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arXiv · 2312.15073

Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation

Jianxin Sun·David Lenz·Hongfeng Yu·Tom Peterka
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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.

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Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka. 2023-12-22. Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation. https://arxiv.org/abs/2312.15073

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