  Classical work on line segment detection is knowledge-based; it uses
carefully designed geometric priors using either image gradients, pixel
groupings, or Hough transform variants. Instead, current deep learning methods
do away with all prior knowledge and replace priors by training deep networks
on large manually annotated datasets. Here, we reduce the dependency on labeled
data by building on the classic knowledge-based priors while using deep
networks to learn features. We add line priors through a trainable Hough
transform block into a deep network. Hough transform provides the prior
knowledge about global line parameterizations, while the convolutional layers
can learn the local gradient-like line features. On the Wireframe
(ShanghaiTech) and York Urban datasets we show that adding prior knowledge
improves data efficiency as line priors no longer need to be learned from data.
Keywords: Hough transform; global line prior, line segment detection.
