  "Wireframe" is a line segment based representation designed to well capture
large-scale visual properties of regular, structural shaped man-made scenes
surrounding us. Unlike the wireframes, conventional edges or line segments
focus on all visible edges and lines without particularly distinguishing which
of them are more salient to man-made structural information. Existing wireframe
detection models rely on supervising the annotated data but do not explicitly
pay attention to understand how to compose the structural shapes of the scene.
In addition, we often face that many foreground objects occluding the
background scene interfere with proper inference of the full scene structure
behind them. To resolve these problems, we first time in the field, propose new
conditional data generation and training that help the model understand how to
ignore occlusion indicated by holes, such as foreground object regions masked
out on the image. In addition, we first time combine GAN in the model to let
the model better predict underlying scene structure even beyond large holes. We
also introduce pseudo labeling to further enlarge the model capacity to
overcome small-scale labeled data. We show qualitatively and quantitatively
that our approach significantly outperforms previous works unable to handle
holes, as well as improves ordinary detection without holes given.
