Shaffer Eric, Garland Michael
Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
IEEE Trans Vis Comput Graph. 2005 Mar-Apr;11(2):139-48. doi: 10.1109/TVCG.2005.18.
We present a new external memory multiresolution surface representation for massive polygonal meshes. Previous methods for building such data structures have relied on resampled surface data or employed memory intensive construction algorithms that do not scale well. Our proposed representation combines efficient access to sampled surface data with access to the original surface. The construction algorithm for the surface representation exhibits memory requirements that are insensitive to the size of the input mesh, allowing it to process meshes containing hundreds of millions of polygons. The multiresolution nature of the surface representation has allowed us to develop efficient algorithms for view-dependent rendering, approximate collision detection, and adaptive simplification of massive meshes. The empirical performance of these algorithms demonstrates that the underlying data structure is a powerful and flexible tool for operating on massive geometric data.
我们提出了一种用于大规模多边形网格的新型外部内存多分辨率曲面表示方法。以前构建此类数据结构的方法依赖于重新采样的曲面数据，或者采用内存密集型构建算法，而这些算法扩展性不佳。我们提出的表示方法将对采样曲面数据的高效访问与对原始曲面的访问相结合。曲面表示的构建算法所展现出的内存需求对输入网格的大小不敏感，使其能够处理包含数亿个多边形的网格。曲面表示的多分辨率特性使我们能够开发出用于视点相关渲染、近似碰撞检测以及大规模网格自适应简化的高效算法。这些算法的实际性能表明，底层数据结构是处理大规模几何数据的强大且灵活的工具。
