Importance guided stream surface generation and feature exploration

Exploring flow features and patterns hidden behind the data has received extensive academic attention in flow visualization. In this paper, we introduce an importance-guided surface generation and exploration scheme to explore the features and their connections. The features are expressed as an importance field, which can either be derived from a scalar field or be specified as a flow pattern. Guided by the importance field, we sample a pool of seeding curves along the binormal direction and construct stream surfaces to fit the regions of high- importance values. Our scheme evaluates candidate seeding curves by collecting importance scores from the curve and corresponding streamlines. The candidate seeding curves are refined using the high-score segments to identify the optimal surfaces. Comparative visualization among different kinds of flow features across time steps can be easily derived for flow structure analysis. In order to reduce the visual complexity, we leverage SurfRiver to achieve clearer observation by flattening and aligning the surface. Finally, we apply our surface generation scheme guided by flow patterns and scalar fields to evaluate the effectiveness of the proposed tool.