Occupancy Networks: Learning 3D Reconstruction in Function Space

Understand

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.