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，并越来越多地应用于视觉任务。由于其空间延伸性和提供的结构信息，线段与特征点互为补充。基于图像梯度的传统线段检测器速度极快且精度高，但在噪声图像和具有挑战性的条件下缺乏鲁棒性。基于学习的检测器可重复性更高且能处理复杂图像，但代价是精度较低且偏向于线框线段。我们提出将传统方法与学习方法相结合，以兼顾两者优势：一种无需真实线段标注即可在真实环境中训练的精确且鲁棒的线段检测器。我们的新型线段检测器DeepLSD通过深度网络处理图像生成线段吸引场，然后将其转换为代理图像梯度幅度和角度，再输入任意现有的手工设计线段检测器。此外，我们提出一种基于吸引场和消失点优化线段的新工具，该细化方法大幅提升了现有深度检测器的精度。我们在底层线段检测指标以及多个下游任务中，使用多个具有挑战性的数据集验证了所提方法的性能。源代码和模型已开源至https://github.com/cvg/DeepLSD。