  Implicit Neural Representations (INRs) have emerged in the last few years as
a powerful tool to encode continuously a variety of different signals like
images, videos, audio and 3D shapes. When applied to 3D shapes, INRs allow to
overcome the fragmentation and shortcomings of the popular discrete
representations used so far. Yet, considering that INRs consist in neural
networks, it is not clear whether and how it may be possible to feed them into
deep learning pipelines aimed at solving a downstream task. In this paper, we
put forward this research problem and propose inr2vec, a framework that can
compute a compact latent representation for an input INR in a single inference
pass. We verify that inr2vec can embed effectively the 3D shapes represented by
the input INRs and show how the produced embeddings can be fed into deep
learning pipelines to solve several tasks by processing exclusively INRs.
