We present an octree-based approach supporting multiresolution volume rendering of large data sets. Given a set of scattered points without connectivity information, we impose an actree data structure of low resolution in the preprocessing step. The construction of this initial octree structure of low resolution in the preprocessing step. The construction of this initial octree structure is controlled by the original data resolution and cell-specific error values. Using the octree nodes, rather than the data points, as elementary units for ray casting, we first generate a crude rendering of a given data set. Keeping the pre-processing step independent from the rendering step, we allow a user to interactively explore a large data set by speicifying a region of interest (ROI), where a higher level of rendering accuracy is desired. To refine an ROI, we are making use of the octree constructed in the pro-processing step. Our approach is aimed at minimizing the number of computations and can be applied to large-scale data exploration tasks.

We present a method for avoiding cracks in isosurfaces extracted from adaptive mesh refinement (AMR) data. By combining several rectilinear grids of different resolution, AMR methods substantially reduce storage and computation requirements to accurately represent and simulate complicated data and phenomena. AMR data consist of grids of different resolution. As a result discontinuities (''hanging nodes'') arise in dependent field variables at the interfaces of different resolution mesh elements. These discontinuities can result in cracks in isosurfaces extracted for a specific value of a dependent scalar variable. We describe a method for creating transition regions'' between grids of different resolution. These regions are composed of pyramid elements that allow us to avoid crack formation during isosurface extraction.

We present an octree-based approach supporting multiresolution visualization of large three-dimenstional scientific data sets. Given an irregular gridded data set, we initially impose an octree data structure of relatively low resolution, i.e., consisting of a relatively small number of cells. The construction of this initial octree structure is controlled by the original data resolution and cell-specific error value. It is thus possible to use the octree to visualize either the field function value or the local error value. The octreee data structure can be refined further in areas that are specified by a user of a visualization system: A user would identify a region in space, i.e., an octree cell, where the field is of greater interest or where octree cells carry relatively large error values. This usage of our data sturcture ensures that we use the highest resolution to render only regions of interest in a large-scale scientific data set.

VisIt is a richly featured visualization tool that is used to visualize some of the largest simulations ever run. The scale of these simulations requires that optimizations are incorporated into every operation VisIt performs. But the set of applicable optimizations that VisIt can perform is dependent on the types of operations being done. Complicating the issue, VisIt has a plugin capability that allows new, unforeseen components to be added, making it even harder to determine which optimizations can be applied. We introduce the concept of a contract to the standard data flow network design. This contract enables each component of the data flow network to modify the set of optimizations used. In addition, the contract allows for new components to be accommodated gracefully within VisIt's data flow network system.

We present an octree-based approach for isosurface extraction from large volumetric scalar-valued data. Given scattered points with associated function values, we impose an octree structure of relatively low resolution. Octree construction is controlled by original data resolution and cell-specific error values. For each cell in the octree, we compute an average function value and additional statistical data for the original points inside the cell. Once a specific isovalue is specified, we adjust the initial octree by expanding its leaves based on a comparison of statistics with the isovalue. We tetrahedrize the centers of the octree's cells to determine tetrahedral meshes decomposing the entire spatial domain of the data, including a possibly specific region of interest (ROI). Extracted isosurfaces are crack-free inside an ROI, but cracks can appear at the boundary of an ROI. The inital isosurface is an approximation of the exact one, but its quality suffices for a viewer to identify an ROI where more accuracy is desirable. In the refinement process, we refine affected octree nodes and update the triangulation locally to produce better isosurface representations. This adaptive and user-driven refinement provides a means for interactive data exploration via real-time and local isosurface extraction.