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Out-of-core multiresolution techniques for graphics compression and volume visualization of large datasets.

机译：用于大型数据集的图形压缩和体积可视化的核心外多分辨率技术。

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摘要

The rapid growth of the data size in recent years has made out-of-core techniques indispensable, where the datasets are too large to fit in main memory and we want to design new computational algorithms to reduce the I/O communications between main memory and disk. In this thesis we present our new out-of-core techniques for graphics compression and volume visualization of large datasets. Our focus is on multiresolution/level-of-detail (LOD) approaches, where we simplify the data and build a multiresolution hierarchy so that we can render the data at just the right level to achieve both high image quality and fast computing speed, in the out-of-core setting where the techniques work well for datasets larger than main memory.;We develop out-of-core multiresolution approaches on three different types of datasets and computing tasks. First we focus on 3D triangle meshes, and develop a novel progressive lossless compression algorithm that supports selective decompression. We then extend our study to time-varying regular-grid volume rendering. We explore the temporal and spatial coherences of the dataset to speed up the volume rendering, by developing a new tree structure that supports a high re-use rate of the sub-volumes, as well as devising the corresponding I/O efficient algorithms that are novel and highly non-trivial. Finally, we develop a volume rendering algorithm for irregular-grid volume data represented as tetrahedral meshes. We devise a novel out-of-core simplification and level-of-detail (LOD) volume rendering algorithm where the underlying LOD mesh is guaranteed to be crack-free, namely, any neighboring sub-volumes in the LOD mesh have consistent boundaries, and all the cells in the LOD mesh are fold-over free (i.e., do not have negative volumes). Our technique supports selective refinement LODs, in addition to the basic uniform LODs. The proposed scalar-value range and view-dependent selection queries for selective refinement are especially effective in producing images of the highest quality with a much faster rendering speed. We present experimental results which show the efficacy of these new out-of-core techniques.
机译：近年来，数据大小的快速增长已使核心技术成为必不可少的技术，其中数据集太大而无法容纳在主存储器中，我们希望设计新的计算算法以减少主存储器与主存储器之间的I / O通信。磁盘。在本文中，我们介绍了用于大型数据集的图形压缩和体积可视化的新核心技术。我们的重点是多分辨率/细节级别（LOD）方法，在此方法中，我们简化了数据并建立了多分辨率层次结构，以便我们可以在适当的级别上渲染数据，以实现高质量的图像质量和快速的计算速度。在核心外设置中，该技术适用于大于主内存的数据集。;我们针对三种不同类型的数据集和计算任务开发了核心外多分辨率方法。首先，我们专注于3D三角形网格，并开发一种支持选择性减压的新颖渐进式无损压缩算法。然后，我们将研究扩展到随时间变化的常规网格体绘制。我们通过开发支持子卷的高重用率的新树结构以及设计相应的I / O高效算法来探索数据集的时间和空间一致性，以加快体积渲染。新颖且高度平凡。最后，我们针对表示为四面体网格的不规则网格体数据开发了体绘制算法。我们设计了一种新颖的核心外简化和细节层次（LOD）体积渲染算法，其中保证了底层LOD网格无裂纹，即，LOD网格中的任何相邻子体积都具有一致的边界，并且LOD网格中的所有像元都没有折叠（即没有负体积）。除了基本的统一LOD外，我们的技术还支​​持选择性优化LOD。提出的标量值范围和依赖视图的选择查询用于选择性细化在以更快的渲染速度生成最高质量的图像时特别有效。我们提供的实验结果表明了这些新的核心技术的功效。

著录项

  • 作者

    Du, Zhiyan.;

  • 作者单位

    Polytechnic Institute of New York University.;

  • 授予单位 Polytechnic Institute of New York University.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2010
  • 页码 109 p.
  • 总页数 109
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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