top of page

3D Flow Reconstruction Using Conic Sections And Learning-Based Image Segmentation

C. Tsalicoglou, T. Rösgen

Institute of Fluid Dynamics, ETH Zurich, Switzerland

Extending 3D Particle Streak Velocimetry (3D-PSV) with conic section matching constraints for volumetric flow reconstruction allows the measurement of flows in large volumes using lower frame rates than typically required for 3D-PTV, with fewer reconstruction ambiguities. Streak segmentation is tackled using a state-of-the-art instance segmentation Convolutional Neural Network (CNN), highlighting the potential of learning-based methods to solve vision-based tasks in experimental fluid dynamics. The curved streaks are processed with an optimization-based conic section reconstruction approach, which results in fewer reconstruction ambiguities compared to linear streaks or individual endpoint models and enables the direct and accurate reconstruction of the curved pathlines.

20th Edition
bottom of page