  Recently, implicit neural representations have gained popularity for
learning-based 3D reconstruction. While demonstrating promising results, most
implicit approaches are limited to comparably simple geometry of single objects
and do not scale to more complicated or large-scale scenes. The key limiting
factor of implicit methods is their simple fully-connected network architecture
which does not allow for integrating local information in the observations or
incorporating inductive biases such as translational equivariance. In this
paper, we propose Convolutional Occupancy Networks, a more flexible implicit
representation for detailed reconstruction of objects and 3D scenes. By
combining convolutional encoders with implicit occupancy decoders, our model
incorporates inductive biases, enabling structured reasoning in 3D space. We
investigate the effectiveness of the proposed representation by reconstructing
complex geometry from noisy point clouds and low-resolution voxel
representations. We empirically find that our method enables the fine-grained
implicit 3D reconstruction of single objects, scales to large indoor scenes,
and generalizes well from synthetic to real data.
