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.

通过递归分层表达和利用对象的自相似性，我们提出了一种对多个形状进行编码的方法，能够以比以前更高的精度和低内存使用量来表示连续神经场。我们的方法支持空间和全局到局部潜在特征融合，并能够进行连续场查询，从而支持射线追踪等应用，并在多个数据集上进行实验证明了具有强大定性结果的多场景重建和压缩效果。