Photo-Guided Exploration of Volume Data Features
This work addresses the challenge of imagery query or reverse engineering in scientific visualization, allowing users to find features in datasets similar to target images without manual parameter tweaking, which is incremental as it applies existing techniques to a new application.
The researchers tackled the problem of generating rendered images from scientific volume data that match a target photograph, using deep neural networks and evolutionary optimization to optimize rendering parameters based on a similarity function. They demonstrated the method's efficacy on a superstorm simulation dataset and online images, with a parallel implementation run on NCSA's Blue Waters.
In this work, we pose the question of whether, by considering qualitative information such as a sample target image as input, one can produce a rendered image of scientific data that is similar to the target. The algorithm resulting from our research allows one to ask the question of whether features like those in the target image exists in a given dataset. In that way, our method is one of imagery query or reverse engineering, as opposed to manual parameter tweaking of the full visualization pipeline. For target images, we can use real-world photographs of physical phenomena. Our method leverages deep neural networks and evolutionary optimization. Using a trained similarity function that measures the difference between renderings of a phenomenon and real-world photographs, our method optimizes rendering parameters. We demonstrate the efficacy of our method using a superstorm simulation dataset and images found online. We also discuss a parallel implementation of our method, which was run on NCSA's Blue Waters.