Worldwide Pose Estimation Using 3D Point Clouds
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 georegistered 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.
Large Scale Visual Geo Localization