We present a novel approach to out-of-core time-varying isosurface visualization. We attempt to interactively visualize time-varying datasets which are too large to fit into main memory using a technique which is dramatically different from existing algorithms. Inspired by video encoding techniques, we examine the data differences between time steps to extract isosurface information. We exploit span space extraction techniques to retrieve operations necessary to update isosurface geometry from neighboring time steps. Because only the changes between time steps need to be retrieved from disk, I/O bandwidth requirements are minimized. We apply temporal compression to further reduce disk access and employ a point-based previewing technique that is refined in idle interaction cycles. Our experiments on computational simulation data indicate that this method is an extremely viable solution to large time-varying isosurface visualization. Our work advances the state-of-the-art by enabling all isosurfaces to be represented by a compact set of operations.

In this work, we visualize high-dimensional particle simulation data using a suite of scatterplot-based visualizations coupled with interactive selection tools. We use traditional 2D and 3D projection scatterplots as well as a novel oriented-disk rendering style to convey various information about the data. Interactive selection tools allow physicists to manually classify "interesting" sets of particles that are highlighted across multiple, linked views of the data. The power of our application is the ability to correspond new visual representations of the simulation data with traditional, well understood visualizations. This approach supports the interactive exploration of the high-dimensional space while promoting discovery of new particle behavior.

We present a simple and output optimal algorithm for accelerated isosurface extraction from volumetric data sets. Output optimal extraction algorithms perform an amount of work dominated by the size of the (output) isosurface rather than the size of the (input) data set. While several optimal methods have been proposed to accelerate isosurface extraction, these algorithms are relatively complicated to implement or require quantized values as input. Our method is based on a straightforward array data structure that only requires an auxiliary sorting routine for construction. The method works equally well for floating point data as it does for quantized data sets. We demonstrate how the data structure can exploit coherence between isosurfaces by performing searches incrementally. We show results for real application data validating the method's optimality.

We present a new approach to isosurface visualization that we call "iso-splatting." We use point primitives for representing and rendering isosurfaces. The method consists of two steps. In the first step, point samples are generated throughout the volumetric domain of a scalar function. In the second step, these points are projected onto the isosurface of interest. We render the resulting point set using a surface splatting algorithm. The method can be extended to out-of-core or parallel environments. Our results show that this method can offer much greater time and space efficiency when compared with standard triangle-based methods, thereby supporting higher levels of interactivity. Parts of the algorithm can be accelerated using graphics hardware. One key advantage of this approach is that, since extraction computations are divided into two smaller phases, work can be distributed to exploit all available resources.

We present a method to represent unstructured scalar fields at multiple levels of detail. Using a parallelizable classification algorithm to build a cluster hierarchy, we generate a multiresolution representation of a given volumetric scalar data set. The method uses principal component analysis (PCA) for cluster generation and a fitting technique based on radial basis functions (RBFs). Once the cluster hierarchy has been generated, we utilize a variety of techniques for extracting different levels of detail. The main strength of this work is its generality. Regardless of grid type, this method can be applied to any discrete scalar field representation, even one given as a "point cloud".