ZSWEEP: An Efficient and Exact Projection Algorithm for Unstructured Volume Rendering
ZSWEEP: An Efficient and Exact Projection Algorithm for Unstructured Volume Rendering
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DOI：
10.1145/353888.353905
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发表时间：
2000-10
期刊：
影响因子：
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通讯作者：
R. Farias;Joseph S. B. Mitchell;Cláudio T. Silva
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文献类型：
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作者：
R. Farias;Joseph S. B. Mitchell;Cláudio T. Silva
我们提出了一个简单的新算法，执行快速和内存高效的细胞投影（精确）渲染非结构化数据集。“ZSweep”算法的主要思想非常简单;它基于用平行于观察平面的平面扫描数据，以增加z的顺序，投影入射到顶点的单元的面，因为它们被扫描平面遇到。效率来自于这样的事实，即该算法利用隐式（近似）的全局排序，顶点的z-排序引起的细胞上的事件。该算法通过投影每个面来投影细胞，特别注意避免内部面的双重投影并确保投影顺序的正确性。在扫描期间，使用有序面交点的短列表分阶段计算每个像素的贡献，已知该列表在计算的每个阶段完成的时刻是正确和完整的。ZSweep算法足够简单，可以很容易地适应一般（非四面体）单元格式。它是内存高效的，因为它的辅助数据结构只需要存储从数据集的少量“切片”中获取的部分信息。我们还介绍了一个简单的数据稀疏化技术，这可能是在其本身的权利感兴趣。我们的实现是硬件独立的，处理数据集包含四面体和/或六面体细胞。我们给出的实验证据表明，我们的方法是有竞争力的，高达5倍的速度比以前已知的最好的精确算法，使用相当数量的内存，而使用更少的内存比光线投射。
We present a simple new algorithm that performs fast and memory-efficient cell projection for (exact) rendering of unstructured datasets. The main idea of the "ZSweep" algorithm is very simple; it is based on sweeping the data with a plane parallel to the viewing plane, in order of increasing z, projecting the faces of cells that are incident to vertices as they are encountered by the sweep plane. The efficiency arises from the fact that the algorithm exploits the implicit (approximate) global ordering that the z-ordering of the vertices induces on the cells that are incident on them. The algorithm projects cells by projecting each of their faces, with special care taken to avoid double projection of internal faces and to assure correctness in the projection order. The contribution for each pixel is computed in stages, during the sweep, using a short list of ordered face intersections, which is known to be correct and complete at the instant that each stage of the computation is completed. The ZSweep algorithm is simple enough to be readily adaptable to general (non-tetrahedral) cell formats. It is memory efficient, since its auxiliary data structures have only to store partial information taken from a small number of "slices" of the dataset. We also introduce a simple technique of data sparsification, which may be of interest in its own right. Our implementation is hardware-independent and handles datasets containing tetrahedral and/or hexahedral cells. We give experimental evidence that our method is competitive, up to 5 times faster than the best previously-known exact algorithms that use comparable amounts of memory, while using much less memory than ray-casting.