  Neural implicit shape representations are an emerging paradigm that offers
many potential benefits over conventional discrete representations, including
memory efficiency at a high spatial resolution. Generalizing across shapes with
such neural implicit representations amounts to learning priors over the
respective function space and enables geometry reconstruction from partial or
noisy observations. Existing generalization methods rely on conditioning a
neural network on a low-dimensional latent code that is either regressed by an
encoder or jointly optimized in the auto-decoder framework. Here, we formalize
learning of a shape space as a meta-learning problem and leverage
gradient-based meta-learning algorithms to solve this task. We demonstrate that
this approach performs on par with auto-decoder based approaches while being an
order of magnitude faster at test-time inference. We further demonstrate that
the proposed gradient-based method outperforms encoder-decoder based methods
that leverage pooling-based set encoders.
