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.

该论文讨论了神经隐式形状表示的基本概念，比传统的离散表示具有更好的记忆效率和更高的空间分辨率，并提出了一种基于梯度的元学习算法以提高形状空间的泛化能力，从而实现从部分或噪声观测数据中进行的几何重建。该方法在测试时间推断上比自动编码器的方法快一个数量级，同时超过基于编码器 - 解码器的池化网络集合编码器方法。