Interactive GPU-based Visualization of Scalar Data with Gaussian Distributed Uncertainty

Abstract

We present a GPU-based approach to visualize samples of normally distributed uncertain, three-dimensional scalar data. Our approach uses a mathematically sound interpolation scheme, i.e., Gaussian process regression. The focus of this work is to demonstrate, that GP-regression can be used for interpolation in practice, despite the high computational costs. The potential of our method is demonstrated by an interactive volume rendering of three-dimensional data, where the gradient estimation is directly computed by the field function without the need of additional sample points of the underlying data. We illustrate our method using three-dimensional data sets of the medical research domain.