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.

翻译：针对大规模科学体数据处理中时空复杂度带来的挑战，多种数据表征方法已被开发用于分析大规模科学数据。多元函数逼近（MFA）作为一种创新数据模型，旨在应对科学数据分析中的重大难题。该模型能在整个空间域中以高阶精度计算数值与导数，有效缓解零阶或一阶插值相关的伪影问题。然而，MFA查询速度较慢，难以支持大规模MFA模型的交互式可视化。本文首次为大规模数据集编码的MFA模型构建了可扩展的交互式体可视化流水线MFA-DVV。该方法通过分布式架构实现低输入延迟，并可通过采用压缩MFA模型进一步提升性能，同时为科学数据集保持高质量渲染结果。综合实验表明，与现有方法相比，MFA-DVV能够降低输入延迟，为大规模科学数据实现更优的可视化效果。