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

该项目通过利用几何一致性作为中级视觉相似性线索来开发地理定位图像数据集的视觉索引，并使用获得的数据关联作为推断数据集元素之间语义关系的手段，从而推进了目前的技术水平。根据场景中观察到的几何和语义元素对图像内容进行表征，为识别和管理大规模照片集中的数据关联提供了一个通用框架。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，确定图像拍摄的地理位置——相应地，平衡搜索完整性和计算可追溯性的挑战被带到了研究工作的前沿；(2)结合从大型照相集或地面侦察视频/照片中获得的几何结构估计，作为在重建的3d环境中识别和识别语义上有意义元素的手段。该项目利用几何一致性作为视觉数据关联原语，以便在开发互联网规模的照片收集分析系统中引入结构和语义索引的概念。此外，通过将几何结构和语义上下文的互补数据抽象层次相结合，研究小组开发了更高效、更健壮的数据组织框架，其适用性远远超出了所研究的城市地理定位测试应用。

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.