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.

翻译：三维体绘制被广泛应用于揭示各领域体数据集中深刻的内在模式。然而，体数据复杂的结构与多尺度变化使得高效生成高质量体绘制结果成为一项挑战。多变量函数逼近（MFA）是一种新型数据模型，它解决了若干关键难题：在空间域任意位置同时对数值与导数进行高阶求值、大规模体数据的紧凑表示，以及结构化与非结构化数据的统一表示。本文提出MFA-DVR——首个利用MFA模型的直接体绘制管线，适用于结构化和非结构化体数据集。通过对比研究，我们在合成数据集与真实数据集上证明了MFA-DVR的绘制质量提升。实验表明，MFA-DVR不仅比使用局部滤波器能生成更保真的体绘制结果，而且在处理结构化与非结构化数据集的高阶插值时效率更高。MFA-DVR已在可视化工具包（VTK）现有体绘制管线中实现，可供科学可视化社区使用。