Coherent view-dependent streamline selection for importance-driven flow visualization

Abstract

Streamline visualization can be formulated as the problem of streamline placement or streamline selection. In this paper, we present an importance-driven approach to view-dependent streamline selection that guarantees coherent streamline update when the view changes gradually. Given a large number of randomly or uniformly seeded and traced streamlines and sample viewpoints, our approach evaluates, for each streamline, the view-dependent importance by considering the amount of information shared by the 3D streamline and its 2D projection as well as how stereoscopic the streamline’s shape is reflected under each viewpoint. We achieve coherent view-dependent streamline selection following a two-pass solution that considers i) the relationships between local viewpoints and the global streamline set selected in a view-independent manner and ii) the continuity between adjacent viewpoints. We demonstrate the effectiveness of our approach with several synthesized and simulated flow fields and compare our view-dependent streamline selection algorithm with a naïve algorithm that selects streamlines solely based on the information at the current viewpoint.