  We introduce Scan2Mesh, a novel data-driven generative approach which
transforms an unstructured and potentially incomplete range scan into a
structured 3D mesh representation. The main contribution of this work is a
generative neural network architecture whose input is a range scan of a 3D
object and whose output is an indexed face set conditioned on the input scan.
In order to generate a 3D mesh as a set of vertices and face indices, the
generative model builds on a series of proxy losses for vertices, edges, and
faces. At each stage, we realize a one-to-one discrete mapping between the
predicted and ground truth data points with a combination of convolutional- and
graph neural network architectures. This enables our algorithm to predict a
compact mesh representation similar to those created through manual artist
effort using 3D modeling software. Our generated mesh results thus produce
sharper, cleaner meshes with a fundamentally different structure from those
generated through implicit functions, a first step in bridging the gap towards
artist-created CAD models.
