Learned volumetric attribute compression using coordinate-based networks

Example embodiments of the present disclosure relate to systems and methods for compressing attributes of volumetric and hypervolumetric datasets. An example system performs operations including obtaining a reference dataset comprising attributes indexed by a domain of multidimensional coordinates; subdividing the domain into a plurality of blocks respectively associated with a plurality of attribute subsets; inputting, to a local nonlinear operator, a latent representation for an attribute subset associated with at least one block of the plurality of blocks; obtaining, using the local nonlinear operator and based on the latent representation, an attribute representation of one or more attributes of the attribute subset; and updating the latent representation based on a comparison of the attribute representation and the reference dataset.