Expressive seeding of multiple stream surfaces for interactive flow exploration

Brambilla, A.; Hauser, Helwig

doi:10.1016/j.cag.2015.01.002

Cited by 5 publications

(1 citation statement)

References 34 publications

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“…In the past decade, multiple works have researched seed placement and stream surfaces selection techniques for the automatic generation of stream surface‐based flow visualizations [EML*11, ELM*12, ELC*12, ESRT13, ELM*14, SEG*14, BKC15, BH15, TW16, TW17, Vas17, HTW18]. For stream surfaces, the placement, separation, and alignment of seed curves is of interest to researchers.…”

Section: Research Challengesmentioning

confidence: 99%

A Survey of Seed Placement and Streamline Selection Techniques

Sane

Bujack

Garth

et al. 2020

Computer Graphics Forum

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Streamlines are an extensively utilized flow visualization technique for understanding, verifying, and exploring computational fluid dynamics simulations. One of the major challenges associated with the technique is selecting which streamlines to display. Using a large number of streamlines results in dense, cluttered visualizations, often containing redundant information and occluding important regions, whereas using a small number of streamlines could result in missing key features of the flow. Many solutions to select a representative set of streamlines have been proposed by researchers over the past two decades. In this state‐of‐the‐art report, we analyze and classify seed placement and streamline selection (SPSS) techniques used by the scientific flow visualization community. At a high‐level, we classify techniques into automatic and manual techniques, and further divide automatic techniques into three strategies: density‐based, feature‐based, and similarity‐based. Our analysis evaluates the identified strategy groups with respect to focus on regions of interest, minimization of redundancy, and overall computational performance. Finally, we consider the application contexts and tasks for which SPSS techniques are currently applied and have potential applications in the future.

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Section: Research Challengesmentioning

confidence: 99%

A Survey of Seed Placement and Streamline Selection Techniques

Sane

Bujack

Garth

et al. 2020

Computer Graphics Forum

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Importance guided stream surface generation and feature exploration

Zhang

Xie

et al. 2023

Visual Informatics

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Semi-Automatic Generation of Stream Surfaces via Sketching

Tao

Wang

2018

IEEE Trans. Visual. Comput. Graphics

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We present a semi-automatic approach for stream surface generation. Our approach is based on the conjecture that good seeding curves can be inferred from a set of streamlines. Given a set of densely traced streamlines over the flow field, we design a sketch-based interface that allows users to describe their perceived flow patterns through drawing simple strokes directly on top of the streamline visualization results. Based on the 2D stroke, we identify a 3D seeding curve and generate a stream surface that captures the flow pattern of streamlines at the outermost layer. Then, we remove the streamlines whose patterns are covered by the stream surface. Repeating this process, users can peel the flow by replacing the streamlines with customized surfaces layer by layer. Furthermore, we propose an optimization scheme to identify the optimal seeding curve in the neighborhood of an original seeding curve based on surface quality measures. To support interactive optimization, we design a parallel surface quality estimation strategy that estimates the quality of a seeding curve without generating the surface. Our sketch-based interface leverages an intuitive painting metaphor which most users are familiar with. We present results using multiple data sets to show the effectiveness of our approach.

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Expressive seeding of multiple stream surfaces for interactive flow exploration

Cited by 5 publications

References 34 publications

A Survey of Seed Placement and Streamline Selection Techniques

A Survey of Seed Placement and Streamline Selection Techniques

Importance guided stream surface generation and feature exploration

Semi-Automatic Generation of Stream Surfaces via Sketching

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