Automatic Camera Placement for In Situ Visualization

Nicole Marsaglia
Date and time: 
Fri, Feb 4 2022 - 12:00pm
Nicole Marsaglia
University of Oregon
  • Hank Childs (Chair)
  • Brittany Erickson
  • Michal Young
  • Ellen Eischen (Mathematics)

Trends in high-performance computing increasingly require visualization to be carried out using in situ processing. This processing most often occurs without a human in the loop, meaning that the in situ software must be able to carry out its tasks without human guidance. This dissertation explores this topic, focusing on automating camera placement for in situ visualization when there is no a priori knowledge of where to place the camera. We introduce a new approach for this automation process, which depends on Viewpoint Quality (VQ) metrics that quantify how much insight a camera position provides. This research involves three major sub-projects: (1) performing a user survey to determine the viewpoint preferences of scientific users as well as developing new VQ metrics that can predict preference 68% of the time; (2) parallelizing VQ metrics and designing search algorithms so they can be executed efficiently in situ; and (3) evaluating the behavior of camera placement of time-varying data to determine how often a new camera placement should be considered. In all, this dissertation shows automating in situ camera placement for scientific simulations is possible on exascale computers and provides insight on best practices.