Almost all scientific visualization involving surfaces is currently done via triangles. The speed at which such triangulated surfaces can be displayed is crucial to interactive visualization and is bounded by the rate at which triangulated data can be sent to the graphics subsystem for rendering. Partitioning polygonal models into triangle strips can significantly reduce rendering times over transmitting each triangle individually.In this paper, we present new and efficient algorithms for constructing triangle strips from partially triangulated models, and experimental results showing these strips are on average 15% better than those from previous codes. Further, we study the impact of larger buffer sizes and various queuing disciplines on the effectiveness of triangle strips.

Triangle strips are a widely used hardware-supported data-structure to compactly represent and efficiently render polygonal meshes. In this paper we survey the efficient generation of triangle strips as well as their variants. We present efficient algorithms for partitioning polygonal meshes into triangle strips. Triangle strips have traditionally used a buffer size of two vertices. In this paper we also study the impact of larger buffer sizes and various queuing disciplines on the effectiveness of triangle strips. View-dependent simplification has emerged as a powerful tool for graphics acceleration in visualization of complex environments. However, in a view-dependent framework the triangle mesh connectivity changes at every frame making it difficult to use triangle strips. In this paper we present a novel data-structure, Skip Strip, that efficiently maintains triangle strips during such view-dependent changes. A Skip Strip stores the vertex hierarchy nodes in a skip-list-like manner with path compression. We anticipate that Skip Strips will provide a road-map to combine rendering acceleration techniques for static datasets, typical of retained-mode graphics applications, with those for dynamic datasets found in immediate-mode applications.