Continuous Indexed Points for Multivariate Volume Visualization

TL;DR

This paper introduces continuous indexed points for multivariate volume visualization, enabling detailed analysis of local correlations in complex datasets through a novel, interactive parallel coordinate approach.

Contribution

The paper presents a new method using local linear fitting and continuous indexed points to visualize multivariate correlations in volume data, enhancing analysis capabilities.

Findings

Effective visualization of local correlations in multivariate data.

Supports analysis of two and three variable relationships.

Validated through case study and expert feedback.

Abstract

We introduce continuous indexed points for improved multivariate volume visualization. Indexed points represent linear structures in parallel coordinates and can be used to encode local correlation of multivariate (including multifield, multifaceted, and multiattribute) volume data. First, we perform local linear fitting in the spatial neighborhood of each volume sample using principal component analysis, accelerated by hierarchical spatial data structures. This local linear information is then visualized as continuous indexed points in parallel coordinates: a density representation of indexed points in a continuous domain. With our new method, multivariate volume data can be analyzed using the eigenvector information from local spatial embeddings. We utilize both 1-flat and 2-flat indexed points, allowing us to identify correlations between two variables and even three variables, respectively. An interactive occlusion shading model facilitates good spatial perception of the volume rendering of volumetric correlation characteristics. Interactive exploration is supported by specifically designed multivariate transfer function widgets working in the image plane of parallel coordinates. We show that our generic technique works for multi-attribute datasets. The effectiveness and usefulness of our new method is demonstrated through a case study, an expert user study, and domain expert feedback.