  We introduce NeuralVDB, which improves on an existing industry standard for
efficient storage of sparse volumetric data, denoted VDB [Museth 2013], by
leveraging recent advancements in machine learning. Our novel hybrid data
structure can reduce the memory footprints of VDB volumes by orders of
magnitude, while maintaining its flexibility and only incurring small
(user-controlled) compression errors. Specifically, NeuralVDB replaces the
lower nodes of a shallow and wide VDB tree structure with multiple hierarchical
neural networks that separately encode topology and value information by means
of neural classifiers and regressors respectively. This approach is proven to
maximize the compression ratio while maintaining the spatial adaptivity offered
by the higher-level VDB data structure. For sparse signed distance fields and
density volumes, we have observed compression ratios on the order of 10x to
more than 100x from already compressed VDB inputs, with little to no visual
artifacts. Furthermore, NeuralVDB is shown to offer more effective compression
performance compared to other neural representations such as Neural Geometric
Level of Detail [Takikawa et al. 2021], Variable Bitrate Neural Fields
[Takikawa et al. 2022a], and Instant Neural Graphics Primitives [M\"uller et
al. 2022]. Finally, we demonstrate how warm-starting from previous frames can
accelerate training, i.e., compression, of animated volumes as well as improve
temporal coherency of model inference, i.e., decompression.
