RI: Small: Crowd-Sourcing the World: Scalable Methods for Dynamic Structure from Motion

RI：小型：众包世界：运动动态结构的可扩展方法

• 批准号: 1423676
• 负责人: Matthew Turk
• 金额: $ 47.74万
• 依托单位: University of California-Santa Barbara
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-15 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1423676&HistoricalAwards=false
• 关键词: RI; Small; Crowd; Sourcing; World

This project enables global-scale dynamic reconstructions that can scale to eventually encompass all of the world's 3D data, to which any user may contribute new visual data, thereby ensuring a more complete, up-to-date model and a better experience for users of applications that rely on such data. The abundance of publicly available imagery from a variety of sources (consumer, industry, and government) and the proliferation of networked mobile devices equipped with cameras provide an opportunity to build large-scale 3D models that cover the entire world. Such global 3D models are invaluable to a wide range of applications that require real-time access to environment structure, such as providing assistance to the visually impaired, exploring dangerous areas for search-and rescue operations, urban planning, self-driving cars, and virtual tourism. The key research question driving this work is how to efficiently and accurately update a global-scale 3D reconstruction in order to support a dynamic global model. The research uses structure from motion (SfM) techniques to acquire global context for 3D modeling and then propagate local 3D reconstructions and other visual data back to the global model via a new technique called "globalization." The project addresses a major gap in large-scale SfM: rapidly extending and updating reconstructions to promote real-time use. The project is built on preliminary work in large-scale image matching, localization, and 3D reconstruction. The research team carries out extensive data collection and experimentation to benchmark the performance of the developed techniques and to assess the progress of the project and the utility of the methods. The project is developing a prototype mobile system for contributing imagery and browsing and updating models for public use once it is available.

该项目实现了全球范围内的动态重建，最终可以扩展到涵盖世界上所有的 3D 数据，任何用户都可以向其中贡献新的视觉数据，从而确保为用户提供更完整、最新的模型和更好的体验。依赖此类数据的应用程序。来自各种来源（消费者、行业和政府）的大量公开图像以及配备摄像头的联网移动设备的激增，为构建覆盖整个世界的大型 3D 模型提供了机会。这种全局 3D 模型对于需要实时访问环境结构的各种应用来说非常宝贵，例如为视障人士提供帮助、探索危险区域进行搜救行动、城市规划、自动驾驶汽车和虚拟旅游。推动这项工作的关键研究问题是如何高效、准确地更新全局尺度 3D 重建以支持动态全局模型。该研究使用运动结构 (SfM) 技术来获取 3D 建模的全局上下文，然后通过称为“全球化”的新技术将局部 3D 重建和其他视觉数据传播回全局模型。该项目解决了大规模 SfM 的一个主要差距：快速扩展和更新重建以促进实时使用。该项目建立在大规模图像匹配、定位和 3D 重建方面的前期工作的基础上。研究团队进行了广泛的数据收集和实验，以对所开发技术的性能进行基准测试，并评估项目的进展和方法的实用性。该项目正在开发一个原型移动系统，用于贡献图像并浏览和更新模型，以供公众使用。