No abstract

Many applications produce three-dimensional points that must be further processed to generate a surface. Surface reconstruction algorithms that start with a set of unorganized points are extremely time-consuming. Sometimes, however, points are generated such that there is additional information available to the reconstruction algorithm. We present Spiraling Edge, a specialized algorithm for surface reconstruction that is three orders of magnitude faster than algorithms for the general case. In addition to sample point locations, our algorithm starts with normal information and knowledge of each point's neighbors. Our algorithm produces a localized approximation to the surface by creating a star-shaped triangulation between a point and a subset of its nearest neighbors. This surface patch is extended by locally triangulating each of the points along the edge of the patch. As each edge point is triangulated, it is removed from the edge and new edge points along the patch's edge are inserted in its place. The updated edge spirals out over the surface until the edge encounters a surface boundary and stops growing in that direction, or until the edge reduces to a small hole that is filled by the final triangle.

Visualization systems are complex dynamic software systems. Debugging such systems is difficult using conventional debuggers because the programmer must try to imagine the threedimensional geometry based on a list of positions and attributes. In addition, the programmer must be able to mentally animate changes in those positions and attributes to grasp dynamic behaviors within the algorithm. In this paper we shall show that representing geometry, attributes, and relationships graphically permits visual pattern recognition skills to be applied to the debugging problem. The particular application is a particle system used for isosurface extraction from volumetric data. Coloring particles based on individual attributes is especially helpful when these colorings are viewed as animations over successive iterations in the program. Although we describe a particular application, the types of tools that we discuss can be app~edto a varietyofproblems.

In researching the communication mechanisms between cells of the immune system, visualization of proteins in three dimensions can be used to determine which proteins are capable of interacting with one another at a given time by showing their spatial colocality. Volume data sets are created using digital confocal immunofluorescence microscopy. A variety of visualization approaches are then used to examine the interactions. These include volume rendering, isosurface extraction, and virtual reality. Based on our experiences, we have concluded that no single one of these approaches provides a complete solution for visualizing biological data. However, in combination, their respective strengths complement one another to provide an understanding of the data.

We present an innovative application developed at Sandia National Laboratories for visual debugging of unstructured finite element physics codes. Our tool automatically locates anomalous regions, such as inverted elements or nodes whose variable values lie outside a prescribed range, then extracts mesh subsets around these features for detailed examination. The subsets are viewed using color coding of variable values superimposed on the mesh structure. This allows the values and their relative spatial locations within the mesh to be correlated at a glance. Both topological irregularities and hot spots within the data stand out visually, allowing the user to explore the exact numeric values of the grid at surrounding points over time. We demonstrate the utility of this approach by debugging a cell inversion in a simulation of an exploding wire.