|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 (PDF, 2.0MB)|
|Supplemental material (PDF, 5.1MB)|
|Poster (PDF, 10.1MB)|
|Video demo (AVI, 2.1MB)|
Landmarks10k (~5.x GB tar.gz), README file
Bundler-style Structure from Motion models computed for images from 10,000 major world landmarks. The models are in a georeferenced coordinate system. We are unable to include the original images, although the image sources are documented.
Visualizations of these datasets can be found at the Landmarks10K page.
|Location Recognition using Prioritized Feature Matching|
This work was supported in part by the NSF (grants IIS-0713185 and IIS-1111534), Intel Corporation, Amazon.com, Inc., MIT Lincoln Laboratory, and the Swiss National Science Foundation. We also thank Flickr users for use of their photos.