CPA-G&V: Compression Techniques for Direct Rendering

CPA-G

• 批准号: 0811809
• 负责人: Gopi Meenakshisundaram
• 金额: $ 32.5万
• 依托单位: University of California-Irvine
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811809&HistoricalAwards=false
• 关键词: CPA; G& Compression; Techniques; Direct

AbstractPI: Meenakshisundaram, Gopi (CCF ? 0811809) Current graphics and visualization systems have to be built such that they can handle gigantic data sets like those from large scientific simulations including nuclear and power simulations, and geometric data sets such as digital models of defense and commercial equipments such as aircraft, ships and power-plants. Such large data sets cannot fit into the main memory of the computers or be rendered interactively in current graphics systems. This project involves fundamental research in designing algorithms for efficient compression of these large data sets that enables fast decompression of the required portion of the data set in the main-memory and efficient interactive rendering of these data sets. This study is exploring three research directions to solve the problem of interactive walkthrough of gigantic data sets: syntactic compression, semantic compression, and access sensitive data layouts. Syntactic compression deals with compressing data bits. In this project, compression algorithms that exhibit properties like random-access decompression and stop-any-time decompression are studied. Semantic compression deals with representing objects with fewer primitives. In this context, this research studies parameterizable semantic compression algorithms that trades-off space for compression efficiency. Finally, optimal data layouts depend on application, and this study explores optimal data layout schemes of the 3D data sets on external memory for interactive walkthrough applications. This includes partitioning of the 3D data set using different metrics like normal vector deviation and spatial distance between objects, and computing the linear layout of these partitions in the external memory using graph algorithms. The research is significantly improving the ability to render large data sets with applications across computer graphics and visualization as well as other application areas such as military and rescue simulations.

摘要PI：Meenakshisundaram，Gopi (CCF ?当前的图形和可视化系统必须能够处理来自大型科学模拟（包括核能和电力模拟）的巨大数据集，以及诸如国防和商用设备（如飞机、船舶和发电厂）的数字模型等几何数据集。这样大的数据集不能放入计算机的主存储器中，也不能在当前的图形系统中交互地呈现。该项目涉及设计算法的基础研究，用于有效压缩这些大型数据集，从而能够快速解压缩主存中所需的数据集部分，并有效地交互呈现这些数据集。本研究探讨三个研究方向，以解决互动漫游的问题，巨大的数据集：语法压缩，语义压缩，访问敏感的数据布局。语法压缩处理压缩数据位。在这个项目中，压缩算法，表现出像随机访问解压缩和停止任何时间解压缩的属性进行了研究。语义压缩处理用更少的原语表示对象。在此背景下，本研究研究可参数化的语义压缩算法，权衡压缩效率的空间。最后，最佳的数据布局取决于应用程序，本研究探讨了交互式漫游应用程序的外部存储器上的三维数据集的最佳数据布局方案。这包括使用不同的度量（如法向量偏差和对象之间的空间距离）对3D数据集进行分区，并使用图形算法计算这些分区在外部存储器中的线性布局。这项研究显著提高了渲染大型数据集的能力，这些数据集应用于计算机图形和可视化以及其他应用领域，如军事和救援模拟。