Abstract
AbstractWe present our approach for the dense visualization and temporal exploration of moving and deforming shapes from scientific experiments and simulations. Our image space representation is created by convolving a noise texture along shape contours (akin to LIC). Beyond indicating spatial structure via luminosity, we additionally use colour to depict time or classes of shapes via automatically customized maps. This representation summarizes temporal evolution, and provides the basis for interactive user navigation in the spatial and temporal domain in combination with traditional renderings. Our efficient implementation supports the quick and progressive generation of our representation in parallel as well as adaptive temporal splits to reduce overlap. We discuss and demonstrate the utility of our approach using 2D and 3D scalar fields from experiments and simulations.
Highlights
Fast computing systems for simulations and highaccuracy measurement techniques enable the generation of timedependent data sets with high spatial and temporal resolution
We present our approach for the dense visualization and temporal exploration of moving and deforming shapes from scientific experiments and simulations
Our image space representation is created by convolving a noise texture along shape contours
Summary
Fast computing systems for simulations and highaccuracy measurement techniques enable the generation of timedependent data sets with high spatial and temporal resolution. We introduce a visualization approach providing a summary and supporting the exploration of moving and transforming shapes in 2D (e.g. Figure 1b) and 3D scalar field data (e.g. Figure 1a). These occur in various scientific analysis scenarios (e.g. in this work, we will consider moving droplets, laser pulses, waves and areas of high temperature or velocity magnitude). Significant temporal overlap can be induced by some complex data sets that make it hard to depict a meaningful overview with one view This cannot be avoided completely in general, our approach introduces different built-in measures to address and mitigate this issue. Our implementation further allows for the quick and progressive generation of our representation in parallel
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