Event-based Sparse-view Background-Oriented Schlieren Tomography

Background-oriented schlieren (BOS) tomography reconstructs3D flow density fields from refraction-induced distortions observed fromone or more viewpoints. Capturing high-speed flows with frame-basedBOS typically requires high-speed cameras and bright illumination tocompensate for short exposure times. In this paper, we propose an event-based BOS tomography approach that reconstructs time-varying 4D den-sity fields from event streams. Leveraging the high temporal resolutionand high dynamic range of event cameras, the proposed approach enableshigh-speed airflow reconstruction under ambient lighting. We representthe spatiotemporal density field as a neural implicit field and render BOSobservations via refractive ray tracing. We utilize physics-informed regu-larization to improve reconstruction under sparse views. Experiments onsimulated and real data in single-view and orthogonal dual-view setupsdemonstrate accurate reconstructions.