  The common trade-offs of state-of-the-art methods for multi-shape
representation (a single model "packing" multiple objects) involve trading
modeling accuracy against memory and storage. We show how to encode multiple
shapes represented as continuous neural fields with a higher degree of
precision than previously possible and with low memory usage. Key to our
approach is a recursive hierarchical formulation that exploits object
self-similarity, leading to a highly compressed and efficient shape latent
space. Thanks to the recursive formulation, our method supports spatial and
global-to-local latent feature fusion without needing to initialize and
maintain auxiliary data structures, while still allowing for continuous field
queries to enable applications such as raytracing. In experiments on a set of
diverse datasets, we provide compelling qualitative results and demonstrate
state-of-the-art multi-scene reconstruction and compression results with a
single network per dataset.
