Worldwide Pose Estimation using 3D Point Clouds

Yunpeng Li Noah Snavely Dan Huttenlocher Pascal Fua


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.

Paper - ECCV 2012

Paper (PDF, 2.0MB)
Supplemental material (PDF, 5.1MB)
Poster (PDF, 10.1MB)
Video demo (AVI, 2.1MB)


Coming soon.

See also

Location Recognition using Prioritized Feature Matching


This work was supported in part by the NSF (grants IIS-0713185 and IIS-1111534), Intel Corporation,, Inc., MIT Lincoln Laboratory, and the Swiss National Science Foundation. We also thank Flickr users for use of their photos.