论文标题
体积深度图像的平行合成，以高框架速率以高框架的互动可视化分布式体积的交互式可视化
Parallel Compositing of Volumetric Depth Images for Interactive Visualization of Distributed Volumes at High Frame Rates
论文作者
论文摘要
我们提出了用于大型三维体积数据的体积深度图像（VDI）的平行合成算法。在数值模拟和实验中通常会产生大量的分布式量数据，但是以平滑，交互式框架速率可视化它们仍然具有挑战性。 VDIS是提供潜在解决方案的音量数据的视图依赖的分段常数表示。与原始数据相比，它们更紧凑，渲染便宜。但是，到目前为止，还没有从分布式数据生成VDI的方法。我们提出了一种算法，该算法可以通过自动选择的内容自适应参数将VDI的分类并行生成和合成。然后可以流式传输所得的合成VDI以进行远程显示，从而提供大型分布式卷数据的响应可视化。
We present a parallel compositing algorithm for Volumetric Depth Images (VDIs) of large three-dimensional volume data. Large distributed volume data are routinely produced in both numerical simulations and experiments, yet it remains challenging to visualize them at smooth, interactive frame rates. VDIs are view-dependent piecewise constant representations of volume data that offer a potential solution. They are more compact and less expensive to render than the original data. So far, however, there is no method for generating VDIs from distributed data. We propose an algorithm that enables this by sort-last parallel generation and compositing of VDIs with automatically chosen content-adaptive parameters. The resulting composited VDI can then be streamed for remote display, providing responsive visualization of large, distributed volume data.
