  In this paper, we propose a learning-based approach to the task of
automatically extracting a "wireframe" representation for images of cluttered
man-made environments. The wireframe (see Fig. 1) contains all salient straight
lines and their junctions of the scene that encode efficiently and accurately
large-scale geometry and object shapes. To this end, we have built a very large
new dataset of over 5,000 images with wireframes thoroughly labelled by humans.
We have proposed two convolutional neural networks that are suitable for
extracting junctions and lines with large spatial support, respectively. The
networks trained on our dataset have achieved significantly better performance
than state-of-the-art methods for junction detection and line segment
detection, respectively. We have conducted extensive experiments to evaluate
quantitatively and qualitatively the wireframes obtained by our method, and
have convincingly shown that effectively and efficiently parsing wireframes for
images of man-made environments is a feasible goal within reach. Such
wireframes could benefit many important visual tasks such as feature
correspondence, 3D reconstruction, vision-based mapping, localization, and
navigation. The data and source code are available at
https://github.com/huangkuns/wireframe.
