Neural Representations for Volume Visualization

Abstract

In this talk, I will describe two projects, both joint work with collaborators at Vanderbilt University. The first project studies how generative neural models can be used to model the process of volume rendering scalar fields. We construct a generative adversarial network that learns the mapping from volume rendering parameters, such as viewpoint and transfer function, to the rendered image. In doing so, we can analyze the volume itself and provide new mechanisms for guiding the user in transfer function editing and exploring the space of possible images that can be volume rendered. Both our training process and applications are available on the web at https://github.com/matthewberger/tfgan

Bio:

Joshua A. Levine is an associate professor in the Department of Computer Science at University of Arizona. Prior to starting at Arizona in 2016, he was an assistant professor at Clemson University from 2012 to 2016, and before that a postdoctoral research associate at the University of Utah’s SCI Institute from 2009 to 2012. He is a recipient of the 2018 DOE Early Career award. He received his PhD in Computer Science from The Ohio State University in 2009 after completing BS degrees in Computer Engineering and Mathematics in 2003 and an MS in Computer Science in 2004 from Case Western Reserve University. His research interests include visualization, geometric modeling, topological analysis, mesh generation, vector fields, performance analysis, and computer graphics.

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Meeting ID: 960 3825 1227

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Meeting ID: 960 3825 1227

Passcode: see colloquium email