Real-time Camera Pose Estimation using Correspondences with High Outlier Ratios - Solving the Perspective n-Point Problem using Prior Probability

Tobias Nöll; Alain Pagani; Didier Stricker

In: Paul Richard; José Braz (Hrsg.). VISAPP 2010 - Proceedings of the Fifth International Conference on Computer Vision Theory and Applications, Angers, France, May 17-21, 2010 - Volume 1. International Conference on Computer Vision Theory and Applications (VISAPP-2010), located at VISIGRAPP 2010, May 17-21, Angers, France, Pages 381-386, ISBN 978-989-674-028-3, INSTICC Press, 2010.


We present PPnP, an algorithm capable of estimating a robust camera pose in real-time, even if being provided with large sets of correspondences containing high ratios of outliers. For these situations, standard pose estimation algorithms using RANSAC are often unable to provide a solution or at least not in the required time frame. PPnP is provided with a probability distribution function which describes all valid possible camera pose estimates. By checking the correspondences for being compatible with the prior probability, it can be decided effectively at a very early stage, which correspondences can be treated as outliers. This allows a considerably more effective selection of hypothetical inliers than in RANSAC. Although PPnP is based on a technique called BlindPnP which is not intended for real-time computing, a number of changes in PPnP allows to estimate a camera pose with the same high quality as BlindPnP while being considerably faster.

Noell2010_VISAPP.pdf (pdf, 3 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence