MFA-DVR: Direct Volume Rendering of MFA Models

TL;DR

MFA-DVR introduces a novel volume rendering pipeline leveraging multivariate functional approximation to produce higher quality and faster visualizations of complex 3D datasets, applicable to both structured and unstructured data.

Contribution

It is the first to integrate MFA into direct volume rendering, improving quality and efficiency for diverse volumetric datasets.

Findings

Enhanced rendering fidelity over local filters

Faster high-order interpolations on complex datasets

Effective for both synthetic and real data

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.