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)

@InProceedings{   li:eccv:2012,
  author        = {Yunpeng Li and Noah Snavely and Daniel Huttenlocher and
                  Pascal Fua},
  title         = {Worldwide Pose Estimation using 3{D} Point Clouds},
  booktitle     = European Conf. on Computer Vision,
  year          = 2012


We provide two datasets from the paper below, Landmark10k and San Francisco. If you use either dataset, please cite our ECCV 2012 paper above.


Data file (~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.

San Francisco

Structure from Motion model of much of downtown San Francisco created from the 1.7-million-image San Francisco Landmark Dataset of Chen et al. There are two versions of this dataset, as described in the paper: SF-0 is created using original images and standard SIFT, whereas SF-1 is created using histogram-equalized images and upright SIFT (yielding a larger number of reconstructed images). This release consists of several files (thanks to Torsten Sattler for archiving this data!):
  • list_sf0.txt, list_sf1.txt: Text files containing the names of the database images used to reconstruct the SF-0 and SF-1 models.
  • sf_0_full.out.gz, sf_1_full.out.gz: Bundler reconstructions of SF-0 and SF-1.
  • list_query_sf_with_intrinsics.txt: A list of query image names with focal length and center of image for each query.
  • The query images of the SF dataset together with their SIFT descriptors (regular SIFT, i.e., the SIFT descriptor used for the SF-0 model).
  • A binary file created by my software that contains the 3D points positions, all SIFT descriptors for each 3D point, all visibility information for each point, as well as the camera poses for the SF-0 model. (Thanks to Torsten Sattler for providing this file!)
  • A small C++ snippet containing code to load the .info file. (Thanks to Torsten Sattler for providing this file!)
In addition to the files above, Sattler, Torii, Sivic, et al. have created reference poses for some of the query images in the San Francisco dataset as part of their paper:

Sattler, Torii, Sivic, Pollefeys, Taira, Okutomi, Pajdla
Are Large-Scale 3D Models Really Necessary for Accurate Visual Localization?
CVPR 2017

Please see their project page for more information.

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.