Abstract
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 ef-
ficient yet allows for representing high-resolution geometry
of arbitrary topology. Many of the state-of-the-art learningbased 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.