Scene Chronology

  • Kevin Matzen
  • Noah Snavely
Conference paper

DOI: 10.1007/978-3-319-10584-0_40

Part of the Lecture Notes in Computer Science book series (LNCS, volume 8695)
Cite this paper as:
Matzen K., Snavely N. (2014) Scene Chronology. In: Fleet D., Pajdla T., Schiele B., Tuytelaars T. (eds) Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol 8695. Springer, Cham

Abstract

We present a new method for taking an urban scene reconstructed from a large Internet photo collection and reasoning about its change in appearance 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 estimates of when each existed. Our method is robust and scalable to scenes with hundreds of thousands of images and billions of noisy, individual point observations. 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.

Keywords

Structure from motion temporal reasoning 4D modeling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

978-3-319-10584-0_40_MOESM1_ESM.pdf (62 kb)
Electronic Supplementary Material (PDF 63 KB)

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Kevin Matzen
    • 1
  • Noah Snavely
    • 1
  1. 1.Cornell UniversityIthacaUSA

Personalised recommendations