  In computer-aided design (CAD) systems, 2D line drawings are commonly used to
illustrate 3D object designs. To reconstruct the 3D models depicted by a single
2D line drawing, an important key is finding the edge loops in the line drawing
which correspond to the actual faces of the 3D object. In this paper, we
approach the classical problem of face identification from a novel data-driven
point of view. We cast it as a sequence generation problem: starting from an
arbitrary edge, we adopt a variant of the popular Transformer model to predict
the edges associated with the same face in a natural order. This allows us to
avoid searching the space of all possible edge loops with various hand-crafted
rules and heuristics as most existing methods do, deal with challenging cases
such as curved surfaces and nested edge loops, and leverage additional cues
such as face types. We further discuss how possibly imperfect predictions can
be used for 3D object reconstruction.
