  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.
