A Cluster Hierarchy-based Volume Rendering Approach for Interactive Visual Exploration of Multi-variate Volume Data

Abstract

Interactive visual analysis of volumetric data relies on intuitive, yet powerful mechanisms to generate transfer functions. For multi-variate data, traditional methods for interactive transfer functions generation are of limited use. We propose a novel approach, where the user operates in a cluster space. It relies on hierarchical densitybased clustering of the high-dimensional feature space. The cluster tree visualization in form of a 2D radial layout serves as an interaction widget for selecting clusters, assigning material properties, changing sizes, merging, and splitting. This widget is complemented by a linked parallel coordinates widget. The interactive selections are automatically mapped to a transfer function for a linked 3D texture-based direct volume rendering, where brushing in parallel coordinates leads to the generation of a 3D binary opacity mask that is overlaid with the opacity values obtained from cluster tree selections. In GPU memory, we only hold the density values from the clustering approach and the cluster IDs. The derived density field allows us to interactively change the size of clusters and to compute normals for lighting. We applied our methods to the visualization of multi-variate data consisting of multiple scalar fields as well as derived scalar property fields from single scalar and vector fields. Our approach scales well to arbitrarily high dimensionality as the complexity of the main user interactions do not increase with the number of dimensions.