, efficient, and robust results. It opens avenues for further research into multi-scale, multi-modal scene understanding, and 3D boundary detection, promising broad impact across computer vision and robotics. Despite its strengths, challenges remain in handling occlusion and complex geometries, guiding future efforts towards more adaptive, generalized models.
Deep Analysis
Background
线段检测作为基础视觉任务，经历了从传统边缘检测、几何特征到深度学习的演变。早期方法依赖手工设计特征，如Hough变换和Gabor滤波，效果受噪声影响较大。近年来，深度卷积网络如HED和DeepLab显著提升了边缘和区域检测性能，但在复杂场景中的线段连接和多尺度信息融合仍存难题。现有方法多为两阶段，存在局部模糊和类别不平衡问题，限制了检测精度和鲁棒性。
Core Problem
核心问题在于如何在复杂背景和多尺度环境下准确检测线段。传统方法受限于手工特征，难以应对噪声和遮挡。深度学习虽改善了性能，但多为两阶段，存在局部模糊、类别不平衡和多尺度适应性差等瓶颈。如何设计统一、端到端的模型，解决局部模糊和类别不平衡，成为亟待突破的难题。
Innovation
创新点包括引入区域-引力场双重表示，将线段检测转化为区域着色问题，利用深度网络端到端预测引力场。该表示避免局部模糊，增强尺度不变性。结合编码-解码架构和引力场压缩模块，提升检测精度和速度。首次验证尺度不变性，显著优于传统方法，为复杂场景提供鲁棒解决方案。
Methodology
- �� 构建区域划分图：利用点到线段距离函数，将每个像素分配到唯一线段区域。
- �� 生成引力场图：每个像素编码为其投影向量，指向对应线段上的投影点。
- �� 设计压缩模块：逆向还原线段，通过贪婪增长算法拟合线段，解决局部模糊。
- �� 网络训练：采用U-Net和DeepLab V3+架构，端到端预测引力场，结合多尺度特征增强。
- �� 损失函数：采用L1损失，优化引力场预测精度。
- �� 测试流程：利用压缩模块将引力场映射还原为线段，评估性能。
Experiments
在WireFrame和YorkUrban数据集上进行验证，采用F-measure、精度和召回率指标。训练过程中采用数据增强和多尺度训练，验证尺度不变性。对比现有方法如LSD、Linelet和MCMLSD，评估检测速度和准确率。结果显示，本文方法在性能和速度上均优于对比方法，特别是在复杂场景中表现出更强鲁棒性。
Results
在WireFrame数据集上，F-measure提升至0.773，较基线提高4.5%；在YorkUrban数据集上，检测精度和召回率均优于现有方法。检测速度达6.6-10.4 FPS，满足实时需求。引力场的尺度不变性验证表明模型具有良好的泛化能力。端到端训练显著改善了局部模糊和类别不平衡问题，提升了整体检测性能。
Applications
该方法适用于自动驾驶、场景理解、机器人导航等领域，能提供高精度、鲁棒的线段信息。只需输入原始图像，无需复杂后处理，便可实现实时检测。未来可结合多模态信息，扩展到三维空间的结构检测，推动智能感知技术发展。
Limitations & Outlook
在极端复杂场景（如遮挡严重、线段交叉密集）下仍存在误检和漏检问题。模型对训练数据依赖较大，泛化能力有限。引力场压缩在极端几何形态下可能出现误差，需进一步优化。未来需结合多尺度特征和图神经网络，提升复杂场景适应性。
Plain Language Accessible to non-experts
想象你在厨房里准备一道菜。每次你需要把食材放到锅里，先要知道每个食材的具体位置和方向。传统方法就像用手去摸，容易摸错或遗漏。现在，这个新方法像是给每个食材贴上了标签，告诉你它在哪个区域，朝哪个方向。你只需要看标签，就能快速找到食材，准确放到锅里。这个标签就是引力场，它帮助你把复杂的厨房变得井井有条。通过这种方式，厨师（算法）可以更快、更准地完成任务，不会被厨房的复杂性搞晕。这个方法让机器像人一样聪明，能在复杂环境中找到线段（食材），而不用逐个检查每个细节。
ELI14 Explained like you're 14
想象你在玩一个拼图游戏。每次拼图时，你需要找到每块拼图的边缘，然后把它们拼在一起。以前的方法就像用手慢慢找边缘，很容易拼错或漏掉。现在，这个新方法像是给每个拼图块画上了指示线，告诉你它的边缘在哪里，方向朝哪个方向。这样，你只要看线，就能很快找到拼图块的正确位置，把它拼到正确的地方。这个线就是引力场，它帮你把复杂的拼图变得简单。机器就像你一样，能用这些线快速找到拼图的边缘，拼出完整的图。这样，拼图变得更快、更准，也更有趣！
Abstract
This paper presents a region-partition based attraction field dual representation for line segment maps, and thus poses the problem of line segment detection (LSD) as the region coloring problem. The latter is then addressed by learning deep convolutional neural networks (ConvNets) for accuracy, robustness and efficiency. For a 2D line segment map, our dual representation consists of three components: (i) A region-partition map in which every pixel is assigned to one and only one line segment; (ii) An attraction field map in which every pixel in a partition region is encoded by its 2D projection vector w.r.t. the associated line segment; and (iii) A squeeze module which squashes the attraction field to a line segment map that almost perfectly recovers the input one. By leveraging the duality, we learn ConvNets to compute the attraction field maps for raw in-put images, followed by the squeeze module for LSD, in an end-to-end manner. Our method rigorously addresses several challenges in LSD such as local ambiguity and class imbalance. Our method also harnesses the best practices developed in ConvNets based semantic segmentation methods such as the encoder-decoder architecture and the a-trous convolution. In experiments, our method is tested on the WireFrame dataset and the YorkUrban dataset with state-of-the-art performance obtained. Especially, we advance the performance by 4.5 percents on the WireFrame dataset. Our method is also fast with 6.6~10.4 FPS, outperforming most of existing line segment detectors.