  In recent years, Neural Fields (NFs) have emerged as an effective tool for
encoding diverse continuous signals such as images, videos, audio, and 3D
shapes. When applied to 3D data, NFs offer a solution to the fragmentation and
limitations associated with prevalent discrete representations. However, given
that NFs are essentially neural networks, it remains unclear whether and how
they can be seamlessly integrated into deep learning pipelines for solving
downstream tasks. This paper addresses this research problem and introduces
nf2vec, a framework capable of generating a compact latent representation for
an input NF in a single inference pass. We demonstrate that nf2vec effectively
embeds 3D objects represented by the input NFs and showcase how the resulting
embeddings can be employed in deep learning pipelines to successfully address
various tasks, all while processing exclusively NFs. We test this framework on
several NFs used to represent 3D surfaces, such as unsigned/signed distance and
occupancy fields. Moreover, we demonstrate the effectiveness of our approach
with more complex NFs that encompass both geometry and appearance of 3D objects
such as neural radiance fields.
