  We focus on a fundamental task of detecting meaningful line structures,
a.k.a. semantic line, in natural scenes. Many previous methods regard this
problem as a special case of object detection and adjust existing object
detectors for semantic line detection. However, these methods neglect the
inherent characteristics of lines, leading to sub-optimal performance. Lines
enjoy much simpler geometric property than complex objects and thus can be
compactly parameterized by a few arguments. To better exploit the property of
lines, in this paper, we incorporate the classical Hough transform technique
into deeply learned representations and propose a one-shot end-to-end learning
framework for line detection. By parameterizing lines with slopes and biases,
we perform Hough transform to translate deep representations into the
parametric domain, in which we perform line detection. Specifically, we
aggregate features along candidate lines on the feature map plane and then
assign the aggregated features to corresponding locations in the parametric
domain. Consequently, the problem of detecting semantic lines in the spatial
domain is transformed into spotting individual points in the parametric domain,
making the post-processing steps, i.e. non-maximal suppression, more efficient.
Furthermore, our method makes it easy to extract contextual line features eg
features along lines close to a specific line, that are critical for accurate
line detection. In addition to the proposed method, we design an evaluation
metric to assess the quality of line detection and construct a large scale
dataset for the line detection task. Experimental results on our proposed
dataset and another public dataset demonstrate the advantages of our method
over previous state-of-the-art alternatives.
