Abstract

We present a new method for taking an urban scene reconstructed from a large Internet photo collection and reasoning about its change in appear- ance through time. Our method estimates when individual 3D points in the scene existed, then uses spatial and temporal affinity between points to segment the scene into spatio-temporally consistent clusters. The result of this segmentation is a set of spatio-temporal objects that often correspond to meaningful units, such as billboards, signs, street art, and other dynamic scene elements, along with es- timates of when each existed. Our method is robust and scalable to scenes with hundreds of thousands of images and billions of noisy, individual point observa- tions. We demonstrate our system on several large-scale scenes, and demonstrate an application to time stamping photos. Our work can serve to chronicle a scene over time, documenting its history and discovering dynamic elements in a way that can be easily explored and visualized.