RI: Medium: Collaborative Research: Reconstructing Cities from Photographs

Cornell University, Ithaca NY

Investigators

Abstract

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.