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.