Speaker: David Banks (University of Tennessee, Knoxville)
3D datasets are becoming increasingly large and complex. In medicine, fiber structures within the brain are inferred from diffusion-tensor magnetic resonance imaging (DT-MRI), yielding thousands to millions of curved trajectories. In chemistry, billions of atoms are included in large-scale molecular dynamics simulations. In both cases, the resulting geometry becomes difficult to comprehend in part because of its complexity.
We describe two approaches to improving perception of the resulting 3D scenes. The first approach is to apply physically based illumination rather than the conventional "local" illumination. The second approach is to transform the data into an ensemble coordinate system where geometric complexity increases slowly. The utility of these approaches have been validated by performing user studies.