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.

尽管在过去十年中，现实主义的成就令人印象深刻，但计算机图形当前仍无法有效地生成看起来很大的对象和环境的图像。这主要是因为计算机图形在传达有关绝对深度的信息方面很差。该项目的目的是证明，如果开发渲染方法，则可以特别注意传达最深度的提示，可以显着提高计算机图形的深度和规模感。实现此目标将需要从2D图像中提取的3D信息，对图形算法进行修改，以更好地渲染显着信息，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的方法是以以前在计算机图形社区中没有完成的方式来利用计算视觉的结果和方法。计算视觉提供了有关如何从2D图像中恢复有关3D空间信息的内在约束的见解。特别是，视觉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，很少有经常研究的图像提示实际上足以提供有关绝对深度的信息。在那些这样做的事情中，由于显示技术的基本限制以及我们无法精确控制观看条件，因此在计算机图形中无法利用几个。这项研究将影响需要传达准确的空间信息的广泛图形应用，包括教育和培训，设计和原型以及远程敏感。