Fast Robust Large-scale Mapping from Video and Internet Photo ...

photo collections. Here we discuss the most related work in the areas of fast
structure from motion, camera registration from Internet photo collections,
real-time dense stereo, scene summarization, and landmark recognition.
Besides the purely image based approaches there is also work on modeling
the geometry by combining cameras and active range scanners. Früh and
Zakhor [4] proposed a mobile system mounted on a vehicle to capture large
amounts of data while driving in urban environments. Earlier systems by
Stamos and Allen [5] and El-Hakim et al. [6] constrained the scanning laser
to be in a few pre-determined viewpoints. In contrast to the comparably expensive
active systems, our approach for real-time reconstruction from video
uses only cameras leveraging the methodology developed by the computer
vision community within the last two decades [7, 8].
The first step in our systems is to establish correspondences between the
video frames or the different images of the photo collection respectively. We
use two different approaches for establishing correspondences. For video data
we use an extended KLT tracker [9] that exploits the inherent parallelism
of the tracking problem to improve the computational performance through
execution on the graphics processor (GPU). The specific approach used is
introduced by Zach et al. in detail in [10].
In the case of Internet photo collections we have to address the challenge
of dataset collection, which is the following problem: starting with the
heavily contaminated output of an Internet image search query, extract a
high-precision subset of images that are actually relevant to the query. Existing
approaches to this problem [11, 12, 13, 14, 15] consider general visual
categories not necessarily related by rigid 3D structure. These techniques
4