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:直线段在我们的人造世界中无处不在,并越来越多地用于视觉任务。由于它们的空间范围和提供的结构信息,直线段是特征点的有力补充。传统的基于图像梯度的直线检测器非常快速而准确,但在嘈杂的图像和挑战性条件下缺乏稳健性。这些线检测器的学习对应物具有更高的重复性,并且可以处理具有挑战性的图像,但代价是较低的准确性和对线框线的偏向。我们提出了结合传统和学习方法的直线检测方法,以获得最佳结果:一种准确而稳健的直线检测器,可以在缺乏线特征标注的自然图像中进行训练。我们提出了一种新的直线段检测器DeepLSD,它使用深度网络处理图像以生成线的吸引力场,然后将其转换为代理图像梯度幅度和角度,并将其馈送到任何当前的手工直线检测器中。此外,我们提出了一种新的优化工具,基于吸引力场和消失点细化直线段。这种细化方法可以显著提高当前深度检测器的准确性。我们在多个具有挑战性的数据集上展示了我们方法的性能,包括基于低级别线检测度量的结果以及多个下游任务的结果。源代码和模型可在https://github.com/cvg/DeepLSD上获取。