Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation
Abstract
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.
Cite
@article{arxiv.2312.15073,
title = {Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation},
author = {Jianxin Sun and David Lenz and Hongfeng Yu and Tom Peterka},
journal= {arXiv preprint arXiv:2312.15073},
year = {2023}
}