  In this paper, we present a novel framework to detect line segments in
man-made environments. Specifically, we propose to describe junctions, line
segments and relationships between them with a simple graph, which is more
structured and informative than end-point representation used in existing line
segment detection methods. In order to extract a line segment graph from an
image, we further introduce the PPGNet, a convolutional neural network that
directly infers a graph from an image. We evaluate our method on published
benchmarks including York Urban and Wireframe datasets. The results demonstrate
that our method achieves satisfactory performance and generalizes well on all
the benchmarks. The source code of our work is available at
\url{https://github.com/svip-lab/PPGNet}.
