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International Journal of Computer Vision

, Volume 109, Issue 3, pp 252–286 | Cite as

Infinitesimal Plane-Based Pose Estimation

  • Toby CollinsEmail author
  • Adrien Bartoli
Article

Abstract

Estimating the pose of a plane given a set of point correspondences is a core problem in computer vision with many applications including Augmented Reality (AR), camera calibration and 3D scene reconstruction and interpretation. Despite much progress over recent years there is still the need for a more efficient and more accurate solution, particularly in mobile applications where the run-time budget is critical. We present a new analytic solution to the problem which is far faster than current methods based on solving Pose from \(n\) Points (PnP) and is in most cases more accurate. Our approach involves a new way to exploit redundancy in the homography coefficients. This uses the fact that when the homography is noisy it will estimate the true transform between the model plane and the image better at some regions on the plane than at others. Our method is based on locating a point where the transform is best estimated, and using only the local transformation at that point to constrain pose. This involves solving pose with a local non-redundant 1st-order PDE. We call this framework Infinitesimal Plane-based Pose Estimation (IPPE), because one can think of it as solving pose using the transform about an infinitesimally small region on the surface. We show experimentally that IPPE leads to very accurate pose estimates. Because IPPE is analytic it is both extremely fast and allows us to fully characterise the method in terms of degeneracies, number of returned solutions, and the geometric relationship of these solutions. This characterisation is not possible with state-of-the-art PnP methods.

Keywords

Plane Pose SfM PnP Homography 

Notes

Acknowledgments

This research has received funding from the EU FP7 ERC research Grant 307483 FLEXABLE. Code is available at http://www.tobycollins.net/research/IPPE.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.ALCoV-ISIT, UMR 6284 CNRS/Université d’AuvergneClermont-FerrandFrance

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