生活流:基于gpu密集边缘渲染的边捆绑图的增强探索

A. Lambert, D. Auber, G. Melançon
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引用次数: 15

摘要

本文描述了一种利用GPU的全部功能来增强边缘捆绑在实际应用中的可用性的方法。边缘捆绑,以及其他依赖于使用高质量边缘重路由的边缘聚类方法。绘制边束图的典型方法是将边绘制成曲线。但曲线生成的计算成本较高,且不易满足实时交互要求。此外,虽然边缘捆绑提供了更好的图的整体可读性,但捆绑使恢复局部信息变得更加困难。因此,我们的目标是提供流体交互,允许通过特定的交互技术恢复本地信息。我们建立的系统提供了民间传说或经典的互动,如变焦&平移,鱼眼和放大镜。我们还执行了Tominski等人的Bring & Go技术。我们提出了一种充分利用GPU的计算能力将图的边缘绘制为参数样条的方法。当在GPU上运行所有曲线计算时,效率的提高使捆绑技术可以嵌入到涉及数千个节点和边的图形的交互系统中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Living Flows: Enhanced Exploration of Edge-Bundled Graphs Based on GPU-Intensive Edge Rendering
This paper describes an approach exploiting the full capabilities of GPU's to enhance the usability of edge bundling in real applications. Edge bundling, as well as other edge clustering approaches relying on the use of high quality edge rerouting. Typical approach for drawing edge-bundled graph is to render edges as curves. But curves generation can have a relatively high computational costs and do not easily comply with real-time interaction. Furthermore, while edge bundling provides a much better overall readability of a graph, the bundles make it more difficult to recover local information. Our goal was thus to provide fluid interaction allowing the recovery of local information through specific interaction techniques. The system we built offers folklore or classical interaction such as zoom & pan, fish-eye and magnifying lens. We also implemented the Bring & Go technique by Tominski et al. We proposed an approach exploiting the full computing power of GPU's when rendering graph edges as parametric splines. The gain in efficiency when running all curves computations on the GPU turns bundling techniques into techniques that can be embedded in interactive systems concerned with graphs of several thousands of nodes and edges.
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