The 2010's

The topological notion of robustness introduces mathematically rigorous approaches to interpret vector field data. Robustness quantifies the structural stability of critical points with respect to perturbations and has been shown to be useful for increasing the visual interpretability of vector fields. We define a new Galilean invariant robustness framework that enables the simultaneous visualization of robust critical points across the dominating reference frames in different regions of the data. Continue reading

Point cloud-based datasets have been critical to a number of research communities, including graphics, robotics, and CAD because in many ways, points better represent many data sources than triangulations. Visualizing points on remote devices remains somewhat problematic to this day. Very large datasets have the problem of needing to be transmitted, have any data structures built locally, and rendered on the local machine. This paper addresses the question of what's the point in rendering billions of points, when the display only has 2 million pixels? Continue reading

In this paper we describe the Myocardial Uncertainty Viewer (muView) system for exploring data stemming from the simulation of cardiac ischemia. The simulation uses a collection of conductivity values to understand how ischemic regions effect the undamaged anisotropic heart tissue. The data resulting from the simulation is multi-valued and volumetric, and thus, for every data point, we have a collection of samples describing cardiac electrical properties. View combines a suite of visual analysis methods to explore the area surrounding the ischemic zone and identify how perturbations of variables change the propagation of their effects. Continue reading