Iso-surfaces or level-sets provide an effective and frequently used means for feature visualization. However, they are restricted to simple features for uni-variate data. The approach does not scale when moving to multi-variate data or when considering more complex feature definitions. In this paper, we introduce the concept of traits and feature level-sets , which can be understood as a generalization of level-sets as it includes iso-surfaces, and fiber surfaces as special cases. The concept is applicable to a large class of traits defined as subsets in attribute space, which can be arbitrary combinations of points, lines, surfaces and volumes. It is implemented into a system that provides an interface to define traits in an interactive way and multiple rendering options. We demonstrate the effectiveness of the approach using multi-variate data sets of different nature, including vector and tensor data, from different application domains.

中文翻译：

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功能级别集：将等值面通用化为多变量数据

等值面或水平集为特征可视化提供了有效且常用的方法。但是，它们仅限于单变量数据的简单功能。当移至多变量数据或考虑更复杂的特征定义时，该方法无法扩展。在本文中，我们介绍了特质 和 功能级别集 ，这可以理解为水平集的一般化，因为它包括等值面和特殊情况下的纤维表面。该概念适用于定义为属性空间中子集的一大类特征，特征可以是点，线，面和体积的任意组合。它被实施到一个系统中，该系统提供了以交互方式定义特征和多种渲染选项的界面。我们使用来自不同应用领域的不同性质的多变量数据集（包括矢量和张量数据）证明了该方法的有效性。