LOCALIS: Locally-adaptive Line Simplification for GPU-based Geographic Vector Data Visualization
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Vector data is abundant in many fields such as geography and cartography. More flexible and faster graphics processing units (GPUs) allowed new rendering techniques to be developed that use vector data directly in the rendering pipeline. This enables new adaptive and efficient solutions for problems such as dynamic level of detail (LOD) management when rendering large-scale vector datasets. This problem has often been tackled by creating discrete vector map LOD datasets. In addition to being limited to a fixed set of the predefined LODs at any time, smooth LOD transitions are also not viable in such approaches. In our work, we present a GPU-based algorithm for real-time simplification and rendering of large line vector datasets directly on the GPU. To achieve this, we adapt the Douglas-Peucker algorithm to create a set of line segments whose specific subsets represent the lines at any variable LOD. At run time, our algorithm supports screen-space adaptive LOD levels and creates an appropriate subset of the line segments accordingly. We efficiently manage information for each line segment in order to individually evaluate its inclusion in the simplified representations. Our technique includes data structures inspired by deferred vector rendering to render a large number of line segments in real time while simplifying them at the same time. Our implementation shows that we can simplify and render large line datasets interactively. Additionally, we can successfully apply line style patterns, dynamic lenses, and anti-aliasing techniques to our line rendering.
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