  We present a learnt system for multi-view stereopsis. In contrast to recent
learning based methods for 3D reconstruction, we leverage the underlying 3D
geometry of the problem through feature projection and unprojection along
viewing rays. By formulating these operations in a differentiable manner, we
are able to learn the system end-to-end for the task of metric 3D
reconstruction. End-to-end learning allows us to jointly reason about shape
priors while conforming geometric constraints, enabling reconstruction from
much fewer images (even a single image) than required by classical approaches
as well as completion of unseen surfaces. We thoroughly evaluate our approach
on the ShapeNet dataset and demonstrate the benefits over classical approaches
as well as recent learning based methods.
