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Worldwide Pose Estimation Using 3D Point Clouds*
Yunpeng Li1, Noah Snavely2, Dan Huttenlocher2, and Pascal Fua1
1EPFL, Switzerland
yunpeng.li@epfl.ch
pascal.fua@epfl.ch
2Cornell University, USA
snavely@cs.cornell.edu
dph@cs.cornell.edu
Abstract. We address the problem of determining where a photo was taken by estimating a full 6-DOF-plus-intrincs camera pose with respect to a large geo-registered 3D point cloud, bringing together research on image localization, landmark recognition, and 3D pose estimation. Our method scales to datasets with hundreds of thousands of images and tens of millions of 3D points through the use of two new techniques: a co-occurrence prior for RANSAC and bidirectional matching of image features with 3D points. We evaluate our method on several large data sets, and show state-of-the-art results on landmark recognition as well as the ability to locate cameras to within meters, requiring only seconds per query.
*This work was supported in part by NSF grants IIS-0713185 and IIS-1111534, Intel Corporation, Amazon.com, Inc., MIT Lincoln Laboratory, and the Swiss National Science Foundation.
LNCS 7572, p. 15 ff.
lncs@springer.com
© Springer-Verlag Berlin Heidelberg 2012