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.

该项目旨在增进对基于能量的立体匹配技术的理解。 该研究将使用具有真实差异的新立体数据集来解决以下问题：“最小化的正确能量函数是什么？” 具体目标包括开发一个自动化系统，用于获取具有地面实况的高精度多视图立体数据集，并设计新的能量函数，其最小化会产生差异，(1) 准确建模遮挡，(2) 使用单目将遮挡边界与对象边界对齐线索，以及（3）在半遮挡区域创建合理的视差假设。 在这些具有挑战性的任务中表现良好的立体算法在新兴的消费级应用中具有重要的应用，包括用于电话会议的虚拟凝视校正，以及用于 3D 可视化的基于图像的对象模型的创建。 该研究还将重点开发快速、近似能量最小化技术，包括图割和动态规划方法的变体。 本科生将积极参与这项研究的所有组成部分，特别是在数据采集和测试阶段。 该项目的智力优点是为计算机视觉社区提供高质量、具有地面实况的多视图数据集，并提高基于能量的图像匹配技术的最先进水平。 该项目更广泛的影响包括提高计算机视觉方法在电信和电子商务等日益成为社会核心的应用中的适用性；以及让文理学院的本科生接触研究、实验和发现世界的机会。