According to MSR

We present a system that can match and reconstruct 3D scenes from extremely large collections of photographs such as those found by searching for a given city (e.g., Rome) on Internet photo sharing sites. Our system uses a collection of novel parallel distributed matching and reconstruction algorithms, designed to maximize parallelism at each stage in the pipeline and minimize serialization bottlenecks. It is designed to scale gracefully with both the size of the problem and the amount of available computation. We have experimented with a variety of alternative algorithms at each stage of the pipeline and report on which ones work best in a parallel computing environment. Our experimental results demonstrate that it is now possible to reconstruct cities consisting of 150K images in less than a day on a cluster with 500 compute cores.

Entering the search term “Rome” on flickr.com returns
more than two million photographs. This collection represents
an increasingly complete photographic record of the
city, capturing every popular site, facade, interior, fountain,
sculpture, painting, cafe, and so forth. Most of these photographs
are captured from hundreds or thousands of viewpoints
and illumination conditions—Trevi Fountain alone
has over 50,000 photographs on Flickr. Exciting progress
has been made on reconstructing individual buildings or
plazas from similar collections [16, 17, 8], showing the potential
of applying structure from motion (SfM) algorithms
on unstructured photo collections of up to a few thousand
photographs. This paper presents the first system capable of
city-scale reconstruction from unstructured photo collections.
We present models that are one to two orders of magnitude
larger than the next largest results reported in the literature.
Furthermore, our system enables the reconstruction of data
sets of 150,000 images in less than a day.

This is really cool. Think about how awesome it would be if everyone geo-tagged there photos this process would be a hundred times easier.

http://research.microsoft.com/apps/pubs/default.aspx?id=101029