Mikio Nagasawa

Reducing Polygonal Data by Structural Grouping Algorithm

In the field of computer graphics, polygonal representations are used for modeling three-dimensional geometrical objects. When recognizing structural characteristics, however, they often have much redundancy. Large numbers of polygons are difficult to render on graphic workstations or transfer over the network. To allow remote handling of polygonal data in virtual reality environments, the rational reduction of polygonal data is required. This paper describes a new algorithm which reduces the number of such polygonal primitives without losing the detailed structures of an object. This method is a kind of grouping algorithm and is effective for reducing structural redundancy. In the reducing process, we merge adjoining polygons, which satisfy a given coplanar criterion, into one plane. This grouping method is designed not to destroy the object structural patterns. The geometrical data are divided into groups satisfying the condition of required accuracy. The local reduction rule of polygons is applied to each classified group. We test this reducing method with geometric models representing human faces. The effectiveness for reducing polygon numbers and keeping the 3-D rendered image quality is investigated.

Volume data compression using smoothed particle transformation

In 3D entertainment oriented games, most geometrical objects are represented by polygon sets. Thus, the handling of polygons is highly optimized in the hardware of graphic workstations. However, 3D scientific simulations do not ordinarily use polygon data but rather volume data in its original form, such as the scalar array p(x,y,z). If we had an effective volume data compression algorithm and a standard way of representing this data, it could be used in a more efficient manner in the network environment. This paper presents a Smoothed Particle Transformation (SPT) algorithm for volume data at several levels of compressed resolution from an original array description of a given variable. Representing data at various levels of detail is important for archiving the huge data in the network environment. SPT contributes not only to data compression but also for resolution rearrangement. As a result, it speeds up of the visualization of transferred data, especially for the direct volume rendering of compressed data.

Direct tensor rendering using a bidirectional reflectance model

For the multi variable volumetric tensor field visualization, an efficient direct rendering technique without using geometrical primitive is proposed. The bi- directional reflectance shading model is used to map the anisotropy stress shear tensor components in direct volume rendering. We model the sub-pixel-sized microfacet at tensor sampling points. The nine component of 3D tensor field are mapped onto grid deformation, opacity mapping, color specification, and normal directions of these microfacets. The ray integration is executed though these irregular infinitesimal microfacets distribution. This direct tensor rendering was applied for at-a-glance tensor visualization of earthquake simulation. That realized a view of deformed structure, stress distribution, local shear discontinuity and the shock front, integrated in a single image. The characteristic P- and S-wave modes are distinguished in the rendered earthquake simulations. Compared with the glyph representation of tensor features, the direct tensor rendering gives the general and total image of tensor field even for the low resolution pixel planes, because the sampling object is assumed as infinitesimally small. the computational cost of direct tensor rendering is not so high than that of scalar volume rendering because the modifications are only ins hading calculation but not in the ray integration.