ITR: Collaborative Research: Generating an Accurate Sense of Depth and Size Using Computer Graphics

ITR：协作研究：使用计算机图形生成准确的深度和大小感

• 批准号: 0080986
• 负责人: Karen Sutherland
• 金额: $ 10.08万
• 依托单位: Augsburg University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-09-01 至 2004-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0080986&HistoricalAwards=false
• 关键词: ITR; Collaborative; Research; Generating; Accurate

Despite impressive gains in realism over the last decade, computer graphics is currently unable to effectively generate images of objects and environments that look large. This is mostly because computer graphics is poor at conveying information about absolute depth. The goal of this project is to demonstrate that it is possible to significantly improve the sense of depth and scale in computer graphics if rendering methods are developed with specific attention to the need to convey cues for absolute depth. Accomplishing this goal will require new insights into the 3D information extractable from 2D images, modifications to graphics algorithms in order to better render salient information, and sophisticated perceptual experimentation to validate that people can actually see the intended 3D space. The PI's approach will be to draw upon the results and methods of computational vision in ways that have not previously been done in the computer graphics community. Computational vision provides insights into the intrinsic constraints on how information about 3D space can be recovered from 2D images. In particular, the computational analysis of vision points out the important distinction between relative depth judgments and absolute depth judgments. Surprisingly few of the commonly studied image cues are in fact sufficient to provide information about absolute depth. Of those that do, several cannot be exploited in computer graphics due to fundamental limitations in display technology and our inability to precisely control viewing conditions except in immersive environments. The research will impact a broad range of graphics applications in which accurate spatial information needs to be conveyed, including education and training, design and prototyping, and telepresence.

尽管在过去的十年中，计算机图形学在现实主义方面取得了令人印象深刻的进展，但目前无法有效地生成看起来很大的物体和环境的图像。这主要是因为计算机图形在传达绝对深度信息方面很差。这个项目的目标是证明，它是可能的，以显着提高在计算机图形的深度和规模的感觉，如果渲染方法的开发，特别注意需要传达绝对深度的线索。实现这一目标将需要对从2D图像中提取的3D信息有新的见解，修改图形算法以更好地呈现突出信息，并进行复杂的感知实验以验证人们实际上可以看到预期的3D空间。PI的方法将以计算机图形学界以前没有做过的方式利用计算视觉的结果和方法。计算视觉提供了对如何从2D图像中恢复有关3D空间的信息的内在约束的见解。特别是，视觉的计算分析指出了相对深度判断和绝对深度判断之间的重要区别。令人惊讶的是，很少有通常研究的图像线索实际上足以提供有关绝对深度的信息。其中，有几个不能在计算机图形中利用，因为显示技术的基本限制和我们无法精确控制观看条件，除非在沉浸式环境中。该研究将影响广泛的图形应用，其中需要传达准确的空间信息，包括教育和培训，设计和原型设计以及远程呈现。