  We introduce PC2WF, the first end-to-end trainable deep network architecture
to convert a 3D point cloud into a wireframe model. The network takes as input
an unordered set of 3D points sampled from the surface of some object, and
outputs a wireframe of that object, i.e., a sparse set of corner points linked
by line segments. Recovering the wireframe is a challenging task, where the
numbers of both vertices and edges are different for every instance, and
a-priori unknown. Our architecture gradually builds up the model: It starts by
encoding the points into feature vectors. Based on those features, it
identifies a pool of candidate vertices, then prunes those candidates to a
final set of corner vertices and refines their locations. Next, the corners are
linked with an exhaustive set of candidate edges, which is again pruned to
obtain the final wireframe. All steps are trainable, and errors can be
backpropagated through the entire sequence. We validate the proposed model on a
publicly available synthetic dataset, for which the ground truth wireframes are
accessible, as well as on a new real-world dataset. Our model produces
wireframe abstractions of good quality and outperforms several baselines.
