JIHAD EL SANA

In this paper, we propose a novel external-memory algorithm to support view-dependent simplification for datasets much larger than main memory. In the preprocessing phase, we use a new spanned sub-meshes simplification technique to build view-dependence trees I/O-efficiently, which preserves the correct edge collapsing order and thus assures the run-time image quality. We further process the resulting view-dependence trees to build the meta-node trees, which can facilitate the run-time level-of-detail rendering and is kept in disk. During run-time navigation, we keep in main memory only the portions of the meta-node trees that are necessary to render the current level of details, plus some prefetched portions that are likely to be needed in the near future. The prefetching prediction takes advantage of the nature of the run-time traversal of the meta-node trees, and is both simple and accurate. We also employ the implicit dependencies for preventing incorrect foldovers, as well as main-memory buffer management and parallel processes scheme to separate the disk accesses from the navigation operations, all in an integrated manner. The experiments show that our approach scales well with respect to the main memory size available, with encouraging preprocessing and run-time rendering speeds and without sacrificing the image quality.