bors in the network. Given a raw set of geotagged photos, however, we do not know which are accurate.

To overcome this problem we have developed a new technique that uses the image network to combine these position estimates in a more intelligent, robust way, “averaging out” errors in the noisy observations by passing geometric information between nodes in the image network. This algorithm uses a message-passing strategy based on an technique known as loopy belief propagation commonly used in machine learning, computer vision, and other areas. 18 This algorithm is scalable and can find good solutions to very nonlinear problems. While complex, our algorithm starts with the simple idea that each image should repeatedly average its location with that of its neighbors, hence using the graph to smooth noisy location estimates. Because of the extreme noise, this simple averaging approach doesn’t work well; therefore, we developed a more sophisticated approach. 9

The message-passing process described here repeats for a number of rounds, so each image repeatedly updates its position based on information from its neighbors. This algorithm results in fairly accurate camera positions, and applying standard optimization techniques (such as gradient descent) using these positions as a starting point can yield further improvements. With this algorithm we have built some very large 3D models, including the reconstructions of the city of Dubrovnik and parts of Rome shown in Figure 9. To process these large problems, we implemented the algorithm using the MapReduce framework and ran these as jobs on a large Hadoop cluster. (For more information, see our project’s Web page10). In other work on the 3D modeling problem, we reconstructed all of the major sites in Rome from hundreds of thousands of Flickr photos in less than 24 hours (thus reconstructing “Rome in a Day”). 3, 19

While photo-sharing sites such as Flickr and Facebook continue to grow at a breathtaking pace, they still do not have enough images to reach our eventual goal of reconstructing the entire world in 3D. The main problem is that the geospatial distribution of

figure 10. PhotoCity.

 

photographs is highly nonuniform, as noted in the previous section—there are hundreds of thousands of photos of Notre Dame but virtually none of the café across the street.

One solution to this problem is to entice people to take photos of underrepresented places through gamifica-tion. This is the idea behind PhotoCity, an online game developed in collaboration with the University of Washington. In PhotoCity, teams of players compete against one another by taking photos at specific points in space to capture flags and buildings. 22 Through this game, we collected more than 100,000 photos of the Cornell and University of Washington campuses over a period of a few weeks. We used these photos to reconstruct large portions of the two campuses, including areas that otherwise did not have much photographic coverage on sites such as Flickr. A few example building models created from these photos, along with a screenshot of the PhotoCity interface, are shown in Figure 10. On the left is a screenshot of the PhotoCity interface showing an overhead map depicting the state of the game. On the right are a few 3D models created from photos uploaded by players.

Creating a successful game involved two key challenges: building a robust online system for users to upload photos for processing; and designing the game mechanics in such a way that users were excited about playing. To address the first challenge, we built a version of our 3D reconstruction algorithm that could take a new photo of a building and quickly integrate it into our current 3D model of that building, updating that model with any new information contributed by that photo.

For the second challenge of design-
ing effective game mechanics, we de-
veloped a mix of incentives. One set
of incentives involved competition at
different levels (for example, between
students at the same school, as well
as a race for each school to build the
best model). Another set involved giv-
ing each player visual feedback about
how much he or she contributed to the
model, by showing 3D points created
by that player’s photos and by updat-
ing models so that players could see
the progress of the game as a whole
over time. A survey of players after
the conclusion of the competition re-
vealed that different players were mo-
tivated by different incentives; some
were driven by competition, while oth-
ers simply enjoyed seeing the virtual
world grow over time.

future Work

This article has presented some of our initial work into unlocking the information latent in large photo-sharing Web sites using network-analysis algorithms, but the true promise of this type of analysis is yet to be realized. The opportunities for future work in this area lie along two different lines. First, new algorithms are needed to extract visual content more efficiently and accurately: the algorithms presented here produce incorrect results on some specific types of scenes, for example, and they are relatively compute-intensive, requiring many hours on large clusters of computers to process just a few thousand images.

Second, this type of analysis could be applied to other disciplines. Many scientists are interested in studying the world and how it has changed over time, including archaeologists, architects, art historians, ecologists, urban planners, and so on. As a specific example, the 3D reconstruction