  With the advent of deep neural networks, learning-based approaches for 3D
reconstruction have gained popularity. However, unlike for images, in 3D there
is no canonical representation which is both computationally and memory
efficient yet allows for representing high-resolution geometry of arbitrary
topology. Many of the state-of-the-art learning-based 3D reconstruction
approaches can hence only represent very coarse 3D geometry or are limited to a
restricted domain. In this paper, we propose Occupancy Networks, a new
representation for learning-based 3D reconstruction methods. Occupancy networks
implicitly represent the 3D surface as the continuous decision boundary of a
deep neural network classifier. In contrast to existing approaches, our
representation encodes a description of the 3D output at infinite resolution
without excessive memory footprint. We validate that our representation can
efficiently encode 3D structure and can be inferred from various kinds of
input. Our experiments demonstrate competitive results, both qualitatively and
quantitatively, for the challenging tasks of 3D reconstruction from single
images, noisy point clouds and coarse discrete voxel grids. We believe that
occupancy networks will become a useful tool in a wide variety of
learning-based 3D tasks.
