ITLACMDScientific Applications  Visualization Group
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Visualization Metrology

We use the term "visualization metrology" to refer to the process of assessing uncertainties associated with the visualization of scientific data. Creating visualizations of data produced by physical or computational experiments can involve various transformations of those data. Each of these transformations may introduce uncertainties in the final visualization. We seek to quantitatively assess these uncertainties in visualizations and in data derived from visualizations.

The visual display of virtual 3D scenes has become commonplace in modern computing systems. While many such visualizations are intended to provide a qualitative experience to the viewer, visualization is increasingly being used to convey accurate representations of spatial relationships. Applications such as computer-aided design and scientific data visualization have important quantitative components. For example, at NIST, we have implemented interactive measurement tools in the virtual world.

As we use such systems for these sorts of quantitative tasks, it is incumbent upon us to understand how the visualization process contributes to uncertainty in these tasks. This work is a first step toward this understanding.

We are focussing first on assessing errors introduced by the rendering process. Rendering is the process by which we transform a 3D geometric description of a virtual scene into a set of pixels that are displayed to the user on a computer monitor.

We have implemented methods for assessing geometric errors introduced by the rendering of three 3D geometric primitive forms: points, line segments, and triangles. These represent the simplest and by far the most commonly used geometric forms rendered in current computer graphics systems. We have developed error metrics based on the comparison the actual set of pixels rendered for a given 3D geometric primitive and an idealized representation of the rendering of that primitive. We have tested these error assessment methods on several commonly used computing platforms and we present the results in the paper cited below.

Future work will involve the assessment of uncertainty introduced by other aspects of the visualization process.

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Date created: 2008-04-10, Last updated: 2011-01-12.