Publications

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 three-dimensional 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 applied to a variety of problems.

Abstract not provided.

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. Often, however, points are generated such that there is additional information available to the reconstruction algorithm. We present 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 fills itself in.