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

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Christian Lagemann , Kai Lagemann , Sach Mukherjee , Wolfgang Schröder · YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.5281/zenodo.4432495

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.

Type

dataset

Language

en

Landing Page

https://doi.org/10.5281/zenodo.4432495

OA Status

green

Related Works

10

OpenAlex ID

https://openalex.org/W4393621308

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