PhotoCity: training experts at large-scale image acquisition through a competitive game

  • Authors:

  • Kathleen Tuite;Noah Snavely;Dun-yu Hsiao;Nadine Tabing;Zoran Popovic

  • Affiliations:

  • University of Washington, Seattle, Washington, USA;Cornell University, Ithaca, New York, USA;University of Washington, Seattle, Washington, USA;University of Washington, Seattle, Washington, USA;University of Washington, Seattle, Washington, USA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2011

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Abstract

Large-scale, ground-level urban imagery has recently developed as an important element of online mapping tools such as Google's Street View. Such imagery is extremely valuable in a number of potential applications, ranging from augmented reality to 3D modeling, and from urban planning to monitoring city infrastructure. While such imagery is already available from many sources, including Street View and tourist photos on photo-sharing sites, these collections have drawbacks related to high cost, incompleteness, and accuracy. A potential solution is to leverage the community of photographers around the world to collaboratively acquire large-scale image collections. This work explores this approach through PhotoCity, an online game that trains its players to become "experts" at taking photos at targeted locations and in great density, for the purposes of creating 3D building models. To evaluate our approach, we ran a competition between two universities that resulted in the submission of over 100,000 photos, many of which were highly relevant for the 3D modeling task at hand. Although the number of players was small, we found that this was compensated for by incentives that drove players to become experts at photo collection, often capturing thousands of useful photos each.