Feature-based Rendering

基于特征的渲染

• 批准号: 0539996
• 负责人: Kavita Bala
• 金额: $ 7.5万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-15 至 2006-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0539996&HistoricalAwards=false
• 关键词: Feature; based; Rendering

This research investigates a way to efficiently produce realistic computer graphics images. Applications of computer graphics are placing an ever-increasing demand on the ability to produce accurate, convincing images of complex scenes. Examples of such applications include architectural and engineering design, virtual training, telecollaboration, and game and movie rendering. However, much of the scene complexity is usually irrelevant to how humans perceive the rendered image. The approach explored by this research isto exploit the limitations of the human visual system by automatically focusing computational effort on the visual features that are important for convincing the eye, while saving time where the eye would be insensitive to the difference. This research develops new, feature-based computer graphics rendering techniques that more efficiently handle the large, complex, realistic scenes needed by future applications.The goal of the research is a scalable, feature-based graphics pipeline that exposes features explicitly at every level from modeling to the final rendered image. If computational work is proportional to visual features, the computational cost is proportional to the intrinsic visual complexity of the output image rather than to other measures of scene complexity such as polygon count. Such a graphics pipeline is fundamentally more scalable. The researchers are investigating efficient algorithms for finding visually important features, new feature-based scene and display representations for high-quality modeling and display, and new rendering algorithms that exploit features to provide scalable, efficient, high-quality image synthesis.

这项研究探讨了一种有效生成逼真计算机图形图像的方法。 计算机图形学的应用对生成复杂场景的准确、令人信服的图像的能力提出了越来越高的要求。此类应用的示例包括建筑和工程设计、虚拟培训、远程协作以及游戏和电影渲染。然而，大部分场景复杂性通常与人类如何感知渲染图像无关。 这项研究探索的方法是利用人类视觉系统的局限性，自动将计算工作集中在对说服眼睛很重要的视觉特征上，同时节省眼睛对差异不敏感的时间。 这项研究开发了新的、基于特征的计算机图形渲染技术，可以更有效地处理未来应用程序所需的大型、复杂、逼真的场景。该研究的目标是建立一个可扩展的、基于特征的图形管道，它可以在每个级别上显式地暴露特征建模到最终渲染的图像。 如果计算工作与视觉特征成正比，则计算成本与输出图像的内在视觉复杂性成正比，而不是与场景复杂性的其他度量（例如多边形计数）成正比。这样的图形管道从根本上来说更具可扩展性。研究人员正在研究寻找视觉重要特征的有效算法、用于高质量建模和显示的基于特征的新场景和显示表示，以及利用特征提供可扩展、高效、高质量图像合成的新渲染算法。