Dataset - Deep Recurrent Optical Flow Learning for Particle Image Velocimetry Data

DZNE Pub

Abstract

This is the official dataset of Recurrent All-Pairs Field Transforms for Particle Image Velocimetry Data (RAFT-PIV) published in Nature Machine Intelligence. In this work, 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). PIV is a key approach in experimental fluid dynamics and of fundamental importance in diverse applications, including automotive, aerospace, and biomedical engineering. In contrast to standard PIV methods, our RAFT-PIV approach is general, largely automated, and provides much higher spatial resolution. This dataset is given as binary TFRECORD format.

Authors 4

1. Christian Lagemann Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University

2. Kai Lagemann

   German Center for Neurodegenerative Diseases · German Center for Neurodegenerative Diseases (DZNE) Dresden

   Affiliation as printed

   German Center for Neurodegenerative Diseases (DZNE)

3. Sach Mukherjee

   German Center for Neurodegenerative Diseases · German Center for Neurodegenerative Diseases (DZNE) Dresden

   Affiliation as printed

   German Center for Neurodegenerative Diseases (DZNE)

4. Wolfgang Schröder Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University