Using 3D Topological Connectivity for Ghost Particle Reduction in Flow Reconstruction

Christina Tsalicoglou, Thomas Rösgen; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 1839-1847

Volumetric flow velocimetry for experimental fluid dynamics relies primarily on the 3D reconstruction of point objects, which are the detected positions of tracer particles identified in images obtained by a multi-camera setup. By assuming that the particles accurately follow the observed flow, their displacement over a known time interval is a measure of the local flow velocity. The number of particles imaged in a 1 Megapixel image is typically in the order of 1e3-1e4, resulting in a large number of consistent but incorrect reconstructions (no real particle in 3D), that must be eliminated through tracking or intensity constraints. In an alternative method, 3D Particle Streak Velocimetry (3D-PSV), the exposure time is increased, and the particles' pathlines are imaged as "streaks". We treat these streaks (a) as connected endpoints and (b) as conic section segments and develop a theoretical model that describes the mechanisms of 3D ambiguity generation and shows that streaks can drastically reduce reconstruction ambiguities. Moreover, we propose a method for simultaneously estimating these short, low-curvature conic section segments and their 3D position from multiple camera views. Our results validate the theory, and the streak and conic section reconstruction method produces far fewer ambiguities than simple particle reconstruction, outperforming current state-of-the-art particle tracking software on the evaluated cases.