RI: Medium: Collaborative Research: Reconstructing Cities from Photographs

RI：媒介：合作研究：从照片重建城市

• 批准号: 0964027
• 负责人: Noah Snavely
• 金额: $ 24万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-15 至 2014-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0964027&HistoricalAwards=false
• 关键词: RI; Medium; Collaborative; Research; Reconstructing

This project is focused on research issues associated with producing extremely detailed and accurate 3D geometry and appearance (BRDF) models at city scale from internet collections of photos from various sources containing millions of photos of enormous diversity such as viewing range and conditions, time of day and weather conditions, to name a few. The properties of urban scenes may include low-texture surfaces, reflective and transparent materials, and repeated structures that challenge existing reconstruction algorithms. The investigators will address these challenges with the aim of reconstructing several large US and foreign cities. Historical photos and virtual models may also be incorporated. There are a number of research topics associated with the project. To register photographs and recover sparse geometry at city-scale, a new, unified, structure-from-motion (SfM) algorithm will be designed to take advantage of large, parallel computing platforms. With registered photographs and sparse 3D scene points recovered by SfM, multi-view stereo (MVS) algorithms can reconstruct detailed geometric models. Novel MVS algorithms will then exploit the structure of architectural scenes and volumetric reconstruction methods will be employed to produce annotated models of exceptional accuracy and usability. Digital models are playing an increasingly important role in social, cultural and economic endeavor and are central to next-generation mapping and visualization applications. All models and datasets will be made freely available to researchers and the general public.

该项目的重点是与产生非常详细和准确的3D几何和外观（BRDF）模型相关的研究问题，该模型是从来自各种来源的照片收集的互联网收集，其中包含数百万个巨大多样性的照片，例如观看范围和条件，例如一天中的时间和天气状况，仅举几例。 城市场景的特性可能包括低文字表面，反光和透明的材料以及挑战现有重建算法的重复结构。调查人员将解决这些挑战，目的是重建美国和外国的几个城市。 还可以合并历史照片和虚拟模型。 该项目有许多研究主题。为了注册照片并在城市规模恢复稀疏的几何形状，将设计一种新的，统一的结构 - 运动算法（SFM）算法，以利用大型，并行的计算平台。 借助SFM恢复的注册照片和稀疏的3D场景点，多视图立体声（MVS）算法可以重建详细的几何模型。 然后，新型MVS算法将利用建筑场景的结构和体积重建方法的结构，以生成具有出色准确性和可用性的注释模型。数字模型在社会，文化和经济努力中起着越来越重要的作用，并且是下一代映射和可视化应用的核心。 所有模型和数据集将免费提供给研究人员和公众。