RI: Small: A Region-Based Approach to Reconstructing Urban Scenes

RI：小：基于区域的重建城市场景的方法

• 批准号: 1116012
• 负责人: Minh Do
• 金额: $ 45万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1116012&HistoricalAwards=false
• 关键词: RI; Small; Region; Based; Approach

Recently, reconstructing full and detailed 3D models for large-scale urban environments has become the crucial technology for many applications and offers a natural platform for different services. Although conventional structure from motion (SFM) techniques have been engineered to their maturity, they normally do not utilize rich global structures of urban scenes, including regularity, symmetry, and self-symmetry; and the very repetitive shapes and textures ubiquitous in urban scenes make detecting and matching features extremely challenging for the conventional techniques. This project develops a novel approach for inferring highly accurate 3D geometry from an individual or multiple 2D images of an urban scene. It takes full advantage of the rich global symmetry and regularity in urban scenes by leveraging powerful computational tools from modern high-dimensional convex optimization. The developed method can accurately recover the regular 3D geometry and 2D texture of the scene directly from the raw image pixels/regions without relying on extracting any intermediate local features. The research includes developing a set of useful tools and a full system that can significantly improve the efficiency and scalability of 3D modeling of large urban scenes and give significantly more compact representation of the 3D geometry and 2D appearance, enabling online real-time rendering and visualization.Research results from this project can be easily integrated into and significantly improve the current computer vision course on 3D reconstruction. The associated technologies can be useful for a very wide range of commercial applications such as online or mobile visual search, visual guidance, navigation, or surveillance, virtual tourism, and augmented reality etc.

近年来，重建大规模城市环境的完整和详细的三维模型已成为许多应用的关键技术，并为不同的服务提供了一个自然的平台。虽然传统的运动恢复结构（SFM）技术已经被设计到它们的成熟度，但是它们通常不利用城市场景的丰富的全局结构，包括规则性、对称性和自对称性;并且城市场景中普遍存在的非常重复的形状和纹理使得检测和匹配特征对于传统技术来说极具挑战性。该项目开发了一种新的方法，用于从城市场景的单个或多个2D图像中推断高度精确的3D几何形状。它充分利用城市场景中丰富的全局对称性和规律性，利用现代高维凸优化的强大计算工具。所开发的方法可以准确地恢复规则的3D几何形状和2D纹理的场景直接从原始图像的像素/区域，而不依赖于提取任何中间的局部特征。该研究包括开发一套有用的工具和一个完整的系统，可以显着提高大型城市场景的3D建模的效率和可扩展性，并提供更紧凑的3D几何和2D外观表示，使在线真实的-时间渲染和可视化。该项目的研究成果可以很容易地集成到3D计算机视觉课程中，并显着改善当前的3D计算机视觉课程重建相关联的技术可以用于非常广泛的商业应用，诸如在线或移动的视觉搜索、视觉引导、导航或监视、虚拟旅游和增强现实等。