O-buffer: A Framework for Sample Graphics

O-buffer：示例图形的框架

• 批准号: 0306438
• 负责人: Arie Kaufman
• 金额: $ 35万
• 依托单位: SUNY at Stony Brook
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-08-01 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0306438&HistoricalAwards=false
• 关键词: buffer; Framework; Sample; Graphics

This proposal introduces an innovative modeling and rendering primitive, called the O-buffer, for sample-based graphics, such as images, volumes, and points. The two-dimensional or three-dimensional O-buffer is in essence a conventional image or a volume, respectively, except that samples are not restricted to a regular grid. A sample position in the O-buffer is recorded as an offset to the nearest grid point of a regular base grid (hence the name O-buffer). The offset is typically quantized for compact representation and efficient rendering. This project investigates and develops representations, algorithms, applications, and a software system for O-buffers. The intellectual merits of the O-buffer are that it is more precise than a conventional image or volume, provides a more compact representation relative to a finer grid, accelerates rendering and supports low-bandwidth communications, improves image quality, is a flexible representation (e.g., non-regular, non-uniform, multi-resolution), and is a unified framework that can represent, render, and mix unstructured primitives. O-buffers can be harnessed in numerous applications, such as improving simulation systems, scientific visualization, Internet graphics, design and manufacturing, and health care, as well as enhancing and upgrading many other computer systems that use sampled-based data, such as images.

该提案引入了一种创新的建模和渲染原语，称为O缓冲区，用于基于样本的图形，如图像，体积和点。除了样本不限于规则网格之外，二维或三维O缓冲区本质上分别是常规图像或体积。O-缓冲区中的采样位置被记录为到规则基础网格的最近网格点的偏移（因此称为O-缓冲区）。偏移通常被量化以用于紧凑表示和高效渲染。本项目研究和开发O-缓冲器的表示、算法、应用程序和软件系统。O缓冲器的智能优点是它比传统图像或体积更精确，相对于更精细的网格提供更紧凑的表示，加速渲染并支持低带宽通信，提高图像质量，是灵活的表示（例如，非规则、非均匀、多分辨率），并且是可以表示、呈现和混合非结构化基元的统一框架。O缓冲器可以用于许多应用，例如改进仿真系统、科学可视化、互联网图形、设计和制造以及医疗保健，以及增强和升级许多其他使用基于采样的数据（例如图像）的计算机系统。