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Integration of volume decompression and out-of-core iso-surface extraction from irregular volume data

机译:从不规则体积数据中进行体积减压和核外等值面提取的集成

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摘要

Volume datasets tend to grow larger and larger as modern technology advances, thus imposing a storage constraint on most systems. One general solution to alleviate this problem is to apply volume compression on volume datasets. However, as volume rendering is often the most important reason why a volume dataset was generated in the first place, we must take into account how a volume dataset could be efficiently rendered when it is stored in a compressed form. Our previous work has shown that it is possible to perform an on-the-fly direct volume rendering from irregular volume data. In this paper, we further extend that work to demonstrate that a similar integration can also be achieved on iso-surface extraction and volume decompression for irregular volume data. In particular, our work involves a dataset decomposition process, where instead of a coordinate-based decomposition used by conventional out-of-core iso-surface extraction algorithms, we choose to use a layer-based structure. Each such layer contains a collection of tetrahedra whose associated scalar values fall within a specific range, and can be compressed independently to reduce its storage requirement. The layer structure is particularly suitable for out-of-core iso-surface extraction, where the required memory exceeds the physical memory capacity of the machine that the process is running on. Furthermore, with this work, we can perform on-the-fly iso-surface extraction during decompression, and the computation only involves the layer that contains the query value, rather than the entire dataset. Experiments show that our approach can improve the performance up to ten times when compared with the results based on traditional coordinate-based approaches.
机译:随着现代技术的发展,体积数据集趋于越来越大,从而对大多数系统施加了存储限制。缓解此问题的一种通用解决方案是将体积压缩应用于体积数据集。但是,由于体积渲染通常是首先生成体积数据集的最重要原因,因此我们必须考虑到以压缩形式存储体积数据集时如何有效地呈现。我们以前的工作表明,可以从不规则的体积数据中即时执行直接体积渲染。在本文中,我们进一步扩展了这项工作,以证明对于不规则的体积数据,在等值面提取和体积减压中也可以实现类似的集成。特别地,我们的工作涉及数据集分解过程,在该过程中,我们选择使用基于层的结构,而不是常规的核心外等值面提取算法所使用的基于坐标的分解。每个这样的层都包含一个四面体的集合,其相关标量值在特定范围内,可以独立压缩以减少其存储需求。该层结构特别适用于核心外等值面提取,其中所需的内存超过了运行该进程的计算机的物理内存容量。此外,通过这项工作,我们可以在解压缩过程中即时进行等值面提取,并且计算仅涉及包含查询值的图层,而不是整个数据集。实验表明,与传统的基于坐标的方法相比,我们的方法可以将性能提高十倍。

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