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Framework for sharing of highly resolved turbulence simulation data 1

1st JARA-HPC Symposium on High-Performance Scientific computing, JHPCS 2016

作者： Tweddell, Bastian Göbbert, Jens Henrik Gauding, Michael Weyers, Benjamin Hagemeier, Björn Jülich Supercomputing Centre Forschungszentrum Jülich GmbH Jülich Germany CORIA - CNRS UMR 6614 Saint-Étienne-du-Rouvray France Visual Computing Institute Virtual Reality and Immersive Visualization Group RWTH Aachen University Aachen Germany

ISBN: (数字)9783319538624

ISBN: (纸本)9783319538617

The growing computational capabilities of nowadays supercomputers have made highly resolved turbulence simulations possible. The large data-sets and tremendous amount of required compute resources create serious new challenges when attempting to share the data between different research groups. But even more difficult to solve is the incompatibility of the data formats and numerical approaches used for turbulence simulations, which in detail are often only known to the simulation code developer. In this paper a framework for sharing data of large scale simulations is presented, which simplifies the access and further post-processing even beyond a single supercomputing center. It combines established services to provide an easy to manage-and-extend software setup without the need to standardize a database or -format. Beside other advantages, it enables the use of direct file outputs from simulation runs which are often archived anyway. © Springer International Publishing AG 2017.

关键词： Turbulence

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A Unified Framework for Event-based Frame Interpolation with Ad-hoc Deblurring in the Wild

arXiv

引用

arXiv 2023年

作者： Sun, Lei Gehrig, Daniel Sakaridis, Christos Gehrig, Mathias Liang, Jingyun Sun, Peng Xu, Zhijie Wang, Kaiwei Van Gool, Luc Scaramuzza, Davide National Research Center for Optical Instrumentation Zhejiang University Hangzhou310027 China Robotics and Perception Group University of Zurich Zurich8050 Switzerland Computer Vision Lab ETH Zurich Zurich8092 Switzerland Centre for Visual and Immersive Computing Huddersfield University HD1 3DH United Kingdom INSAIT Sofia University "St. Kliment Ohridski" Bulgaria

Effective video frame interpolation hinges on the adept handling of motion in the input scene. Prior work acknowledges asynchronous event information for this, but often overlooks whether motion induces blur in the video, limiting its scope to sharp frame interpolation. We instead propose a unified framework for event-based frame interpolation that performs deblurring ad-hoc and thus works both on sharp and blurry input videos. Our model consists in a bidirectional recurrent network that incorporates the temporal dimension of interpolation and fuses information from the input frames and the events adaptively based on their temporal proximity. To enhance the generalization from synthetic data to real event cameras, we integrate self-supervised framework with the proposed model to enhance the generalization on real-world datasets in the wild. At the dataset level, we introduce a novel real-world high-resolution dataset with events and color videos named HighREV, which provides a challenging evaluation setting for the examined task. Extensive experiments show that our network consistently outperforms previous state-of-the-art methods on frame interpolation, single image deblurring, and the joint task of both. Experiments on domain transfer reveal that self-supervised training effectively mitigates the performance degradation observed when transitioning from synthetic data to real-world data. Code and datasets are available at https://***/AHupuJR/REFID. Copyright © 2023, The Authors. All rights reserved.

关键词： Self-supervised learning

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