RI: Medium: Collaborative Research: Reconstructing Cities from Photographs

这个项目的重点是研究如何在城市尺度上生成极其详细和精确的3D几何和外观（BRDF）模型，这些模型来自互联网上各种来源的照片集合，其中包含数百万张巨大多样性的照片，例如观看范围和条件，一天中的时间和天气条件，等等。城市场景的特性可能包括低纹理表面、反射和透明材料，以及挑战现有重建算法的重复结构。研究人员将以重建几个美国和国外的大城市为目标来解决这些挑战。历史照片和虚拟模型也可以纳入。有许多与该项目相关的研究课题。为了在城市尺度上配准照片和恢复稀疏几何，将设计一种新的、统一的、基于运动的结构（SfM）算法，以利用大型并行计算平台。多视立体（MVS）算法利用SfM恢复的配准照片和稀疏的三维场景点，可以重建详细的几何模型。然后，新的MVS算法将利用建筑场景的结构和体积重建方法来产生具有卓越准确性和可用性的注释模型。数字模型在社会、文化和经济领域发挥着越来越重要的作用，是下一代地图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。

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.