SGER: Volume Comparisons

大量数据是许多类型的科学和医学调查的重要组成部分。体可视化提供了工具来帮助科学家检查体数据：现有的方法可以显示大量数据集，也可以使用商用硬件交互式地显示适度的数据集。合成外观方法提供假照明和风格化（如非真实感）渲染方法有助于使复杂的形状更容易理解。然而，所有这些进步都集中在帮助科学家检查单个体积数据集的工具上。我们需要一种工具，明确地帮助科学家在海量数据集之间进行视觉比较。例如，科学家可能需要找出两个标本之间的差异，或者在一系列的卷中寻找趋势。随着数据生成吞吐量的增加，对此类工具的需求将会增长：扫描的扩散意味着将扫描更多的标本；无创成像技术允许对动物的发育进行一系列扫描；基于网格的超级计算允许运行大量独立的模拟。

Volume data is a vital part of many types of scientific and medical inquiry. Volume visualization has provided tools to help scientists examine volume data: methods exist for display of massive datasets and to display moderate datasets interactively using commodity hardware. Synthetic appearance methods to provide fake lighting and stylized (e.g. non-photorealistic) rendering methods help make complex shapes easier to comprehend. However, all of these advances focus on tools to help a scientist examine a single volumetric dataset.There is a need for tools that explicitly help scientists make visual comparisons among volume datasets. For example, a scientist may need to find differences between two specimens or to look for trends in a sequence of volumes. The need for such tools will grow as the throughput of data generation increases: the proliferation of scanning means more specimens will be scanned; noninvasive imaging techniques allow for series of scans over the development of an animal; and grid-based supercomputing allows for large numbers of independent simulations to be run.