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

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升到实用水平。 该项目旨在弥合在当前基准测试中表现最好的复杂但脆弱的方法与真实世界应用中采用的稳健但简单的方法之间的差距。该项目提供了具有准确地面实况的新的高分辨率数据集，这些数据集使用不同的相机在不同的照明条件下拍摄，并描绘了具有非朗伯表面和离群值（如移动的人，反射和阴影）的复杂室内和室外场景。 该项目探索了应对这些挑战的新算法方法，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推迟到区域级。 本科生积极参与这项研究的所有组成部分。该项目在沿着几个方面具有强大的潜在影响。 这项工作产生的数据集和基准可以作为新研究的催化剂，并使机器学习方法成为可能。 算法的贡献允许利用在线图像的爆炸。 强大的匹配技术可以处理互联网上可用的各种图像，从而实现了许多对广大人口产生广泛影响的新应用，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，该项目使佛蒙特州农村一所文科学院的本科生接触到研究、实验和发现的世界。

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.