RI:Small:RUI: Towards the Next Generation of Stereo Algorithms

RI:Small:RUI：迈向下一代立体算法

• 批准号: 0917109
• 负责人: Daniel Scharstein
• 金额: $ 24.5万
• 依托单位: Middlebury College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917109&HistoricalAwards=false
• 关键词: RI; Small; RUI; Towards; Next

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This project provides challenging test data and benchmarks designed to advance stereo vision methods to a level of practical relevance. It aims to bridge the gap between the sophisticated but brittle methods that perform best on current benchmarks and the robust but simple methods employed in real-world applications.The project provides new high-resolution datasets with accurate ground truth, taken with different cameras under different lighting conditions, and depicting complex indoor and outdoor scenes with non-Lambertian surfaces and outliers such as moving people, reflections, and shadows. The project explores novel algorithmic approaches for dealing with such challenges, including ways to leverage resolution, deriving color and noise models on the fly, and designing local region-growing techniques that allow deferring global optimization from the pixel level to the region level. Undergraduate students are actively involved in all components of this research.The project has strong potential impact along several fronts. The datasets and benchmarks resulting from this work serve as catalyst for new research and enable machine learning approaches. The algorithmic contributions allow harnessing the explosion of images available online. Robust matching techniques that can handle the variety of images available on the Internet enable a host of new applications with broad impacts on the population at large, including visual localization and navigation, as well as automatic 3D reconstruction and visualization of whole cities. Finally, the project exposes undergraduates at a liberal-arts college in rural Vermont to the world of research, experimentation, and discovery.

该奖项是根据2009年美国复苏和再投资法案(公法111-5)资助的。该项目提供了具有挑战性的测试数据和基准，旨在将立体视觉方法提高到具有实际意义的水平。该项目旨在弥合在当前基准上表现最好的复杂但脆弱的方法与实际应用中使用的稳健但简单的方法之间的差距。该项目提供了新的高分辨率数据集，具有准确的地面真实信息，使用不同的摄像头在不同的照明条件下拍摄，并描述具有非朗伯表面和异常值的复杂室内和室外场景，如移动的人、反射和阴影。该项目探索了应对此类挑战的新算法方法，包括利用分辨率的方法，动态推导颜色和噪声模型，以及设计局部区域增长技术，允许将全局优化从像素级别推迟到区域级别。本科生积极参与这项研究的所有组成部分。该项目在几个方面都有很强的潜在影响。这项工作产生的数据集和基准充当了新研究的催化剂，并使机器学习方法成为可能。算法方面的贡献允许利用网上可用图像的爆炸性增长。强大的匹配技术可以处理互联网上的各种图像，使许多对广大人口产生广泛影响的新应用得以实现，包括视觉定位和导航，以及整个城市的自动3D重建和可视化。最后，该项目让佛蒙特州乡下一所文理学院的本科生接触到研究、实验和发现的世界。