TY - GEN

T1 - A Direct Least-Squares Solution to the PnP Problem with Unknown Focal Length

AU - Zheng, Yinqiang

AU - Kneip, Laurent

N1 - Publisher Copyright:
© 2016 IEEE.

PY - 2016/12/9

Y1 - 2016/12/9

N2 - In this work, we propose a direct least-squares solution to the perspective-n-point (PnP) pose estimation problem of a partially uncalibrated camera, whose intrinsic parameters except the focal length are known. The basic idea is to construct a proper objective function with respect to the target variables and extract all its stationary points so as to find the global minimum. The advantages of our proposed solution over existing ones are that (i) the objective function is directly built upon the imaging equation, such that all the 3D-to-2D correspondences contribute equally to the minimized error, and that (ii) the proposed solution is noniterative, in the sense that the stationary points are retrieved by means of eigenvalue factorization and the common iterative refinement step is not needed. In addition, the proposed solution has O(n) complexity, and can be used to handle both planar and nonplanar 3D points. Experimental results show that the proposed solution is much more accurate than the existing state-of-the-art solutions, and is even comparable to the maximum likelihood estimation by minimizing the reprojection error.

AB - In this work, we propose a direct least-squares solution to the perspective-n-point (PnP) pose estimation problem of a partially uncalibrated camera, whose intrinsic parameters except the focal length are known. The basic idea is to construct a proper objective function with respect to the target variables and extract all its stationary points so as to find the global minimum. The advantages of our proposed solution over existing ones are that (i) the objective function is directly built upon the imaging equation, such that all the 3D-to-2D correspondences contribute equally to the minimized error, and that (ii) the proposed solution is noniterative, in the sense that the stationary points are retrieved by means of eigenvalue factorization and the common iterative refinement step is not needed. In addition, the proposed solution has O(n) complexity, and can be used to handle both planar and nonplanar 3D points. Experimental results show that the proposed solution is much more accurate than the existing state-of-the-art solutions, and is even comparable to the maximum likelihood estimation by minimizing the reprojection error.

UR - http://www.scopus.com/inward/record.url?scp=84986309171&partnerID=8YFLogxK

U2 - 10.1109/CVPR.2016.198

DO - 10.1109/CVPR.2016.198

M3 - Conference contribution

T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

SP - 1790

EP - 1798

BT - Proceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

PB - IEEE Computer Society

T2 - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016

Y2 - 26 June 2016 through 1 July 2016

ER -