  We investigate the problem of learning category-specific 3D shape
reconstruction from a variable number of RGB views of previously unobserved
object instances. Most approaches for multiview shape reconstruction operate on
sparse shape representations, or assume a fixed number of views. We present a
method that can estimate dense 3D shape, and aggregate shape across multiple
and varying number of input views. Given a single input view of an object
instance, we propose a representation that encodes the dense shape of the
visible object surface as well as the surface behind line of sight occluded by
the visible surface. When multiple input views are available, the shape
representation is designed to be aggregated into a single 3D shape using an
inexpensive union operation. We train a 2D CNN to learn to predict this
representation from a variable number of views (1 or more). We further
aggregate multiview information by using permutation equivariant layers that
promote order-agnostic view information exchange at the feature level.
Experiments show that our approach is able to produce dense 3D reconstructions
of objects that improve in quality as more views are added.
