  We consider the problem of planning views for a robot to acquire images of an
object for visual inspection and reconstruction. In contrast to offline methods
which require a 3D model of the object as input or online methods which rely on
only local measurements, our method uses a neural network which encodes shape
information for a large number of objects. We build on recent deep learning
methods capable of generating a complete 3D reconstruction of an object from a
single image. Specifically, in this work, we extend a recent method which uses
Higher Order Functions (HOF) to represent the shape of the object. We present a
new generalization of this method to incorporate multiple images as input and
establish a connection between visibility and reconstruction quality. This
relationship forms the foundation of our view planning method where we compute
viewpoints to visually cover the output of the multi-view HOF network with as
few images as possible. Experiments indicate that our method provides a good
compromise between online and offline methods: Similar to online methods, our
method does not require the true object model as input. In terms of number of
views, it is much more efficient. In most cases, its performance is comparable
to the optimal offline case even on object classes the network has not been
trained on.
