Profile
|
Jan Frieder Milke, B.Sc. |
Publications
InsitUE - Enabling Hybrid In-situ Visualizations through Unreal Engine and Catalyst
In-situ, in-transit, and hybrid approaches have become well-established visualization methods over the last decades. Especially for large simulations, these paradigms enable visualization and additionally allow for early insights. While there has been a lot of research on combining these approaches with classical visualization software, only a few worked on combining in-situ/in-transit approaches with modern game engines. In this paper, we present and demonstrate InsitUE, a Catalyst2 compatible hybrid workflow that enables interactive real-time visualization of simulation results using Unreal Engine.
@InProceedings{10.1007/978-3-031-73716-9_33,
author="Kr{\"u}ger, Marcel
and Milke, Jan Frieder
and Kuhlen, Torsten W.
and Gerrits, Tim",
editor="Weiland, Mich{\`e}le
and Neuwirth, Sarah
and Kruse, Carola
and Weinzierl, Tobias",
title="InsitUE - Enabling Hybrid In-situ Visualizations Through Unreal Engine and Catalyst",
booktitle="High Performance Computing. ISC High Performance 2024 International Workshops",
year="2025",
publisher="Springer Nature Switzerland",
address="Cham",
pages="469--481",
abstract="In-situ, in-transit, and hybrid approaches have become well-established visualization methods over the last decades. Especially for large simulations, these paradigms enable visualization and additionally allow for early insights. While there has been a lot of research on combining these approaches with classical visualization software, only a few worked on combining in-situ/in-transit approaches with modern game engines. In this paper, we present and demonstrate InsitUE, a Catalyst2 compatible hybrid workflow that enables interactive real-time visualization of simulation results using Unreal Engine.",
isbn="978-3-031-73716-9"
}
Leveraging BC6H Texture Compression and Filtering for Efficient Vector Field Visualization
The steady advance of compute hardware is accompanied by an ever-steeper amount of data to be processed for visualization. Limited memory bandwidth provides a significant bottleneck to the runtime performance of visualization algorithms while limited video memory requires complex out-of-core loading techniques for rendering large datasets. Data compression methods aim to overcome these limitations, potentially at the cost of information loss. This work presents an approach to the compression of large data for flow visualization using the BC6H texture compression format natively supported, and therefore effortlessly leverageable, on modern GPUs. We assess the performance and accuracy of BC6H for compression of steady and unsteady vector fields and investigate its applicability to particle advection. The results indicate an improvement in memory utilization as well as runtime performance, at a cost of moderate loss in precision.
@inproceedings{10.2312:vmv.20231238,
booktitle = {Vision, Modeling, and Visualization},
editor = {Guthe, Michael and Grosch, Thorsten},
title = {{Leveraging BC6H Texture Compression and Filtering for Efficient Vector Field Visualization}},
author = {Oehrl, Simon and Milke, Jan Frieder and Koenen, Jens and Kuhlen, Torsten W. and Gerrits, Tim},
year = {2023},
publisher = {The Eurographics Association},
ISBN = {978-3-03868-232-5},
DOI = {10.2312/vmv.20231238}
}
