We present a novel visual representation and interface named the matrix of isosurface similarity maps (MISM) for effective exploration of large time-varying multivariate volumetric data sets. MISM synthesizes three types of similarity maps (i.e., self, temporal, and variable similarity maps) to capture the essential relationships among isosurfaces of different variables and time steps. Additionally, it serves as the main visual mapping and navigation tool for examining the vast number of isosurfaces and exploring the underlying time-varying multivariate data set. We present temporal clustering, variable grouping, and interactive filtering to reduce the huge exploration space of MISM. In conjunction with the isovalue and isosurface views, MISM allows users to identify important isosurfaces or isosurface pairs and compare them over space, time, and value range. More importantly, we introduce path recommendation that suggests, animates, and compares traversal paths for effectively exploring MISM under varied criteria and at different levels-of-detail. A silhouette-based method is applied to render multiple surfaces of interest in a visually succinct manner. We demonstrate the effectiveness of our approach with case studies of several time-varying multivariate data sets and an ensemble data set, and evaluate our work with two domain experts.

We present an efficient GPU-based solution to compute isosurface similarity maps for scientific volume data sets. Our approach first replaces exact isosurface extraction with a binary volume indicating whether each voxel intersects the surface or not. We then employ bounding volume hierarchy (BVH)-trees to speed up the distance field computation. Finally, a self-similarity map is generated from which we identify representative isosurfaces. We apply our approach to compute isosurface similarity maps from different volume data sets of varying sizes and characteristics. The results demonstrate significant speed gain with acceptable loss of accuracy, showing the potential of our solution for handling large-scale time-varying multivariate data sets.