  Considering the challenges posed by the space and time complexities in
handling extensive scientific volumetric data, various data representations
have been developed for the analysis of large-scale scientific data.
Multivariate functional approximation (MFA) is an innovative data model
designed to tackle substantial challenges in scientific data analysis. It
computes values and derivatives with high-order accuracy throughout the spatial
domain, mitigating artifacts associated with zero- or first-order
interpolation. However, the slow query time through MFA makes it less suitable
for interactively visualizing a large MFA model. In this work, we develop the
first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA
model encoded from large-scale datasets. Our method achieves low input latency
through distributed architecture, and its performance can be further enhanced
by utilizing a compressed MFA model while still maintaining a high-quality
rendering result for scientific datasets. We conduct comprehensive experiments
to show that MFA-DVV can decrease the input latency and achieve superior
visualization results for big scientific data compared with existing
approaches.
