RI: Medium: Collaborative Research: Reconstructing Cities from Photographs

该项目专注于从互联网上收集的各种来源的照片中生成城市规模的极其详细和精确的3D几何和外观(BRDF)模型的相关研究问题，其中包含数百万张具有巨大多样性的照片，例如观看范围和条件、一天中的时间和天气条件，仅举几例。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。调查人员将应对这些挑战，目标是重建几个美国和外国的大城市。历史照片和虚拟模特也可能被纳入其中。与该项目相关的研究课题有很多。为了在城市尺度上配准照片和恢复稀疏几何图形，将设计一种新的、统一的运动结构(SFM)算法，以利用大型并行计算平台。多视点立体(MVS)算法利用SfM恢复的配准照片和稀疏的3D景物点，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，并将使用体积重建方法来产生具有非凡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。所有模型和数据集都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.