Sahoo, SarojBerger, MatthewAgus, Marco and Garth, Christoph and Kerren, Andreas2021-06-122021-06-122021978-3-03868-143-4https://doi.org/10.2312/evs.20211054https://diglib.eg.org:443/handle/10.2312/evs20211054In this work we propose an integration-aware super-resolution approach for 3D vector fields. Recent work in flow field superresolution has achieved remarkable success using deep learning approaches. However, existing approaches fail to account for how vector fields are used in practice, once an upsampled vector field is obtained. Specifically, a cornerstone of flow visualization is the visual analysis of streamlines, or integral curves of the vector field. To this end, we study how to incorporate streamlines as part of super-resolution in a deep learning context, such that upsampled vector fields are optimized to produce streamlines that resemble the ground truth upon integration. We consider common factors of integration as part of our approach - seeding, streamline length - and how these factors impact the resulting upsampled vector field. To demonstrate the effectiveness of our approach, we evaluate our model both quantitatively and qualitatively on different flow field datasets and compare our method against state of the art techniques.Computing methodologiesNeural networksReconstructionHumancentered computingScientific visualization10.2312/evs.2021105449-53