Topology-guided accelerated vector field streamline visualization

Streamline visualization is crucial for understanding 3D vector fields, and seed placement is essential for high-quality streamlines. Current algorithms are either uniform, omitting vital information, or slow due to precalculation. We propose three novel techniques to build a topology-guided accelerated vector field streamline visualization framework. First, we convert the tracing process into an iterative one, dynamically generating seed points based on critical region detection and a cooldown mechanic. Second, we introduce “planar critical points” combined with traditional critical points to identify critical regions, and place new seed points according to their critical point types. During this process, we circumvent complex eigenvalue calculation with determining whether the eigenvalues are real, which is enough for our placement strategies. Third, we offer a fast streamline simplification technique based on global distortion to reduce clutter. Based on the proposed streamline visualization framework and various vector field visualization methods, we develop a CUDA-based application tool called AdaptiFlux, which achieves real-time visualization of vector fields more efficiently than those representative visualization tools.