Streamlines are a fundamental representation for flow visualization, enabling analysis of internal flow structures, transport phenomena, and vortical behavior in complex vector fields. Extending this technique into extended reality (XR) environments introduces new opportunities for situated, embodied exploration of large-scale computational fluid dynamics (CFD) datasets. However, existing approaches predominantly address seed placement and occlusion management, while real-time rendering of dense animated streamlines and physically consistent motion encoding remain underexplored, especially in immersive settings where optical flow sensitivity and spatial presence mutually reinforce analytic interpretation.
To address these challenges, we propose a novel real-time animated streamline approach integrated into an interactive XR platform, enabling the progressive and insight-driven exploration of large-scale flow datasets.
At its core, the method introduces an Eulerian Flow Map algorithm coupled with spacetime parallelism, leveraging pixel-wise temporal phase shifts for motion alignment to seamlessly capture the continuous evolution of streamlets, supported by rigorous proofs.
Accelerated by GPU computing, our approach enables rendering hundreds of millions of dynamic streamlets, achieving an exact apparent motion that faithfully represents the underlying physical flow velocity and direction.
Furthermore, the method supports versatile motion patterns and advanced rendering features, tunable via multi-parameter controls to facilitate uncertainty perception.
Ultimately, we provide diverse engineering implementations of our method, contributing to the establishment of a powerful and intuitive framework for deciphering complex flow behaviors and advancing insights into fluid fields.
