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.

In the second part of my talk, I will explore a recent neural modeling approach for building compressive representations of volume data. This approach represents volumetric scalar fields as learned implicit functions wherein a neural network maps a point in the domain to an output scalar value. By setting the number of weights of the neural network to be smaller than the input size, we achieve compressive function approximation. Combined with carefully quantizing network weights, we show that this approach yields highly compact representations that outperform state-of-the-art volume compression approaches. We study the impact of network design choices on compression performance, highlighting how conceptually simple network architectures are beneficial for a broad range of volumes. Our compression approach is hosted at https://github.com/matthewberger/neurcomp.