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.
翻译:我们为卷积深度图像(VDIs)提供了一种平行的三维大体积数据的合成算法(VDIs ) 。 在数字模拟和实验中,通常都会生成大量分布式量数据,但以平滑、互动框架速率对它们进行可视化仍然具有挑战性。 VDI是具有潜在解决方案的量数据的视向、片断常态表达方式。它们比原始数据更紧凑、成本更低。然而,到目前为止,还没有方法从分布式数据中生成VDI。我们建议一种算法,通过对 VDIs进行排序式的最后一次平行生成和编译,并自动选择内容适应参数。 由此产生的合成VDI随后可以流到远程显示, 提供大型、分布式体积数据的响应性可视化。