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Journal of Visualization

Erschienen in:

10.10.2023 | Regular Paper

MFA-DVR: direct volume rendering of MFA models

verfasst von: Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka

Erschienen in: Journal of Visualization | Ausgabe 1/2024

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Abstract

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for large-scale volumetric data, and uniform representation of both structured and unstructured data. In this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured and unstructured volumetric datasets. We demonstrate improved rendering quality using MFA-DVR on both synthetic and real datasets through a comparative study. We show that MFA-DVR not only generates more faithful volume rendering than using local filters but also performs faster on high-order interpolations on structured and unstructured datasets. MFA-DVR is implemented in the existing volume rendering pipeline of the Visualization Toolkit (VTK) to be accessible by the scientific visualization community.

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Vorheriger Artikel Interactive steering on in situ particle-based volume rendering framework

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Titel: MFA-DVR: direct volume rendering of MFA models
verfasst von: Jianxin Sun
David Lenz
Hongfeng Yu
Tom Peterka
Publikationsdatum: 10.10.2023
Verlag: Springer Berlin Heidelberg
Erschienen in: Journal of Visualization / Ausgabe 1/2024
Print ISSN: 1343-8875
Elektronische ISSN: 1875-8975
DOI: https://doi.org/10.1007/s12650-023-00946-y

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