Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness in noisy images and challenging conditions. Their learned counterparts are more repeatable and can handle challenging images, but at the cost of a lower accuracy and a bias towards wireframe lines. We propose to combine traditional and learned approaches to get the best of both worlds: an accurate and robust line detector that can be trained in the wild without ground truth lines. Our new line segment detector, DeepLSD, processes images with a deep network to generate a line attraction field, before converting it to a surrogate image gradient magnitude and angle, which is then fed to any existing handcrafted line detector. Additionally, we propose a new optimization tool to refine line segments based on the attraction field and vanishing points. This refinement improves the accuracy of current deep detectors by a large margin. We demonstrate the performance of our method on low-level line detection metrics, as well as on several downstream tasks using multiple challenging datasets. The source code and models are available at https://github.com/cvg/DeepLSD.

翻译：线段在人造世界中无处不在，并日益广泛应用于视觉任务中。凭借其空间延展性和所蕴含的结构信息，线段与特征点形成互补。基于图像梯度的传统线段检测器速度极快、精度高，但在噪声图像和复杂条件下缺乏鲁棒性；而基于学习的检测器可重复性更强且能处理复杂图像，但代价是精度较低且存在偏向于线框结构的偏差。我们提出融合传统方法与学习方法，以兼顾两者优势：开发一种无需真实线段标注即可在自然场景中训练的精确鲁棒线段检测器。我们提出的新型线段检测器DeepLSD首先通过深度网络处理图像生成线段吸引力场，随后将其转换为代理图像梯度幅值与角度，再输入任意现有手工设计的线段检测器。此外，我们提出一种基于吸引力场与消失点优化线段的新工具，该精化方法能大幅提升现有深度检测器的精度。我们在低层级线段检测指标及多项下游任务中，使用多个具有挑战性的数据集验证了本方法性能。源代码和模型已开源至https://github.com/cvg/DeepLSD。