RI: Small: RUI: Image Matching in the Wild

RI：小：RUI：野外图像匹配

• 批准号: 1320715
• 负责人: Daniel Scharstein
• 金额: $ 23.61万
• 依托单位: Middlebury College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320715&HistoricalAwards=false
• 关键词: RI; Small; RUI; Image; Matching

This project aims to advance stereo vision and optical flow algorithms to work in challenging real-world conditions. It contributes novel algorithmic approaches, new high-resolution datasets with ground truth, and an update to the Middlebury benchmarks.The algorithmic advances include fast matching algorithms that employ radiometric and geometric self-calibration, including smart data terms that establish noise and color models during the matching process, and novel layer-based surface reconstruction algorithms with explicit reasoning about half-occluded regions, reflections, and transparency. The new datasets reflect current challenges, including high-resolution images of real-world scenes with complex occlusions, specular surfaces and reflections under different illuminations and taken with different cameras. Undergraduate students are actively involved in all components of this research.The project has strong potential impact along several fronts. The new datasets and benchmarks challenge the community and serve as catalysts for new research. The algorithmic advances allow harnessing the explosion of images available online, and enable real-world applications such as automated driving, geolocation, and automatic 3D reconstruction of whole cities. Finally, the project exposes undergraduates at a liberal-arts college in rural Vermont to the world of research, experimentation, and discovery.

该项目旨在推动立体视觉和光流算法在具有挑战性的真实世界条件下工作。它贡献了新的算法方法，新的具有地面真实的高分辨率数据集，以及对Middlebury基准的更新。算法的进步包括采用辐射和几何自校准的快速匹配算法，包括在匹配过程中建立噪声和颜色模型的智能数据项，以及具有关于半遮挡区域、反射和透明度的显式推理的新颖的基于层的表面重建算法。新的数据集反映了当前的挑战，包括具有复杂遮挡、镜面和反射的真实世界场景的高分辨率图像，这些场景在不同的照明下使用不同的相机拍摄。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。新的数据集和基准对社区构成了挑战，并成为新研究的催化剂。算法的进步允许利用在线可用图像的爆炸性增长，并支持现实世界的应用，如自动驾驶、地理定位和整个城市的自动3D重建。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。