EAGER: Leveraging 3D structure estimates for photo collection based geo-localization and semantic indexing

EAGER：利用 3D 结构估计进行基于照片收集的地理定位和语义索引

• 批准号: 1349074
• 负责人: Jan-Michael Frahm
• 金额: $ 28.25万
• 依托单位: University of North Carolina at Chapel Hill
• 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: 2013-10-01 至 2015-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1349074&HistoricalAwards=false
• 关键词: EAGER; Leveraging; 3D; structure; estimates

This project advances the state of the art by utilizing geometric consistency as a mid-level visual similarity cue used to develop a visual index of a geo-located image dataset and use the attained data associations as a means to infer semantic relationship among dataset elements. The characterization of the image content in terms of the geometric and semantic elements observed in scene provides a general framework for both identifying and managing data association in large scale photo collections. The project develops such complementary data abstractions into a single framework by focusing on two main research topics: (1) Determining the geographic location where an image was taken by comparing it against a large database of geo-located urban imagery - accordingly, the challenge of balancing both search completeness and computational tractability is brought to the forefront of research efforts; and(2) Incorporating geometric structure estimates attained from large photo-collections or ground reconnaissance video/photos as a means to identify and recognize semantically meaningful elements within the reconstructed 3D-environment.This project leverages the use geometric consistency as a visual data association primitive in order to introduce the concept of structural and semantic indexing within the development internet scale photo collection analysis systems. Moreover, by combining the complementary data abstraction levels of geometrical structure and semantic context the research team develops more efficient and robust data organization framework with applicability well beyond the studied test application of urban geo-localization.

该项目通过利用几何一致性作为用于开发地理定位图像数据集的视觉索引的中级视觉相似性线索，并使用所获得的数据关联作为推断数据集元素之间的语义关系的手段，来推进现有技术。在场景中观察到的几何和语义元素的图像内容的表征提供了一个通用的框架，用于识别和管理大规模照片集合中的数据关联。该项目通过关注两个主要研究主题将这种互补的数据抽象发展成一个单一的框架：（1）通过将图像与地理定位城市图像的大型数据库进行比较来确定图像拍摄的地理位置-因此，平衡搜索完整性和计算易处理性的挑战被带到了研究工作的最前沿;以及（2）将从大型照片集合或地面侦察视频/照片获得的几何结构估计作为识别和辨识经重构的3D内的语义上有意义的元素的手段。该项目利用几何一致性作为可视化数据关联原语，以引入结构化和语义索引的概念开发互联网范围内的照片采集分析系统。此外，通过结合几何结构和语义上下文的互补数据抽象层次，研究团队开发了更有效和强大的数据组织框架，其适用性远远超出了城市地理定位的测试应用。