RUI: Ground-Truth Driven Stereo Algorithm Design

RUI：地面实况驱动的立体算法设计

• 批准号: 0413169
• 负责人: Daniel Scharstein
• 金额: $ 23.4万
• 依托单位: Middlebury College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2008-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0413169&HistoricalAwards=false
• 关键词: RUI; Ground; Truth; Driven; Stereo

This project aims to advance the understanding of energy-based stereo-matching techniques. Using new stereo data sets with ground-truth disparities, the research will address the question: "What is the right energy function to minimize?" The specific goals include developing an automated system for obtaining highly accurate multi-view stereo data sets with ground truth, and designing new energy functions whose minimization yields disparities that (1) accurately model occlusion, (2) align occlusion boundaries with object boundaries using monocular cues, and (3) create reasonable disparity hypotheses in half-occluded regions. Stereo algorithms that perform well on these challenging tasks have important applications in emerging consumer-level applications, including virtual gaze correction for teleconferencing, and the creation of image-based object models for 3D visualization. The research will also focus on developing fast, approximate energy minimization techniques, including variants of graph-cut and dynamic programming methods. Undergraduate students will be actively involved in all components of this research, in particular in the data acquisition and testing stages. The intellectual merits of the project are to provide the computer vision community with high-quality, multi-view data sets with ground truth and to improve the state of the art in energy-based image-matching techniques. The broader impacts of the project include the improved applicability of computer vision methods to applications that are becoming central to society, such as telecommunication and e-commerce; and the opportunity to expose undergraduates at a liberal-arts college to the world of research, experimentation, and discovery.

该项目旨在提高对基于能量的立体声匹配技术的理解。 该研究使用新的立体声数据集，研究将解决以下问题：“最小化的正确能量功能是什么？” 具体目标包括开发一个自动化系统，该系统用于与地面真理获得高度准确的多视图立体数据集，并设计新的能量功能，其最小化产生的差异（1）准确地模拟了遮挡，（（2）使用单眼线索将对象界限与对象界限保持一致，并创建合理的差异不同，（3）创建了一半的假设。 在这些具有挑战性的任务上表现良好的立体声算法在新兴的消费级应用程序中具有重要的应用程序，包括用于电信的虚拟凝视校正以及创建基于图像的对象模型以进行3D可视化。 该研究还将着重于开发快速，近似能量最小化技术，包括剪图和动态编程方法的变体。 本科生将积极参与这项研究的所有组成部分，尤其是数据获取和测试阶段。 该项目的智力优点是为计算机视觉社区提供具有地面真相的高质量多视图数据集，并在基于能量的图像匹配技术中改善最新技术。 该项目的更广泛影响包括改善计算机视觉方法对正在成为社会核心的应用程序（例如电信和电子商务）的应用程序；并有机会在一所自由派艺术学院揭露本科生的研究，实验和发现的世界。