RI: Small: Modeling and Recognition of Landmarks and Urban Environments

RI：小型：地标和城市环境的建模和识别

• 批准号: 0916829
• 负责人: Jan-Michael Frahm
• 金额: $ 44.92万
• 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: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0916829&HistoricalAwards=false
• 关键词: RI; Small; Modeling; Recognition; Landmarks

The goal of this project is to design a scalable and robust system for modeling and representing the spatiotemporal and semantic structure of large collections of partially geo-referenced imagery. Specifically, the project is aimed at Internet photo collections of images of famous landmarks and cities. The functionalities of the system include 3D reconstruction, browsing, summarization, location recognition, and scene segmentation. In addition, the system incorporates human-created annotations such as text and geo-tags, models scene illumination conditions, and supports incremental model updating using an incoming stream of images. This system is designed to take advantage of the redundancy inherent in community photo collections to achieve levels of robustness and scalability not attainable by existing geometric modeling approaches. The key technical innovation of the project is a novel data structure, the iconic scene graph that efficiently and compactly captures the perceptual, geometric, and semantic relationships between images in the collection.The key methodological insight of this project is that successful representation and recognition of landmarks requires the integration of statistical recognition and geometric reconstruction approaches. The project incorporates statistical inference into all components of the landmark modeling system, and includes a significant layer of high-level semantic functionality that is implemented using recognition techniques.Potential applications with societal impact include virtual tourism and navigation, security and surveillance, cultural heritage preservation, immersive environments and computer games, and movie special effects. Datasets and code produced in the course of the project will be made publicly available. The project includes a significant education component through undergraduate and graduate course development.

该项目的目标是设计一个可扩展和稳健的系统，用于对部分地理参考图像的大量集合的时空和语义结构进行建模和表示。具体地说，该项目旨在收集著名地标和城市的图像的互联网照片。该系统的功能包括三维重建、浏览、摘要、位置识别和场景分割。此外，该系统结合了文本和地理标签等人工创建的注释，对场景照明条件进行建模，并支持使用传入的图像流进行增量模型更新。该系统旨在利用社区照片集合中固有的冗余来实现现有几何建模方法无法达到的健壮性和可伸缩性。该项目的关键技术创新是一种新颖的数据结构--标志性场景图，它高效而紧凑地捕捉了集合中图像之间的感知、几何和语义关系。该项目的关键方法论见解是，地标的成功表示和识别需要统计识别和几何重建方法的结合。该项目将统计推理融入到地标建模系统的所有组件中，并包括使用识别技术实现的重要高级语义功能。具有社会影响的潜在应用包括虚拟旅游和导航、安全和监视、文化遗产保护、沉浸式环境和计算机游戏，以及电影特效。项目过程中产生的数据集和代码将公之于众。该项目包括通过本科生和研究生课程开发的重要教育部分。