ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

dependent data. They conclude that in the common case in which features possess regular
structure, a single transfer function is sufficient to represent the time evolution of the data 6 .
Bernardon et al. detail transfer function generation for multi-volume visualization problems,
including time-dependent data 7 . They detail aspects of their user interface; of particular note is
the need to quickly identify, modify, and save new transfer functions for the time points which
require updated functions.
Our work builds directly on that of Woodring et al. 2 They propose a method of volume rendering
time-dependent data by considering the data to be a 4D function and designing transfer functions
directly in that space. However, we find that, for many users, the mapping introduced by a
transfer function for 3D data is already fairly complex, and we therefore endeavor to create a
simplified paradigm for the user. We improve up on their work by providing a more natural twostage
presentation, an improved exploratory process which does not rely on reorganizing the data
volume, and improved performance through a novel rendering architecture.
3. CHRONOPHOTOGRAPHIC VISUALIZATION
Fig. 2 Chronophotographic visualization of a bouncing
ball with all timesteps occupying the same space. The
timestep under focus (just before the ball hits the 'floor') is
viewed normally; past and future timesteps are viewed
with transparency which increases based on the distance
from the time point under investigation.
Chronophotography, as the name suggests, is a
photographic method for conveying time. There are two
primary types of photographic visualizations. The first
places timesteps spatially adjacent to one another, to form
a tiled display of multiple time points, as depicted in
Figure 1. It should be noted that this can extend to two dimensions, despite Figure 1 only utilizing
the X dimension for this tiling. The second method layers time points, as shown in Figure 2. As
utilized in that figure, it is common to have a 'focus' time point which is visualized normally, with
nearby times deemphasized. In Figure 2, this deemphasis is realized via transparency, but other
methods are possible, such as a decrease in color saturation or a translation in the Z dimension.
3.1 Challenges
There are a variety of visualization challenges which are exacerbated by or unique to the addition
of the additional dimension of time. The first is supplying meaningful overview or context
visualizations: identifying the subset of timesteps which warrant deeper analysis. For this process
to be productive, users require an easy and more importantly, fast, method for understanding
qualitative differences between a set of timesteps. Finally, once identified, tools are required to
extract quantitative differences between timesteps of interest.
As can be seen by comparing Figure 1 with Figure 2, the adjacent method has difficulty
conveying low-frequency motion. In Figure 1, these motion implied is difficult to quantify,
whereas Figure 2 makes this information available at a glance. The adjacent method tends to