Deep recurrent optical flow learning for particle image velocimetry data

Christian Lagemann, Kai Lagemann, Sach Mukherjee, Wolfgang Schroeder

Abstract: A wide range of problems in applied physics and engineering involve learning physical displacement fields from data. In this paper we propose a deep neural network-based approach for learning displacement fields in an end-to-end manner, focusing on the specific case of particle image velocimetry (PIV), a key approach in experimental fluid dynamics that is of crucial importance in diverse applications such as automotive, aerospace and biomedical engineering. The current state of the art in PIV data processing involves traditional handcrafted models that are subject to limitations including the substantial manual effort required and difficulties in generalizing across conditions. By contrast, the deep learning-based approach introduced in this paper, which is based on a recent optical flow learning architecture known as recurrent all-pairs field transforms, is general, largely automated and provides high spatial resolution. Extensive experiments, including benchmark examples where true gold standards are available for comparison, demonstrate that the proposed approach achieves state-of-the-art accuracy and generalization to new data, relative to both classical approaches and previously proposed optical flow learning schemes.