  In this paper, we revisit the long-standing problem of automatic
reconstruction of 3D objects from single line drawings. Previous
optimization-based methods can generate compact and accurate 3D models, but
their success rates depend heavily on the ability to (i) identifying a
sufficient set of true geometric constraints, and (ii) choosing a good initial
value for the numerical optimization. In view of these challenges, we propose
to train deep neural networks to detect pairwise relationships among geometric
entities (i.e., edges) in the 3D object, and to predict initial depth value of
the vertices. Our experiments on a large dataset of CAD models show that, by
leveraging deep learning in a geometric constraint solving pipeline, the
success rate of optimization-based 3D reconstruction can be significantly
improved.

Computer Vision and Pattern Recognition