Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation

We propose a novel approach for the estimation of the pose and focal length of a camera from a set of 3D-to-2D point correspondences. Our method compares favorably to competing approaches in that it is both more accurate than existing closed form solutions, as well as faster and also more accurate t...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on pattern analysis and machine intelligence 2013-10, Vol.35 (10), p.2387-2400
Main Authors: Penate-Sanchez, A., Andrade-Cetto, J., Moreno-Noguer, F.
Format: Article
Language:English
Subjects:
Algorithms
Applied sciences
Artificial Intelligence
Camera calibration
Cameras
Classificació INSPEC
Computer science
control theory
systems
Computer Simulation
Computer vision
computer vision paraules autor: camera calibration
Equations
Estimation
Exact sciences and technology
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Imaging, Three-Dimensional - methods
Informàtica
Kernel
Linear Models
Linear systems
Noise
Pattern recognition
Pattern Recognition, Automated - methods
Pattern recognition. Digital image processing. Computational geometry
perspective-n-point problem
Reproducibility of Results
Robòtica
Sensitivity and Specificity
Vectors
Àrees temàtiques de la UPC
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We propose a novel approach for the estimation of the pose and focal length of a camera from a set of 3D-to-2D point correspondences. Our method compares favorably to competing approaches in that it is both more accurate than existing closed form solutions, as well as faster and also more accurate than iterative ones. Our approach is inspired on the EPnP algorithm, a recent O(n) solution for the calibrated case. Yet we show that considering the focal length as an additional unknown renders the linearization and relinearization techniques of the original approach no longer valid, especially with large amounts of noise. We present new methodologies to circumvent this limitation termed exhaustive linearization and exhaustive relinearization which perform a systematic exploration of the solution space in closed form. The method is evaluated on both real and synthetic data, and our results show that besides producing precise focal length estimation, the retrieved camera pose is almost as accurate as the one computed using the EPnP, which assumes a calibrated camera.
ISSN:0162-8828
1939-3539
2160-9292
DOI:10.1109/TPAMI.2013.36