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Large Mesh Simplification using Processing Sequences

机译:使用处理序列大的网格简化

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In this paper we show how out-of-core mesh processing techniques can be adapted to perform their computations based on the new processing sequence paradigm [Isenburg and Gumhold 2003; Isenburg et al. 2003], using mesh simplification as an example. We believe that this processing concept will also prove useful for other tasks, such as parameterization, remeshing, or smoothing, for which currently only in-core solutions exist. A processing sequence represents a mesh as a particular interleaved ordering of indexed triangles and vertices. This representation allows streaming very large meshes through main memory while maintaining information about the visitation status of edges and vertices. At any time, only a small portion of the mesh is kept in-core, with the bulk of the mesh data residing on disk. Mesh access is restricted to a fixed traversal order, but full connectivity and geometry information is available for the active elements of the traversal. This provides seamless and highly efficient out-of-core access to very large meshes for algorithms that can adapt their computations to this fixed ordering. The two abstractions that are naturally supported by this representation are boundary-based and buffer-based processing. We illustrate both abstractions by adapting two different simplification methods to perform their computation using a prototype of our mesh processing sequence API. Both algorithms benefit from using processing sequences in terms of improved quality, more efficient execution, and smaller memory footprints.
机译:在本文中,我们展示了如何基于新的处理序列范式[Isenburg和Gumhold 2003; 2003的新核心网格处理技术如何适应核心网格处理技术。 Isenburg等人。 2003],使用网格简化为例。我们认为,此处理概念还将对其他任务进行有用,例如参数化,remeshing或平滑,其中当前仅存在核心解决方案。处理序列表示作为索引三角形和顶点的特定交织排序的网格。该表示允许通过主存储器流式传输非常大的网格,同时维持有关边缘和顶点的探视状态的信息。在任何时候,只有一小部分网格保存在核心,其中大部分网状数据驻留在磁盘上。网格访问仅限于固​​定的遍历顺序,但是完全连接和几何信息可用于遍历的活动元素。这为对算法的非常大的网格提供了无缝和高效的核心访问,可以使其计算适应该固定排序。这种表示自然支持的两个抽象是基于边界和基于缓冲的处理。我们通过调整两种不同的简化方法来使用网眼处理序列API的原型来执行两种不同的简化方法来说明两种抽象。这两种算法都会在提高质量,更有效的执行和更小的内存占地面积方面使用处理序列。

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