Neural Volume Visualization

This webpage serves as the central hub for the collaborative NSF project Neural Volume Visualization.

The focus of this project is on the development of deep learning-based volume visualization techniques that are compressive, interactive, trustworthy, and enable intuitive analysis. These goals are motivated by the demands placed by data produced in a variety of scientific fields, wherein the size and complexity of the data necessitates new techniques for visual analysis.

Principal Investigators

Matthew Berger, Vanderbilt University, NSF-2007444

Joshua A. Levine, University of Arizona, NSF-2006710

Publications

Saroj Sahoo, Yuzhe Lu, Matthew Berger
Neural Flow Map Reconstruction
EuroVis, 2022

Sangwon Jeong, Shusen Liu, Matthew Berger
Interactively Assessing Disentanglement in GANs
EuroVis, 2022

Yuzhe Lu, Kairong Jiang, Joshua A. Levine, Matthew Berger
Compressive Neural Representations of Volumetric Scalar Fields
EuroVis, 2021
(paper), (project)

Roza G. Bayrak, Nhung Hoang, Colin B. Hansen, Catie Chang, Matthew Berger
PRAGMA: Interactively Constructing Functional Brain Parcellations
IEEE Visualization Conference, 2021
(paper)