Implicit Neural Representations (INRs) have recently exhibited immense
potential in the field of scientific visualization for both data generation and
visualization tasks. However, these representations often consist of large
multi-layer perceptrons (MLPs), necessitating millions of operations for a
single forward pass, consequently hindering interactive visual exploration.
While reducing the size of the MLPs and employing efficient parametric encoding
schemes can alleviate this issue, it compromises generalizability for unseen
parameters, rendering it unsuitable for tasks such as temporal
super-resolution. In this paper, we introduce HyperINR, a novel hypernetwork
architecture capable of directly predicting the weights for a compact INR. By
harnessing an ensemble of multiresolution hash encoding units in unison, the
resulting INR attains state-of-the-art inference performance (up to 100x higher
inference bandwidth) and can support interactive photo-realistic volume
visualization. Additionally, by incorporating knowledge distillation,
exceptional data and visualization generation quality is achieved, making our
method valuable for real-time parameter exploration. We validate the
effectiveness of the HyperINR architecture through a comprehensive ablation
study. We showcase the versatility of HyperINR across three distinct scientific
domains: novel view synthesis, temporal super-resolution of volume data, and
volume rendering with dynamic global shadows. By simultaneously achieving
efficiency and generalizability, HyperINR paves the way for applying INR in a
wider array of scientific visualization applications